RESOURCES, INSIGHTS AND THOUGHT LEADERSHIP

What Claims Teams Don’t Know Is Costing Them

Mon, 27 Apr 2026 15:29:31 GMT

February 18, 2026

By David Wilson

How hidden comorbidities and delayed insights drive workers’ comp claim costs and why early detection changes everything

The Hidden Cost Inside a “Routine” Worker’s Comp Claim

A claim comes in: lower back strain from a warehouse fall.
The adjuster follows a standard process, but critical details are missing:

Pre-existing conditions: Hypertension, Type 2 diabetes
Prior treatment history: Multiple orthopedic visits for a past back issue
Medication conflicts: Regimens that complicate recovery and pain management

Result:

Recovery stalls after 3 months
Worker becomes frustrated and uncertain
Attorney gets involved
Claim cost increases ~4x

Key takeaway: The most expensive risks are often invisible at intake.

The Two Biggest Drivers of Claim Severity

Two factors disproportionately increase costs:

Comorbidities

NCCI data shows
Claims cost ~2x more than those without them
58% of complex claims include at least one

Attorney involvement – often triggered by delayed or inadequate care

A 2024 study by WCRI found attorney involvement:
Increases lost time days by 284%
Inflates expense payments by 200%

Critical insight:

These risks are connected.

Faster, appropriate treatment → less attorney involvement
Early clinical clarity → better outcomes + lower costs

The Real Problem: Timing, Not Awareness

Claims teams already know these risks exist. The issue is:

Key medical details don’t appear at first notice of loss (FNOL)
Claimants may not disclose full history
Data surfaces only after the claim is deteriorating

By then, intervention is reactive, not preventative.

Closing the Gap with Early Insight

Gradient AI’s ClaimVoyant ^TM solves this at day one:

Introduces injured worker health data from outside the claim file:
Pre-existing conditions
Treatment history
Medication profiles
Applies a predictive algorithm to generate:
Complexity identification directly in adjuster workflow

What this means:

Immediate identification of claims at risk of delayed recovery
Proactive intervention before issues escalate

Deeper Clinical Context = Smarter Decisions

Claims teams gain access to detailed reports from Gradient AI:

Adjuster Workbook
NCM (Nurse Case Manager) Workbook
Medical Canvassing Profile

Benefits:

Better coordination between adjusters and RN case managers
More informed care planning
Faster path to recovery

Goal: Return injured workers to pre-injury functionality or as close as possible.

The Compounding Impact Over Time

Early identification + full clinical context leads to:

Faster claim resolution
Improved worker outcomes
Lower claim costs

And at the portfolio level:

Better reserve accuracy
Fewer high-cost outliers
Stronger predictive models

Result: A measurable improvement in overall claims performance, not just individual cases.

Final Thought

Workers’ compensation claims will always be complex. The real differentiator is how much of that complexity you can see and how early you can act on it.

 David Wilson, Senior Product Manager, Property & Casualty, Gradient AI

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AI in P&C Insurance Has Hit an Inflection Point

Wed, 22 Apr 2026 21:27:31 GMT

February 18, 2026

By Jim Bowers

Executives are shifting from experimentation to execution but scaling AI safely and effectively remains the biggest challenge.

Over the past year, one theme has come up in nearly every conversation we’ve had with carriers, TPAs, brokers, and self-insured organizations: AI is no longer theoretical.

Industry data supports this trajectory:

89% of insurers planned to increase investment in generative AI in 2025, and 92% already have dedicated budgets. ¹

74% of firms identified it as a key area for underwriting and claims functions. ²

Executives aren’t asking what it is or whether it matters—they’re asking how to implement it, how to scale it, and how to do both without introducing risk.

What was experimentation a year ago is now execution. And the gap between pilots and real impact is where most organizations are stuck.

This blog is drawn from those conversations, combined with broader industry research and what we’re seeing across active implementations.

Where Carriers Are on the AI Journey

Executives describe their organizations as being somewhere between pilot mode and early scaling . Few have fully operationalized AI across business units, but nearly all have active initiatives underway.

Exploration: Teams are experimenting with GenAI tools for summarization, document review, and internal knowledge search.
Piloting: Carriers are testing underwriting assistants, claims agents, and customer service automation.
Scaling: Governance frameworks, AI councils, and secure data platforms are being established.
Operationalization: A small group of leaders are embedding AI into workflows and measuring ROI.

AI Adoption is not Equal

We are seeing the pace increase across the market, but there is a difference by size of organization and type of organization.

TPAs are more focused on cost transfer to clients and having enough AI tools in place to use for marketing and client acquisition and retention.
Carriers are in different stages of their journeys based on a few observed factors:
Are they invested in legacy system upgrades to handle new technologies
Have they taken any steps to consolidate and leverage disparate data assets
Large and large-mid-sized carriers seem to be further along in terms of governance and regulatory considerations

- Larger carriers tend to have AI governance committees, policies, and established, but evolving protocols in place
- These carriers typically are more protective of their data assets
- They usually like to build when feasible vs. buy. This is particularly true the larger they get.

Small and small-to-mid-sized carriers are often a few steps behind in their roadmap journey and sophistication but still are now more likely to be “buyers” and users of AI then in the past, when they were not early adopters or fast-followers.

Brokers, PEOs, and Self-Insureds timelines and sophistication seem to match a similar pattern to carriers based on size of the organization.

Where AI Is Delivering Real Value

Claims: The Frontline of Impact

Claims lead the way.

Predictive models improve triage and severity accuracy
GenAI summarizes medical records and legal documents
Automation reduces cycle times and leakage

What matters most: explainability. If adjusters trust it—and regulators understand it—it gets used.

Underwriting: From Data Overload to Decision Support

Underwriters don’t need more data; they need better decisions.

AI extracts key insights from submissions and loss runs
Models improve risk scoring and appetite alignment
Assistants reduce manual work

The shift: AI isn’t replacing underwriters; it’s increasing their leverage.

Operations: The Fastest ROI

Operations teams are seeing immediate gains.

Call summarization and real-time policy insights
Automated workflows (policy changes, certificates)
Faster, more accurate document processing

Result: Better customer experience with lower cost to serve.

What’s Slowing Scale

Despite momentum, four barriers keep coming up:

Regulatory Pressure
Fairness, transparency, and consumer protection are non-negotiable.
Data Security
Carriers are drawing a hard line: no sensitive data in public models.
Governance Gaps
AI councils and controls are forming but still maturing.
Change Management
Adoption, not technology, is the bottleneck.

Build, Buy, or Partner? It’s All Three

Most carriers are landing on a hybrid model:

Build core platforms and governance
Buy specialized capabilities
Partner to accelerate execution

The winning approach: Own the foundation. Leverage the ecosystem.

What Leaders Are Doing Differently

The organizations pulling ahead share three traits:

They build governed AI platforms
Secure, reusable, and scalable across use cases
They focus on “lighthouse” wins
Targeted use cases with measurable ROI
They treat AI as a business capability
Not a side project or experiment

The Next 12–24 Months Will Separate Leaders

The roadmap is becoming clearer:

Now (0–6 months):

Establish AI governance and responsible AI framework.
Build secure enterprise AI platform.
Launch 2–3 lighthouse use cases (claims, underwriting, operations).

Next (6–18 months):

Integrate AI into core systems and workflows.
Formalize AI product ownership and value tracking.
Expand to front-office and agentic workflows.

Then (18+ months):

Manage AI as a product portfolio.
Embed AI into strategic planning and budgeting.
Build continuous improvement and lifecycle management.

AI Implementation Roadmap for P&C Insurers

The Bottom Line

AI is no longer a future initiative. It’s a current competitive requirement.

Carriers aren’t deciding whether to adopt AI. They’re deciding how fast they can scale it without losing control.

The real risk isn’t moving too fast. It’s moving too slow while others figure it out.

1. SAAS , GenAI in Insurance: 3 key takeaways from a global industry survey, October 2024

2. EY , How insurers are embracing customer-facing applications for GenAI, 2025 Survey Summary

Jim Bowers is the Principal Solutions Consultant for the Property & Casualty business at Gradient AI. He is a leader in the Workers’ Compensation insurance industry with more than 25 years of carrier experience across claims and underwriting.

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Managing Workers’ Compensation Claims: Turning Complexity into Actionable Insight

Tue, 14 Apr 2026 19:22:45 GMT

February 18, 2026

The Gradient AI Team

Q&A with Gradient AI’s General Manager of Property & Casualty, Brook Rosenbaum

Workers’ compensation claims are inherently complex, and much of that complexity stems from the reality that each injury and each claimant have unique characteristics that have different treatment needs and can lead to dramatically different outcomes. Effective management of workers’ compensation claims starts with identifying and understanding the relevance of these differences.

While the objective is clear: to deliver appropriate care and return injured workers to work, execution often falls short due to uncertainty, fragmented data, and operational inefficiencies.

For carriers, TPAs, and self-administered employers, the challenge is to efficiently deploy resources to quickly respond to the unique needs of each claimant and doing so at scale. This requires more than data. It requires clarity, integration, and practical decision-making.

In this Q&A with Gradient AI’s General Manager of Property & Casualty, Brook Rosenbaum, we explore how leading organizations are addressing these challenges by applying advanced analytics in ways that are actionable, transparent, and aligned with real-world workflows.

Turning Experience into Practice: Bridging Insight and Execution

Q: Let’s start with the big picture. What are the most prominent challenges in managing workers’ compensation claims from both a cost and outcome perspective?

A: As anyone involved in managing workers’ compensation claims can tell you, early intervention is critical to achieving the best outcomes for complex claims. The challenge is in identifying complex claims and the specific drivers of that complexity early enough to enable that early intervention. At its core, a workers’ compensation claim is about delivering the right care at the right time and supporting a safe return to work. All stakeholders (injured workers, employers, carriers, and claims handlers) are aligned to that goal.

Clarity that Drives Trust: Making Complex Risk Understandable

Q: Even with aligned goals, why does execution remain so difficult?

A: Parties are aligned in the goals of quickly treating an injury and resolving a claim. However, each stakeholder also has unique circumstances that shape how claims are managed. Claimants have prior injuries, physical comorbidities, and psychological conditions that can significantly affect their recoveries. Claims managers are tightly managed on loss adjustment expenses. Employers want low premiums. Carriers and self-insured employers hope to limit loss costs and need to reserve appropriately for future expenses.

The overarching challenge is translating the alignment on outcome and the various goals and needs into consistent execution. Even experienced claims professionals are almost always navigating a claim with incomplete or evolving information.

Without the ability to consistently apply insights at the point of decision-making, organizations can struggle to proactively intervene in claims that are trending off track while also controlling costs.

A major issue is limited visibility into underlying and pre-existing medical issues that are major factors that drive claim complexity. Treatment decisions and recovery timelines are often influenced by comorbidities, prior injuries, or medications. These factors aren’t always immediately accessible to the individuals and teams tasked with managing a claim.

For example, an underlying condition or a prior surgery might not appear in the initial claim data or be volunteered by a claimant. Without that context, an adjuster can be hard-pressed to identify claims with emerging risk and delayed recovery characteristics.

That lack of clarity makes it harder for claims professionals, nurse case managers, and other stakeholders to confidently assess risk and take action. At Gradient AI, our solution enhances productivity of adjusters and nurses by alerting them to risks within daily workflows, reducing manual triage and improving claim segmentation.

The Compounding Engine: Embedding Analytics into Workflows

Q: How does data, and increasingly AI solutions, help address these challenges?

A: The real value of predictive analytics comes when insights are embedded directly into claims workflows. As much as possible, claims organizations need to make it easy for adjusters and clinical resources to access information that empowers them to make claims handling and treatment decisions.

When analytics are operationalized, they can flag high-risk claims earlier in the lifecycle, utilize resources more effectively, and support more consistent decision-making across teams. For example, identifying a claim at risk of delayed recovery early on allows for timely intervention, whether that’s additional clinical review, care coordination, or alternative treatment pathways.

Over time, this creates a compounding effect. Better decisions at the claim level lead to improved outcomes, which then inform future models and workflows. The result is not just incremental improvement, but sustained performance gains across underwriting and claims operations.

Conclusion

Workers’ compensation claims will always involve complexity. The differentiator is how effectively organizations translate that complexity into action at scale.

Integrating AI into your workflows and working with the right partner helps turn complex models into clear signals. That means better insight into risk, earlier and more confident decisions, and a shift from reactive management to a more proactive, data-driven approach.

Solutions like Gradient AI’s ClaimVector™ and ClaimVoyant™ exemplify this approach. They help organizations benchmark performance, identify high-risk claims early, and surface actionable insights directly within workflows. By operationalizing advanced analytics in this way, claims teams are better equipped to make consistent, informed decisions that improve both financial outcomes and injured worker care.

Key Takeaways for Claims Leaders

Operationalizing insights at the point of decision-making is critical to improving outcomes.
Clear, explainable signals build confidence across claims, clinical, and leadership teams.
Embedded analytics drive efficiency, consistency, and continuous performance gains.
Balancing financial outcomes with injured worker care remains central to long-term success.

Brook Rosenbaum leads strategy and execution for Gradient’s Property and Casualty segment. He brings more than 15 years’ experience in the P&C insurance industry. Brook has held leadership positions in product management and development, digital transformation, and go-to-market strategy in Insurtech and large carriers.

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Getting to the How and Why: AI Shows Its Work

Thu, 19 Mar 2026 16:44:46 GMT

March 19, 2026

With insurance sector AI solutions, explainability was once a hindrance to widespread adoption. Those days are waning thanks to next-gen models, according to Gradient AI’s Stan Smith.

For years, the most pressing questions surrounding artificial intelligence solutions for insurance underwriting concerned accuracy. In a sector where margins are thin and exacting calculations non-negotiable, could AI be accurate enough to trust?

That question has largely been answered. Modern AI systems routinely outperform traditional actuarial and rules-based models across a range of underwriting applications. They uncover patterns humans overlook, integrate broader data sources, and continuously learn from outcomes. In terms of predictive performance, AI has arrived.

Yet adoption across insurance remains cautious and disjointed. Why? Because the hesitance involves explainability rather than accuracy.

This conclusion follows simple logic: Insurers are rational actors. Underwriting is fundamentally about pricing risk as precisely as possible. If a new tool improves loss ratios and portfolio performance, carriers have every incentive to use it.

But accuracy alone has never been enough. Insurance operates in a highly regulated environment. Underwriting decisions must be defensible—to regulators, auditors, reinsurers and internal governance teams. Insurers must not only make sound decisions; they must clearly explain how and why those decisions were made.

For decades, that requirement has shaped underwriting practices. Traditional statistical models often persist not because they are the most predictive but because they are transparent. Underwriters can trace decisions back to specific variables. Regulators can follow the logic step by step. The reasoning is visible.

Early machine learning models disrupted that balance. While often transformative in performance, first-generation AI systems struggled to “show their work.” They produced highly accurate scores and recommendations but offered little meaningful insight into how those outcomes were generated.

For underwriters, that opacity was uncomfortable. For compliance teams, it was untenable. As a result, many insurers accepted lower predictive performance in exchange for greater transparency. Even when AI was adopted, it was often constrained—”good enough” models were favored over those more powerful but less explainable.

This was not resistance to innovation but merely good governance. Underwriters must understand recommendations to trust and apply them. Executives need confidence that AI aligns with risk appetite and company policy. Regulators need clarity around methodology, bias controls and decision logic. Without explainability, even the most accurate model is incomplete.

Over time, this dynamic hardened into a false tradeoff: performance versus transparency. The industry assumed that more accurate models would necessarily be less explainable.

That assumption is now outdated. Recent advances in AI, particularly the integration of large language model techniques with advanced analytics, are redefining explainability. Modern systems no longer simply output risk scores. They can translate complex model behavior into plain-language reasoning aligned with underwriting judgment.

Instead of forcing underwriters to interpret abstract probabilities, today’s tools can articulate the specific drivers behind a recommendation. For example, in healthcare underwriting, sophisticated platforms can replicate analyses that once required manual nurse review—while clearly outlining the conditions, utilization patterns or cost drivers that influenced the result.

Explainability in this new context does not mean exposing every mathematical parameter. It means making decision logic understandable, defensible and actionable for the people responsible for applying it.

When underwriters understand why a model reached a conclusion, confidence increases. When regulators can see how key factors are measured and managed, resistance decreases. Explainability transforms AI from a perceived compliance risk into a governance asset.

This is especially critical in addressing concerns about bias. Insurers must demonstrate not only that their models perform well but that they perform fairly. That requires visibility into training data, performance across populations and outcomes over time.

AI systems operating across multiple carriers and geographies offer a broader data foundation for identifying and mitigating bias. With deeper and more diverse datasets, potential issues can be detected earlier, validated when absent or corrected when present. In this context, explainability becomes central to sustaining long-term trust. A model that cannot clearly demonstrate how it detects and mitigates bias will struggle to satisfy regulators, regardless of its predictive strength.

The value of explainable AI is particularly evident in complex underwriting environments where human intuition has limits. Consider small commercial property/casualty insurance policies. Based on my company’s internal data assessment, more than 98% may never generate a claim, yet a small subset accounts for most losses. Identifying that subset using traditional methods is extremely challenging.

AI excels at detecting subtle combinations of factors that signal higher risk. Crucially, explainable AI can also articulate what those factors are, allowing underwriters to validate and act on the insight rather than blindly accepting a score.

A similar dynamic exists in small-group healthcare underwriting, where a single high-cost individual can materially impact profitability. Advanced models can detect early indicators of elevated risk that conventional approaches may miss while clearly explaining the drivers behind the assessment.

In each case, adoption hinges less on raw accuracy than on whether the recommendation can be understood and defended. AI must align with regulatory expectations, underwriting workflows and enterprise risk management practices. It must build confidence rather than demand blind trust.

The future of AI-driven underwriting is not about replacing human expertise. It is about augmenting it with systems that predict accurately and explain clearly. When that balance is achieved, the supposed tradeoff between performance and transparency disappears.

The misconception that insurers must choose between what works and what can be explained no longer holds. The technology is mature. The data is robust. The models are proven. As explainable AI continues to advance, insurers no longer have to compromise between predictive power and defensibility. They can—and increasingly will—have both.

This article appeared first on Carrier Management .

Gradient AI Launches Workers’ Comp Insurance Triage Solution That Flags Potentially Expensive or Complex Claims Upon First Notice of Loss

Tue, 17 Mar 2026 11:00:12 GMT

With a match rate exceeding 90%, ClaimVoyant™ assesses relative claim complexity given underlying co-morbidities and other health issues, providing opportunities for early staff assignment and optimized care management.

Boston, MA – March 17, 2026 – G radient A I , a prominent enterprise software provider of artificial intelligence solutions for the insurance industry, has introduced an AI tool allowing Workers’ Comp (WC) carriers, third-party administrators (TPAs) and self-insured employers to flag claims likely to be expensive, complex or involve delayed recovery periods. The company’s ClaimVoyant™ solution provides early indicators that create opportunities for thoughtful intervention, leading to appropriate staff assignment and proper care management planning.

Expenses for claimants with pre-existing physical comorbidities and psychological conditions can be three to four times as high as those without such conditions. Early identification of these claims enables proactive treatment that better manages the recovery trajectory for an injured worker, giving AI tools like ClaimVoyant™ a clear, commonsense path to expedient return on investment.

With a claimant match rate exceeding 90%, Gradient AI’s ClaimVoyant™ solution parlays data captured at First Notice of Loss (FNOL) into actionable insight for program managers. In doing so, the AI-driven triage tool provides a gateway to deeper intervention for injured workers, including recommendations on whether to obtain ClaimVoyant™ detailed reports, available with medical authorization. ClaimVoyant™ is available at an accessible price point and offers a variety of delivery options – including portal, application programming interface (API), computer system validation (CSV), and core system integration.

“Our new ClaimVoyant solution delivers powerful insights at FNOL that enables carriers, TPAs and self-insured employers to take meaningful action before a claim trajectory takes a negative turn,” said Brook Rosenbaum, General Manager, Property & Casualty for Gradient AI. “Notably, ClaimVoyant’s ease of use and flexibility allows for integration in virtually any claims handling workflow.”

Gradient AI’s ClaimVoyant™ triage tool is currently available for Workers’ Comp insurance settings. The company has plans to expand the solution’s purview to additional lines of business in the future.

# # #

About Gradient AI

Unlike other solutions that use a limited claims and underwriting dataset, Gradient AI’s software-as-a-service (SaaS) platform leverages a vast industry data lake comprising tens of millions of policies and claims. It also incorporates numerous other features including economic, health, geographic, and demographic information. Customers include some of the most recognized insurance carriers, MGAs, MGUs, TPAs, risk pools, PEOs, and large self-insured employers across all major lines of insurance. By using Gradient AI's solutions, insurers of all types achieve a better return on risk. To learn more about Gradient AI, visit: www.gradientai.com or follow on LinkedIn .

Move Beyond Static Workers’ Comp Reporting

Mon, 16 Mar 2026 15:43:35 GMT

February 18, 2026

The Gradient AI Team

How AI-Driven Claims Benchmarking Helps Brokers Deliver Deeper Client Insights

For many brokers and consultants, Workers’ Compensation reporting hasn’t evolved much in the past decade. Clients still receive claim summaries, loss runs, and high-level trend charts, but these reports rarely answer the questions clients actually care about.

Why are our claims trending upward?
How do we compare to similar employers?
What operational changes could improve outcomes?

Traditional claims reports summarize historical activity, but they fail to provide the context needed to explain performance or guide strategy. As client expectations grow, brokers are increasingly asked to provide deeper analysis and clearer recommendations.

This is where modern claims benchmarking and AI-driven analytics are beginning to reshape how brokers evaluate Workers’ Compensation performance.

The Limitations of Traditional Claims Analytics

Even with modern dashboards, many Workers’ Compensation analytics platforms still rely on static reporting and generalized industry averages. While these tools can visualize claims data effectively, they often fall short when brokers need deeper analysis.

Common challenges include:

Industry averages that lack meaningful context
Difficulty comparing claims performance across regions or industries
Inconsistent methodology when analyzing long-term trends
Limited ability to identify the operational drivers behind claims outcomes

As a result, brokers often spend significant time translating raw data into client-ready explanations. Without reliable comparison points, it can be difficult to move beyond describing what happened to explaining why it happened and what actions might improve results.

Clients increasingly expect brokers to deliver forward-looking insights, not just historical reporting.

Why Claims Benchmarking Matters

Effective benchmarking allows brokers to evaluate a client’s claims performance relative to organizations with similar risk profiles. When done correctly, this comparison provides important context for understanding trends and identifying opportunities for improvement.

Rather than relying solely on broad market statistics, meaningful claims benchmarking analyzes performance across factors such as industry, geography, claim maturity, and claim outcomes. This approach provides a clearer picture of how a portfolio compares to relevant peers.

For brokers, that context transforms claims reporting into a more strategic advisory conversation.

AI Is Changing How Claims Performance Is Analyzed

Advances in artificial intelligence are making more sophisticated benchmarking possible. By analyzing large volumes of structured claims data, AI-driven tools can identify patterns, highlight emerging risks, and reveal performance differences across comparable organizations.

One example is ClaimVector™ , recently introduced by Gradient AI.

The platform applies AI-powered benchmarking logic specifically designed for Workers’ Compensation portfolios. Instead of relying on generalized market comparisons, it evaluates claims performance using structured analytics grounded in real claims data.

This enables brokers to:

Compare portfolio performance against relevant peer groups
Identify underlying drivers of claims trends
Detect emerging risk patterns earlier
Apply consistent year-over-year performance analysis
Support more informed client strategy discussions

The result is a shift from basic reporting toward more data-driven decision support.

Supporting Better Outcomes for Clients

When brokers have access to stronger claims intelligence, they are better positioned to guide clients toward measurable improvements.

Deeper benchmarking insights can help organizations:

Identify emerging injury trends earlier
Focus prevention initiatives on the most common risks
Strengthen claims management practices
Improve outcomes for injured workers

“Brokers are increasingly expected to deliver deeper insights into claims performance. By transforming real claims data into practical benchmarking intelligence, ClaimVector helps brokers strengthen their advisory role and provide clients with clearer guidance on improving outcomes.”
— Brook Rosenbaum, General Manager, Property & Casualty, Gradient AI

Designed for Broker Workflows

A key challenge for many analytics platforms is translating complex data into insights brokers can confidently share with clients. Tools designed specifically for broker workflows can simplify that process.

AI-driven benchmarking platforms embed Workers’ Compensation expertise directly into the analytics workflow, helping brokers evaluate performance across multiple client portfolios while maintaining a consistent analytical approach.

As more claims data is analyzed over time, these platforms can continue to refine comparisons and surface increasingly valuable insights.

Moving Beyond Static Reporting

Workers’ Compensation analytics is entering a new phase. As AI-powered claims benchmarking becomes more accessible, brokers have an opportunity to move beyond static reporting and deliver deeper, more strategic guidance.

By combining structured benchmarking with real-world claims data, brokers can better evaluate performance, uncover opportunities for improvement, and help clients achieve stronger claims outcomes.

For organizations seeking a more proactive approach to Workers’ Compensation risk management, that shift could make a meaningful difference.

Traditional Claims Reporting vs AI-Powered Benchmarking

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At SIIA Spring Exchange, Gradient AI to Give Presentation on Best Practices for Choosing AI-driven Risk Management Solutions

Thu, 05 Mar 2026 17:37:38 GMT

Amid an uptick of new AI solutions in the insurance landscape, discussion

will explore key considerations to help guide the partner selection process.

Boston, MA – March 5, 2026 – Gradient AI , a prominent enterprise software provider of artificial intelligence solutions for the insurance industry, will give a presentation intended to help guide the AI solutions partner selection process at the Self-Insurance Institute of America’s (SIIA) Spring Exchange in New Orleans. On April 1 from 3:30-4:30 pm, senior sales director Matt Weaver will be among the Gradient AI executives to present “Five Key Considerations for Choosing the Right AI Solution – and the Right AI Partner.”

As more AI-driven insurance underwriting and risk management products enter the market, the discussion aims to help support streamlined solutions selection through well-informed capabilities prioritization. For example, insurance underwriters and other decision-makers are well advised to consider solutions that support integrated workflows and enterprise-wide risk intelligence, rather than siloed processes that can leave data analysis blind spots. Two other mission-critical AI solution elements – accuracy and continuous evolution – also will be addressed in-depth amid a broader discussion on precision and perpetual improvement.

As with any new technology, the importance of change management is another key component of successful adoption. The presentation will explore the attributes of an optimized change management strategy, including clear roles for both insurance companies and their AI solutions partners.

Gradient AI’s presentation at the SIIA Spring Exchange aligns with the company’s holistic, full lifecycle approach to AI-supported insurance underwriting decision-making. Viewed as a natural step up from siloed, single-purpose solutions, Gradient AI’s integrated approach is intended to achieve more precise, well-reasoned recommendations that consider significantly broader sets of data, variables and trends. For group health, the company’s fully integrated platform features Gradient SAIL™, which fills in data gaps and provides industry-leading match rates on medical, Rx, Rx specialty and lab details.

Additionally, Gradient AI’s Renewal Analytics solution focuses on the projection of anticipated claims going forward – not just claims that have already occurred – to allow for more accurate risk prediction. Together, these products detect new and emerging health risk during policy terms, create consistent risk scoring for better pricing accuracy, improve underwriter efficiency and speed, identify high-value retention and growth opportunities, and support greater population health.

“With so many choices when it comes to AI risk management solutions, integration, evolution and accuracy are key,” explains Matt Weaver, Senior Sales Director at Gradient AI. “We are delighted for the opportunity to provide information to SIIA members that helps them not only select the best solutions for their specific needs, but also successfully adopt, integrate and leverage these technologies.”

Those attending the SIIA Spring Exchange can learn more about this presentation, as well as request a meeting with Gradient AI, by visiting this link .

# # #

About Gradient AI

CIBC Innovation Banking Provides Growth Capital to Gradient AI

Tue, 03 Mar 2026 23:37:22 GMT

March 3, 2026

BOSTON--( BUSINESS WIRE )-- CIBC Innovation Banking announced today that it has provided growth capital financing to Gradient AI (Gradient). This financing will support Gradient’s growth plans and development efforts to better serve clients and address evolving challenges across the insurance industry.

Gradient is a leading enterprise software provider of artificial intelligence solutions in the insurance industry. The company’s solutions help improve loss ratios and profitability by predicting underwriting and claim risks with greater accuracy, as well as reducing quote turnaround times and claim expenses through intelligent automation.

"While we are thrilled to secure this investment from CIBC Innovation Banking, it is now up to us to continue to address the industry challenges by enhancing our platform and delivering unparalleled value to our customers,” said Stan Smith, CEO of Gradient AI. “Insurers are becoming increasingly sophisticated in their risk assessment, yet challenges still arise. We are focused on helping them achieve these goals by automating processes, reducing costs, and significantly improving results.”

“We are excited to work with and support the Gradient AI team as it continues to expand its market footprint and transformational solutions across the insurance industry,” said George Bixby, Director at CIBC Innovation Banking. “The team’s innovative approach to leveraging artificial intelligence is reshaping how insurers assess risk, manage claims, and deliver value to their customers.”

Gradient is backed by Centana Growth Partners, MassMutual Ventures, Sandbox Insurtech Ventures, and Forte Ventures.

About CIBC Innovation Banking

CIBC Innovation Banking has 25 years of specialized experience in growth-stage tech and life science companies across North America – a longer track record than most banks. CIBC Innovation Banking now has over $11 billion in funds managed including life sciences, health care, cleantech companies, investors, and entrepreneurs, and has assisted over 700 venture and private equity-backed businesses over the past six and a half years. The bank operates out of 14 global locations in San Francisco, Menlo Park, New York, Toronto, London, Austin, Boston, Chicago, Seattle, Vancouver, Montreal, Atlanta, Reston, and Durham. Connect with us today to start the conversation. Innovationbanking.cibc.com

About Gradient AI

Gradient AI is a leading provider of proven artificial intelligence (AI) solutions for the insurance industry. Its solutions improve loss ratios and profitability by predicting underwriting and claim risks with greater accuracy, as well as reducing quote turnaround times and claim expenses through intelligent automation. Unlike other solutions that use a limited claims and underwriting dataset, Gradient AI’s software-as-a-service (SaaS) platform leverages a vast industry data lake comprising tens of millions of policies and claims. It also incorporates numerous other features including economic, health, geographic, and demographic information. Customers include some of the most recognized insurance carriers, MGAs, MGUs, TPAs, risk pools, PEOs, and large self-insured employers across all major lines of insurance. By using Gradient AI's solutions, insurers of all types achieve a better return on risk. To learn more about Gradient AI, visit: https://www.gradientai.com or follow on LinkedIn .

Contacts

Katarina Milicevic, katarina.milicevic@cibc.com , 416-362-3458

This article first appeared on BusinessWire.

Gradient AI Launches Workers’ Comp Benchmarking Tool

Thu, 19 Feb 2026 20:55:00 GMT

February 19, 2026

By Anthony R. O’Donnell

The AI-powered ClaimVector solution provides brokers with claims-based performance benchmarks grounded in real data.

Gradient AI (Boston) has launched ClaimVector for Workers’ Compensation, an artificial intelligence-powered benchmarking solution designed to help brokers analyze claims performance using real data.

Gradient AI says the platform transforms limited claims information into metrics that enable brokers to compare portfolio performance, identify risk drivers and support advisory discussions with clients and prospects. The company describes the tool as an alternative to market-average benchmarking, using claims-level data to generate explainable comparisons.

According to Gradient AI, ClaimVector is built to integrate into broker workflows, supporting repeatable benchmarking processes rather than one-time or manual competitor analyses. The company says the system provides transparent metrics intended to strengthen claims oversight and renewal strategies.

“By mining and dashboarding real data, the ClaimVector benchmarking solution helps Workers’ Comp brokers substantially improve key portfolio differentiators, as well as build and enhance credibility, consistency, and defensibility,” says Brook Rosenbaum, General Manager, Property & Casualty, Gradient AI. “The insights this solution provides translate directly to strengthened claims stewardship and renewal strategies.”

Gradient AI says the benchmarking tool refines its comparisons over time as it processes additional data from a broker’s book of business. The company provides artificial intelligence solutions for property/casualty insurers and other risk-bearing organizations.

This article first appeared on Insurance Innovation Reporter .

New Gradient AI Solution Provides AI-Powered Workers’ Compensation Claims Benchmarking for Brokers

Thu, 19 Feb 2026 15:40:22 GMT

Far more exacting than abstract market averages, Gradient AI’s ClaimVector ^™ benchmarking solution is grounded in real claims data – providing defensible, data-driven decision-making for enhanced claims oversight.

Boston, MA – February 19, 2026 – Gradient AI , a prominent enterprise software provider of artificial intelligence solutions for the insurance industry, is launching ClaimVector ^TM for Workers’ Compensation (WC), an AI-powered solution allowing brokers to transform limited claims information into actionable insights. The result is clear, verifiable metrics that brokers can utilize to concisely compare performance, identify risk drivers, improve outcomes, and support confident advisory conversations with current and potential customers.

A data-driven departure from abstract market averages, ClaimVector ^™ applies deep WC expertise to real broker workflows, turning experience into practical decision support and delivering transparent, explainable benchmarks brokers can confidently stand behind. The solution is designed for efficient, repeatable broker workflows rather than labor-intensive, siloed competitor analyses and progress assessments.

As Workers’ Comp and other insurance segments continue to benefit from integrated artificial intelligence solutions, Gradient AI has emerged as a top-tier provider whose platforms help brokers, carriers and other key industry players make more thoroughly informed decisions. Laterally synchronized for deeper data sets, Gradient AI’s solutions have been recognized for predicting underwriting and claim risks with greater accuracy – resulting in improved loss ratios and profitability while reducing quote turnaround times and claim expenses.

Like all AI-driven insurance solutions, the ClaimVector™ benchmarking solution grows more effective with time. As the system becomes more accustomed to a WC broker’s specific book of business, the comparisons it provides become more honed and, therefore, more valuable. At the same time, the system helps brokers establish a standardized practice for benchmarking not only against competitors, but also against their own previous portfolio performance.

“By mining and dashboarding real data, the ClaimVector™ benchmarking solution helps Workers’ Comp brokers substantially improve key portfolio differentiators, as well as build and enhance credibility, consistency, and defensibility,” said Brook Rosenbaum, General Manager, Property & Casualty for Gradient AI. “Knowledge is power, and the insights that this groundbreaking solution provides translate directly to enhanced advisory value, strengthened claims stewardship, and elevated renewal strategies.”

# # #

About Gradient AI

Why Explainable, Integrated AI Is the Future of Insurance Underwriting

Wed, 18 Feb 2026 22:56:29 GMT

February 18, 2026

The Gradient AI Team

Q&A with Gradient AI Founder and CEO Stan Smith.

Q: What are your thoughts on the current insurance underwriting landscape overall? What’s happening right now?

Stan Smith:
Overall, insurers are becoming more comfortable using advanced analytics in underwriting and relying on a broader range of third-party data. Rather than depending solely on their own historical experience and narrow internal datasets, carriers are increasingly adopting more sophisticated tools because they’re seeing better results. Ultimately, outcomes are driving openness to newer techniques, and that’s the most important trend.

Q: As AI-driven underwriting solutions rapidly evolve, insurers have very different levels of AI maturity. How important is it to meet underwriters where they are, especially when their exposure to AI is still fairly basic?

Stan Smith:
Explainability is the fundamental issue. Insurers have historically relied on traditional methods not only because they understand them, but because regulators do as well. More advanced approaches raise concerns about complexity, specifically whether the methodology, lack of bias, and decision logic can be clearly explained to regulators. That concern has slowed adoption in many cases.

Q: What factors have most constrained broader adoption of AI-driven underwriting solutions?

Stan Smith:
The biggest constraint has been the belief that AI cannot deliver sufficient explainability. As a result, some organizations choose simpler, more transparent approaches, even when they’re less accurate. There’s been a clear divide: traditional methods prioritize explainability, while AI prioritizes accuracy. The real breakthrough comes when AI can deliver both. AI with explainability ultimately wins because it provides the most accurate pricing while still meeting regulatory and governance requirements.

Q: Many underwriters’ early experiences with AI involved first-generation solutions. How does Gradient AI address the concerns that came from those early limitations?

Stan Smith:
We leverage newer large language model techniques to help explain what advanced AI models are clearly identifying and in ways that are useful to underwriters. Earlier approaches often fell short in helping users truly understand the “why” behind a recommendation. Today, we can provide not just a decision, but the reasoning behind it in plain language.

For example, in healthcare underwriting, our platform can now deliver the kind of analysis that previously required a nurse review. Underwriters receive actionable insights and explanations directly from the AI, which significantly improves efficiency and confidence in the decision-making process.

Q: Would you also say that Gradient AI overcomes adoption roadblocks by acting as a true partner to its clients?

Stan Smith:
Absolutely. We work closely with our clients as partners, not just as a technology vendor. That means proactively addressing concerns around legal requirements, technology integration, utilization, and outcomes. A major focus is ensuring we can measure, demonstrate, and validate the absence of bias in the data and models we provide.

Because Gradient AI works with data across multiple carriers and geographies, we’re uniquely positioned to assess bias at a broader level. We can identify potential issues, prove when bias is not present, and help eliminate it where necessary. That capability is critical for building trust and long-term adoption.

Q: What are some key signals or patterns that AI-enabled underwriting solutions detect that humans might miss?

Stan Smith:
A strong example is small commercial policies in the P&C market, typically policies under $20,000 in premium. More than 98% of these policies never experience a loss. The challenge for underwriters is identifying which small subset is most likely to generate future claims.

AI excels here by analyzing the characteristics of policies that have experienced losses and identifying subtle combinations of factors that humans would struggle to detect. While AI won’t predict every individual loss, it can identify the group of policies most likely to account for the majority of claims. This has proven to be an extremely effective use case for AI in small commercial underwriting.

Another example is healthcare underwriting for small groups. Traditionally, carriers preferred large groups because risk naturally balances out. In small groups, however, a single high-cost individual can make the group unprofitable. Traditional methods often failed to detect that risk, which is why Gradient AI has been particularly successful in the small-group healthcare market since launch.

Q: As AI solutions become more sophisticated, how do you ensure they remain user-friendly?

Stan Smith:
It comes down to actionable intelligence. Underwriters need to quickly understand how the information helps them make decisions – daily, weekly, or long-term. The solution must clearly highlight areas of concern, allow users to drill down into key drivers, and show them what actions they can take to improve outcomes.

Q: How does more accurate underwriting benefit consumers and the insured, not just insurers?

Stan Smith:
Accuracy builds trust. When consumers distrust insurers, it’s often because they believe risk isn’t being assessed fairly. Gradient AI has no incentive to over- or underprice risk since we’re not the carrier. Our goal is to provide an unbiased, accurate representation of risk and expected cost so carriers can price appropriately and sustainably.

When the same organization both assesses risk and profits directly from pricing decisions, perceived bias can exist. Our independence helps eliminate that concern, which ultimately benefits consumers through fairer pricing and more stable coverage.

Q: Gradient AI differentiates itself through the integration of previously siloed AI solutions. How far ahead is the company in this area?

Stan Smith:
The insurance industry remains highly siloed, with many endpoint solutions and fragmented datasets. This makes it difficult for insurers to gain a holistic understanding of their business, such as identifying duplicate exposures or evaluating risk across multiple lines.

A core part of our strategy is connecting these silos to provide a unified view of a carrier’s book of business. That holistic perspective allows insurers to operate more strategically and make better decisions across their organization.

Q: How does an integrated risk management lifecycle improve enterprise-level performance?

Stan Smith:
AI becomes more powerful as it learns from a company’s own performance over time. By observing the full lifecycle of risk within a client’s portfolio, our models can identify which types of risks perform well and which do not.

Future underwriting, portfolio management, and strategic decisions are then informed by that specific carrier’s historical strengths and challenges, not generic industry averages. The system continuously learns, becoming more accurate and better aligned with the insurer’s capabilities over time.

Q: What’s the next game-changing milestone for AI-driven underwriting, and how is Gradient AI moving toward it?

Stan Smith:
The next major milestone is bringing together data that has never been meaningfully combined before. For example, insights from healthcare data informing property and casualty underwriting. Historically, data has remained locked within verticals, limiting its broader value.

Gradient AI is at the forefront of responsibly connecting these datasets to generate deeper insights for our customers and unlock new opportunities across insurance lines.

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Five Keys to Choosing the Right AI Tool and the Right Partner for Profitable Risk Management

Tue, 20 Jan 2026 20:01:24 GMT

GROUP HEALTH INSIGHTS BLOG | Nov 13, 2023

The Gradient AI Team

In this Blog post, we outline five practical criteria to help organizations evaluate AI tools and partners that actually drive profitable growth, not just better reporting.

Whether you’re a carrier, consultant, MGA, captive, pooled risk group, or PEO , your challenge remains how to better manage and project risk , while growing your book of business . Your strategy and the tools utilized need to be addressed and refined to reach your objectives.

Here are five key considerations in choosing the right tool and the right partner:

1. Get the right data to make accurate decisions.

Whether you use an in-house tool or a third-party solution, without the correct – and timely – data, you are lost. Ask yourself and your team:

Are you getting the detail for risk assessment that shows not just claims that have already occurred but anticipated claim costs in the future?
Does your AI solution provide at least a 90% match rate on medical, Rx, Rx specialty and/or lab of the headcount provided in a census?
Can your current tool fill the inevitable gaps in data that come with every census?
If you answered no to any of these questions, you need to look for a new AI solution.
Remember, a pivotal component to risk management is risk projection . You can’t change what was. You can change and impact what will be.

“Prior to partnering with Gradient AI, we could only look through the rear-view mirror at past claims data. Now we can look through the windshield to better predict costs our clients can expect to incur.”

- Aaron MacDonald, chief strategy officer at Michigan Planners

2. Make sure your AI solution evolves and provides the insight you need.

Innovation can be an overused term. And every company claims to be “an innovator.” How do you know you’ve partnered with the right AI provider?

First, are you getting the complete picture of risk and all its drivers or are you a victim of “black box underwriting?” Black box underwriting means the reasons behind the risk score are not easily explainable or transparent. Your AI partner should not only provide a risk score, but they should also be able to share the detail around that score which you can apply to the business case at hand.

For example, among numerous other data points, does your AI partner deliver:

Top conditions
Top drugs
High-cost claims reporting
Stop loss risk scores
Condition flags

At Gradient AI, we’re constantly iterating and striving to improve our outputs. We were the first to provide access to medical, lab and prescription data and the first to provide actionable insights.

“We sought a tool capable of greatly enhancing our risk assessment efforts, especially in areas with limited or no detailed group-specific data. Gradient AI’s solution not only helped us make better-informed decisions but provided us with the critical detail of what is driving that risk. With other competitors’ models, that critical information was missing.”

- Jennifer Taylor, Director of Alternative Risk, Evolution Risk Partners (MGU)

3. Choose AI That Connects the Entire Risk Lifecycle.

AI offers clear advantages in speed, consistency, and analytical depth. However, realizing the benefits of AI in risk management requires more than simply adopting AI point solutions in isolation.

Organizations that use separate tools for new business underwriting, renewals, and population health often struggle to generate consistent, actionable intelligence. Fragmentation creates blind spots that limit both performance and scalability, including:

Disconnected data and learning loops , preventing insights from being reused across the risk lifecycle
Inconsistent pricing and decisioning , making outcomes harder to track and compare
Missed changes in group risk profiles , such as new members or emerging health trends

To truly improve performance, you must move away from siloed processes and toward coordinated full-cycle workflows that align risk management across new business acquisition, population health management, and existing group renewals.

When evaluating AI solutions and partners, prioritize those that support integrated workflows and enterprise-wide risk intelligence—not isolated use cases. The right AI partner helps unify these workflows—ensuring insights flow seamlessly across the entire book of business.

4. Find a great partner, not just a great tool.

The right AI tool is important, but the right AI partner is essential. True value comes from a partner who is easy to work with, understands your business at a deep level, and helps turn data into decisions—not just dashboards.

A strong AI partner delivers value through:

Consultative subject matter experts, not just technical expertise
Provides access to underwriting and risk management experts who understand the challenges of your business.
Works alongside your team to improve speed, accuracy, and profitability in underwriting
Takes the time to learn your underwriting philosophy, unique cases, and workflows

The right people supporting the right data
Assigns experienced support teams who know how to source, validate, and structure the data that actually matters
Helps translate complex data into actionable underwriting insights
Recognizes that data alone doesn’t solve problems—getting the right data does

Solutions that meet you where you are
Adapts to your organization’s position on the technology adoption curve
Delivers practical solutions that fit your current workflows while enabling future growth
Addresses your goals rather than forcing a one-size-fits-all approach

At Gradient AI, we see successful partnerships as more than just fair pricing. They’re built on taking the time to understand your business, the nuances of your risk decisions, and the processes that support them. This insight enables us to design solutions aligned with your objectives and challenges, instead of asking you to conform to a standardized approach.

“This is not meant to be a standalone underwriting tool. It is advanced decision support. Do your due diligence. Conduct your market research. If you want to see where our competitors will be in the future, look at Gradient AI today.”

- Matt Weaver, Senior Sales Director, Gradient AI.

5. Change Management: where adoption succeeds or fails.

Change management is an art, not a science. How you introduce AI matters just as much as the technology itself. Successful adoption starts with aligning your people, processes, and goals, plus clearly communicating why change is happening and how it benefits your underwriting teams.

A strong change strategy focuses on your ability to:

Assess readiness before you deploy
Evaluate your staff, resources, and workflows with a change-readiness assessment
Understand upstream and downstream impacts across underwriting, operations, and leadership
Reinforce that AI supports underwriters, it doesn’t replace them

Align the tool to your team and objectives

o Adopt AI that fits your current capabilities and scales with your goals

o Avoid forcing change for the sake of technology

o Equip teams with tools that increase confidence and consistency in risk decisions

Set clear expectations and outcomes
Communicate why you’re adopting AI, or why you’re switching tools
Define success in terms that matter, such as faster turnaround times, better decision support, reduced backlogs
Managing risk, pricing right, and enhanced decision-making lead to better outcomes for your customers, your team, and your company.

The true value of AI is realized when change is introduced with clarity, purpose, and alignment, turning technology investments into lasting adoption and measurable results.

The most successful organizations will rethink not just the AI tools they use, but how those tools fit into their teams, workflows, and long-term goals. When AI is implemented with purpose and supported by the right partner, it empowers underwriters to see risk more clearly, act more decisively, and deliver better outcomes across new business, renewals, and population health.

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Explore the benefits of Gradient AI's Group Health Solutions.

Gradient AI Earns “Best Places to Work” Awards from Technology Recruitment Platform Built In

Tue, 20 Jan 2026 17:35:03 GMT

A leading provider of AI-driven enterprise software for the insurance sector, Gradient AI was recognized by Built In for the sixth consecutive year.

Boston, MA – January 20, 2026 – Gradient AI , a prominent enterprise software provider of artificial intelligence solutions for the insurance industry, has been recognized by leading technology recruitment platform Built In as one of the Best Places to Work in 2026 . The annual awards honor employers across the U.S. who set standards for today’s workforce. Gradient AI was named in the “Best Midsize Places to Work in Boston” category, as well as the overall “Best Places to Work in Boston” list; both consider factors such as compensation, benefits, and employee-centric culture.

As group health, property & casualty and other insurance segments continue to benefit from integrated artificial intelligence solutions, Gradient AI has emerged as a top-tier provider whose platforms offer simpler, more thoroughly informed underwriting decisions. Laterally synchronized for deeper data sets, Gradient AI’s solutions have been heralded for predicting underwriting and claim risks with greater accuracy – resulting in improved loss ratios and profitability while reducing quote turnaround times and claim expenses.

The recognition from Built In is further validation of Gradient AI’s innovative business model. Testament to Gradient AI’s success is its dynamic working environment, one where team members enjoy ample opportunities for professional enrichment and advancement. Other positive job satisfaction indicators include work-life balance, remote options and stock options. Gradient AI’s categorization as a “midsize” employer also exemplifies its growth, with 2026 marking the third year of its graduation from Built In’s “Best Places to Work” list for startups and smaller organizations.

“Our position as pioneers in the AI insurance solutions landscape is only made possible by our exceptionally talented team, who are truly the heartbeat of our growing company,” said Stan Smith, Founder & CEO of Gradient AI. “While we’re certainly a tech-centric organization, ensuring that insurance policies are fair and economically viable for everyone – both insurers and the insured – requires a people-first approach. We strive to embody this mindset through a working environment that provides mentorship, invites feedback and welcomes a broad range of viewpoints.”

Now in its eighth year, Built In’s Best Places to Work program celebrates the companies shaping the future of work. In a rapidly evolving AI-first job market, recognition as a Best Place to Work helps employers stand out as trusted brands when candidates turn to tools like ChatGPT and Google AI Overviews to research where to work next.

The awards reflect Built In’s data-driven approach, evaluating companies based on compensation, benefits, and company-wide culture programs.

“Today’s candidates discover the companies they want to work for using AI tools,” said Maria Christopoulos Katris, Founder & CEO of Built In. “Earning a Best Place to Work award not only signals to candidates that you invest in your people, it’s a lever to strengthen how AI search tools understand and represent your company’s story.”

To learn more about the 2026 Best Places to Work program and view all winners, click here .

# # #

About Gradient AI

Group Health Insurers Must Integrate AI

Mon, 05 Jan 2026 21:06:14 GMT

January 5, 2026

By Marc Jeffreys

Group health insurers using siloed AI tools miss opportunities that connected solutions across policy lifecycles could provide.

Group health insurance executives: If your AI is constricted, so are your policies.

In recent years, underwriters and other group health insurance policy designers have leaned in to artificial intelligence tools. In fact, a recent NAIC survey of U.S. health insurers showed that 84% currently use some form of AI and machine learning.

This substantial shift toward AI policy design and management tools is completely understandable – and completely necessary to stay competitive. In a sector where rising costs, unpredictable claim patterns and shifting risk profiles continue to hinder forecasting precision, business-as-usual methods often fall short as teams responsible for assessing and managing risk are charged with making faster, more accurate decisions with fragmented data.

In this increasingly demanding landscape, AI solutions can analyze large datasets quickly, apply consistent methodologies and uncover insights that otherwise would have gone overlooked. In addition to expediency, such tools can yield cost containment, pricing transparency and deeper customization. Unsurprisingly, then, 75% of executives polled in a recent Roots Automation survey deemed AI tools key to premium growth; more than half reported that AI accelerates the quoting process, and nearly half are using AI solutions to help reduce loss ratios.

However, fully realizing the risk management benefits of AI solutions means bringing them out of their single-use silos. The underlying principle is simple: since AI excels at mining and measuring multiple factors with exceptional speed… why limit the data it analyzes? After all, the more factors group health policies can consider, the more accurate, resilient and cost-effective they will inevitably be.

This article explores how group health insurers can optimize AI usage and maximize its game-changing effect. This can only be achieved when AI-powered solutions are integrated into enterprise-level workflows to facilitate consistent, data-driven insights throughout policy lifecycles.

The AI Silo Trap

Anyone of a certain age will remember the big, boxy desktop computers of the 1980s and early 1990s. As the PC revolution took off, so did the ease and speed at which once-onerous tasks could be performed. Everything from word processing to number crunching became a lot easier in a hurry. It was useful, impressive and altogether helpful.

And it was nothing compared with what came next: networking.

Once computers were linked to each other via the World Wide Web, their applications and usefulness were exponentially amplified. Knowledge could be collected more broadly; trends and the opportunities they uncovered could be noticed and acted upon more quickly. The tagline of the day may have been "You've Got Mail," but the force that drove the internet's rapid proliferation was that, suddenly and forevermore, our newly connected computers provided access to far more knowledge than any one PC could offer. Knowledge shared was knowledge gained.

Fast forward to today, and artificial intelligence is emerging from its nascent, newfangled days into the biggest buzzword on the planet, let alone the insurance industry. And like the pre-internet days of PCs, the potential benefits of today's early-stage AI solutions – let's call them AI 1.0 – are being underused largely for lack of connectivity.

In this still-siloed landscape, many organizations that design and manage group health policy lifecycles rely on one set of AI tools and methodologies for assessing risk in new business, another for existing client renewals, and yet another for managing member health risk. Still, the results have been undeniably encouraging: with growing volumes of consumer data available from medical, prescription, and lab sources, even rudimentary AI solutions are making crisper, more confident decisions that go beyond the limits of personal judgment and historical patterns.

Unfortunately, these benefits have led to blind spots. AI solutions have proven so promising that most organizations have overlooked the logical next step: connection.

While AI solutions in and of themselves are exceptional inventions, their effect is limited when constrained to single-set columns. Among other pitfalls, this approach may lead to disconnected data and a potential inability to account for shifts in group risk profiles.

In an environment as multifactored and ever shifting as group health insurance policy lifecycle management, the time has come for AI solutions to take the natural next step in their evolution. Insurance players are well-advised to move from standalone AI processes to enterprise-level, full-cycle workflows that align risk management across new business acquisition, population health management, and existing group renewals.

Better Together: Connected AI Solutions

Much like the dawn of high-speed internet in the late 1990s, group health's fledgling "AI 2.0" era promises unprecedented advantages. Opportunities now exist to transcend segmented AI tools by implementing sweeping AI solutions that provide truly integrated lifecycle risk management. Simply put, such solutions replace several stagnant tools that each examine one aspect of policymaking with one versatile solution that monitors all aspects.

When AI solutions are properly integrated across the myriad datasets inherent in group health, they can maintain tightly controlled continuity across policy lifecycles. First and foremost, connectivity breeds data consistency, which in turn supports enhanced decision-making while providing actionable, member-level insights to power care management solutions.

By rooting decisions in the same risk logic across initial quoting, renewal pricing and continuing population management, this un-siloed, unshackled approach enables end-to-end application of shared data signals and risk methodologies. The result is reduced variability and improved portfolio performance.

Like the internet before it, such solutions thrive on one overarching principle: knowledge shared is knowledge gained. Faster quote turnaround times reduce underwriting lifecycle friction, and unified historical and real-time data inform seamless renewal transitions. With group health's countless footnotes and fine print suddenly on the same, succinct page, the resulting reliable benchmarks optimize underwriting strategies through the newfound ability to measure performance and identify improvement opportunities at each stage of a group's lifecycle.

Of course, any transition can bring challenges – including, for starters, determining precisely how to begin. At the inception of the enterprise-level AI integration journey, insurance organizations should carefully consider their key priorities. At the heart of this introspection is one question: what does interconnectivity-driven success look like?

What challenges are the organization's policy lifecycle management experiencing because, for example, its new business and renewal underwriting tools are separate? Which workflows or decisions would benefit most from shared data and consistent risk scoring? What internal systems or processes will need to connect with the new platform? And of course, how will we train and properly prepare our workforce for this next-generation solution?

In many cases, these considerations mirror patterns seen across the broader market. Let's close with a few examples showcasing the value of AI solution synchronization.

Use Case #1: Detecting New and Emerging Health Risks During a Policy Term

Based on the original census, an employer group appears healthy during initial policy quoting. Of course, several factors can affect this risk assessment, including final member enrollment and the entrance of additional members during the policy's lifecycle. To better account for these factors, an integrated risk scores solution can provide supplemental data that informs pricing and cost containment strategies at renewal.

Integrated risk score solutions can be especially valuable to companies with high member turnover, or that have newer groups with limited experience. The goal is to supplement a group's limited experience-based risk scores with models that mine third-party datasets.

Use Case #2: Consistent Risk Scoring for Refined Pricing Accuracy

Using different tools for new and renewal underwriting can result in inconsistent risk assessments and pricing. An integrated approach uses the same underlying data signals and modeling logic across both business phases.

Such consistency supports fairer and more accurate pricing, reduces volatility in rate changes, and strengthens relationships with employer groups that expect predictability. When the same factors drive decisions from quoting to renewal, underwriting teams can take different actions based on risk scores, explain pricing shifts more clearly, and maintain trust.

Use Case #3: Improving Underwriter Efficiency and Speed

When new and renewal underwriting data resides in separate systems, underwriters may spend extra time reconciling information or duplicating analysis. With an integrated workflow, teams gain access to a consolidated view of group data, historical insights, and predictive signals in one place. This eliminates repetitive work, speeding up both the quoting and renewal processes. As a bonus, faster decisions mean insurers can respond more expediently to broker and employer requests.

March Jeffreys, General Manager, Health , Gradient AI

His 30-year career includes extensive experience in the health insurance and financial services segments.

This article first appeared on Insurance Thought Leadership .

Reinventing Group Health Risk Management: Why AI + Integration Are Now Essential

Thu, 11 Dec 2025 21:47:36 GMT

GROUP HEALTH INSIGHTS BLOG | Nov 13, 2023

The Gradient AI Team

Group health insurers are being pushed harder than ever to deliver speed, accuracy, and value, often while working with fragmented data and highly variable claim patterns. Underwriters are expected to answer tougher questions, in less time, with greater precision.

The old ways of evaluating risk simply can’t keep up.

Today’s Pressures Are Reshaping Group Health Underwriting

Insurers face a growing set of challenges, including:

Rising and unpredictable healthcare costs
Volatile claim behavior
Rapidly shifting member health risks
Inconsistent or incomplete datasets

At the same time, employer groups expect more:

Faster responses
Greater pricing transparency
Tailored coverage options
Stronger cost-containment strategies

These pressures magnify the limitations of traditional underwriting and expose gaps that slow teams down—or create unnecessary risk.

“Underwriting accuracy is no longer just a competitive advantage.
It’s a growth imperative.”

AI Helps Underwriting Teams See What They Couldn’t See Before

Artificial intelligence and machine learning give insurers the ability to operate with far greater clarity—even when data is fragmented.

AI enables teams to:

Surface early indicators of potential high-cost claimants
Spot unusual patterns in medical, Rx, and lab data
Predict cost drivers with far better accuracy
Score groups consistently across thousands of quotes
Redeploy expertise to complex or high-value cases

AI doesn’t replace the underwriter. It amplifies their judgment and reduces reliance on assumptions.

But AI Alone Isn’t Enough. Integration Is the Real Game Changer

Many insurers already use AI in some form. But when quoting, renewals, and population insights sit in separate systems , insurers still operate with blind spots.

An integrated risk-management life cycle solution ensures that every stage—new business, renewal, and ongoing population monitoring—feeds into the next.

With a unified platform, insurers gain:

A single, connected source of truth
Shared risk scoring across quoting and renewals
Better renewal positioning with year-round visibility
Reduced underwriting leakage
More consistent decisions across teams and regions
A continuously learning system

This connected approach turns AI insights into a repeatable, scalable strategy for profitable growth.

“AI delivers insight. Integration delivers transformation.”

Key Questions to Ask Before Moving Toward Integration

To prepare for an integrated life cycle model, insurers should consider:

Where are we losing efficiency because systems don’t talk to each other?
Which underwriting workflows feel the most strained today?
How would shared scoring improve accuracy and speed?
What data sources need to connect with the new platform?
How will we define success across new business, renewals, and account management?

These questions help build alignment and set a clear roadmap.

Start Small. Gain Confidence. Then Scale

Shifting to an integrated model doesn’t need to happen all at once. Most insurers begin with a targeted pilot, a specific product line, region, or block of business.

A pilot allows teams to:

Validate results in a controlled environment
Build trust in the insights
Identify process improvements
Scale adoption with clarity and confidence

Once early wins are confirmed, the integration expands—powered by proven value and team buy-in.

The Future of Group Health Underwriting Is Connected

As the market grows more complex, insurers who combine AI with a fully integrated risk-management life cycle will outperform those relying on siloed tools or manual processes.

Integration eliminates blind spots, strengthens renewal strategy, and helps insurers grow profitably—even in a volatile environment.

Ready to Build Your Integrated Risk-Management Life Cycle?

Discover how Gradient AI helps insurers connect new business, renewals, and population insights into one unified platform.

Schedule a Demo →

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Explore the benefits of Gradient AI's Group Health Solutions.

5 Questions with Brook Rosenbaum, General Manager, P&C

Tue, 02 Dec 2025 23:19:17 GMT

P&C INSIGHTS BLOG | Feb 26, 2024

The Gradient AI Team

Brook joined Gradient AI to lead strategy and execution for Gradient’s Property and casualty segment. He brings more than 12 years’ experience in the P&C insurance industry. Brook has held leadership positions in product management and development, digital transformation, and go-to-market strategy in Insurtech and large carriers.

Most recently, Brook served as Vice President at American Family Insurance, where he led expansion into new markets and spearheaded the company’s loT/connect home strategy.

Prior to moving into the insurance industry, Brook was a practicing attorney and worked as an Engagement Manager at McKinsey & Co.

Question 1: What were some key factors which led you to pursue a role with Gradient AI?

I have spent much of my career working at large companies and institutions – a law firm, a state government, a consultancy, and a multiline insurance carrier – and I have learned a lot along the way. I also worked at a startup insurance company for several years. The experience of working in that startup environment was exilerating and fulfilling in ways that my time at larger companies was not.

Gradient AI’s size and the company’s ambition to grow quickly were incredibly attractive to me. When I met the leadership team, I was struck with a consistent positive energy. We spend a large portion of our lives with our professional colleagues, and I prefer to work alongside people who challenge me to do my best every day and who I can trust will be tireless in their pursuit of our shared goals. Gradient AI provided an opportunity for me to continue my professional growth and to contribute to a team that is poised to achieve great success.

Question 2: What is the primary focus of your role as General Manager, P&C and how does it impact our clients?

My goal is to develop and articulate a vision and strategy for how Gradient AI can create value for stakeholders in the P&C industry. This means:

offering solutions that meet the needs of our customers today and anticipate and address the challenges they will face in the future.
taking the time to understand their business, their unique workflows and their goals.
helping them achieve their objectives by giving them the tools and support they need to profitably serve their customers.

We want to be more than just be a vendor to our customers, we strive to be their trusted partner.

Question 3: What are the biggest challenges facing P&C insurers today and how can they address them through AI?

The P&C industry has traditionally been slow to innovate and legacy insurers have allowed their tech infrastructure to age passed its ability to accommodate technological advancements.

The industry is also a heavily regulated one and over decades it has returned a reasonable return for owners and investors. This has created a relatively consistent risk aversion and resistance to change among the large, established players in the industry. Now however, we are at a convergence of the arrival of big data analytics, industry profitability challenges, and the emergence of automation technologies.

This has created an opportunity that is causing some companies to shake off their resistance to change and giving rise to successful startups both in the carrier/MGA space as well as in the Insurtech ecosystem of companies who want to provide the picks and shovels to help unlock the opportunities to capitalize on these new technologies.

Going forward, the biggest challenges for insurers will be to prioritize and execute an outline that will allow them to compete in this industry in the years to come.

Question 4: What are common misconceptions about the use of AI in the P&C industry?

Whenever a new technological innovation hits the mainstream, there is a tendency for companies and leaders to raise expectations for the innovation to lead to immediate bottom-line benefits. And while it is true that new data sources, new computational approaches, new automations, and new go-to-market modalities have created huge value for the industry, most of the time this did not happen overnight, nor was it necessarily a straight line between the initial innovation and commercial success.

The emergence of “AI” is the latest in a long line of innovations that have great promise. Undoubtedly AI-driven predictive analytics, generative AI, and agentic AI will create value in P&C insurance . But AI, by itself, is not a panacea. Realizing the financial benefits of AI will require investment in talent and infrasture, a willingness to experiment, trial and error, and strategic decisions that will take years to unfold.

Question 5: What do you like to do in your free time?

For the past several years, I have spent a lot of my free time coaching youth sports. My son and daughters have played soccer, baseball, basketball, and softball with me as their coach. But my youngest is about to age out of youth sports, so this chapter in my life will soon be coming to an end. I expect that I will fill that vacuum with other things I enjoy – cooking, golf, and other sports where I mainly try to avoid injury!

Bonus Question: What is a bucket list travel spot for you and why?

New Zealand – I have always loved pictures of the landscapes in New Zealand. And I am fascinated by the opportunity to ski down a mountain and go to a beach on the same day.

To stay up to date on the P&C market and AI’s evolving impact in the industry be sure to visit our website for insightful white papers, thought-provoking webinar discussions, health blogs and more! Look for upcoming 5-question interviews to learn more about our talented and dedicated team and how they support better outcomes and the success of our clients.

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From Data Silos to Smart Systems: 5 Use Cases for an Integrated AI Strategy for Group Health Carriers

Thu, 13 Nov 2025 21:44:10 GMT

GROUP HEALTH INSIGHTS BLOG | Nov 13, 2023

The Gradient AI Team

The Shifting Landscape of Group Health Risk

Managing risk in today’s group health market has become more complex than ever. Insurers are contending with rising medical costs, evolving care utilization patterns, and heightened client expectations for cost containment and transparency. Traditional underwriting methods—often reliant on fragmented data and manual analysis—are struggling to keep pace with the need for faster, more precise decisions.

This is where artificial intelligence (AI) is rapidly reshaping the game. When properly integrated into underwriting and risk workflows, AI delivers the ability to:

Analyze vast datasets
Apply consistent models
Reveal insights that elevate both speed and accuracy.

Yet, achieving these benefits requires more than plugging in a new algorithm—it demands end-to-end integration across the group health lifecycle.

The Power of Integrated Workflows

Siloed systems create blind spots. To capture AI’s full potential, carriers must connect processes across new business quoting, population health management, and renewal cycles . Integrated workflows ensure that insights gathered in one stage inform decisions in another—creating a continuous feedback loop for smarter pricing and better results.

Here are five real-world use cases demonstrating how integration changes the risk management equation:

1. Detecting Emerging Health Risks in Real Time

A group that looks healthy during quoting may not stay that way. Member turnover and midyear enrollment changes can shift the overall risk profile. Integrated AI-driven risk scoring provides additional insights with supplemental data to inform pricing and cost containment strategies at renewal. Underwriters gain updated insights based on third-party data refreshed biweekly—rather than waiting for lagging claims reports.

This allows carriers to:

Adjust strategies mid-cycle
Price renewals more accurately
Anticipate cost pressures before they escalate

Not only can an integrated solution provide access information on newer members and groups with little to no claims experience, but it also indicates potentially earlier disease signals.

2. Creating Consistent Risk Scoring from Quote to Renewal

When quoting and renewal teams rely on different models or data inputs, inconsistencies in risk scoring—and pricing—inevitably follow. A unified approach applies the same methodology across all business stages.

The outcome of this consistency?

Supports fairer and more accurate pricing
Reduces volatility in rate changes
Strengthens relationships with employer groups that expect predictability.

When the same factors drive decisions from quoting to renewal, underwriting teams can take different actions based on risk scores, explain pricing shifts more clearly, and maintain trust.

3. Accelerating Underwriter Productivity

Disjointed systems slow everything down. Integrated data views allow underwriters to see group history, predictive risk scores, and member trends all in one dashboard.

The result?

Fewer manual reconciliations
Reduced rework
Faster quoting cycles.

Speed doesn’t just improve efficiency—it enhances competitiveness in a fast-moving market. And faster decisions mean insurers can respond more quickly to broker and employer requests.

4. Finding Growth and Retention Opportunities

An integrated life cycle solution enables insurers to view the entire book of business and identify groups where targeted strategies can deliver value. An integrated analytics platform doesn’t just flag risk— it also uncovers opportunity .

For instance, data from new business quotes, combined with historical results, provides performance trends across the entire book of business . Carriers can pinpoint employer groups showing improving risk profiles. These accounts may warrant competitive renewal pricing or upsell conversations for expanded coverage, fueling profitable growth .

5. Turning Member-Level Data into Action

Continuous ingestion of member-level data powers proactive care management. This data fuels the AI models used to assess risk at the group level, while also providing deep insights at the member level to inform care management solutions , which are a cornerstone of cost containment strategies.

AI models detect patterns that may:

Signal emerging chronic conditions
Help carriers and employer groups take preventive action
Improve population health outcomes while keeping claims spend in check.

The Bottom Line

These five use cases demonstrate why transitioning from separate AI tools to an integrated approach can yield substantial benefits. Integrating workflows ensures:

Continuity of data
Consistency of risk assessment
Greater agility for the organization

This fully integrated lifecycle approach creates measurable gains in precision, efficiency, and profitability. In today’s competitive group health environment, integration isn’t just an advantage—it’s a necessity.

The carriers who win tomorrow will be those who unify their data ecosystems today. Integrated AI doesn’t just automate tasks—it creates a shared intelligence across the organization, empowering underwriters, actuaries, and sales teams to collaborate around a single source of truth.

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5 Questions with Marc Jeffreys, General Manager, Health

Thu, 13 Nov 2025 17:20:49 GMT

GROUP HEALTH INSIGHTS BLOG | Nov 13, 2023

The Gradient AI Team

Marc is a results-driven executive with comprehensive experience in the financial services and health insurance industries. Marc brings his depth of experience and industry knowledge in his role as General Manager, Health at Gradient AI. He is accountable for all facets of the health business, including setting the strategic direction, defining solution to meet customers’ needs, and delivering financial performance.

Over his 30-year career, Marc has held leadership roles in sales, marketing, operations, product and strategy which prepared him to lead businesses. Prior to joining Gradient AI, Marc was President of Supplemental Health Solutions at Cigna. He was instrumental in building and growing the business from $25M to over $400M in eight years and introducing industry-leading claim integration capabilities.

Question 1: What were some key factors which led you to pursue a role with Gradient AI?

There were several factors that influenced my decision to join Gradient.

I’ve always gravitated toward new opportunities that allow me to leverage past experiences while being challenged to learn new things. And I’m fascinated by disruptive innovation particularly as it relates to leveraging data.

After almost a decade focused on the supplemental health market, I found myself inching for a new challenge. I was particularly intrigued by the advancements in AI and its potential as a disruptor in healthcare.

The General Manager role here at Gradient brought it all together for me.

Question 2: What is the primary focus of your role as General Manager, Health and how does it impact our clients?

I’m responsible for setting the strategic direction of the health business, which in large part includes continuing to build and deliver innovative solutions that create value for our customers. I’m fortunate to be building on a solid foundation established by our industry leading new business solution- SAIL. The foundation includes deep technical and industry expertise that together position Gradient to continue to lead innovation in risk management solutions.

Question 3: What are the biggest challenges facing insurers today and how can they address them through AI?

The healthcare ecosystem is vast and fragmented in many ways. While many would argue the United States of America has the best healthcare in the world, many would agree it is also one of the least efficient and costly.

It is incredibly challenging for Payers to navigate the complexities while trying to address affordability issues, deliver patient outcomes and meet the expectations of key stakeholders.

AI is a key enabler for business transformation that’s necessary to compete in today’s market. There are many use cases for AI, but at its core I think of AI as a “force multiplier.” Implemented correctly, AI can help Payers’ scale efficiently, manage a profitable book of business and deliver the superior experiences and outcomes for their patients.

Question 4: Are insurers limiting AI capabilities by using it primarily for driving operating efficiencies?

Managing operating expense is certainly a critical area of focus for Payers, so I’m not surprised by the emphasis on AI uses cases that space. However, many organizations also recognize the value of introducing AI models into the risk management life cycle of their customers whether that’s new business underwriting, renewal assessment or population health. In fact, it’s the organizations who are thinking holistically about AI applications across their business that are best positioned for success.

Question 5: What do you like to do in your free time?

I’m a father of four. My youngest, twin boys, just left for college this fall. So, right now I’m just enjoying the slower pace of an empty nest. I’m sure that will wear off soon and I’ll miss the hustle and bustle of never-ending kid activities.

I’m also a huge college basketball fan… a UConn fan specifically… I’m a UConn grad, live 3 miles from the UConn campus and have one kid attending UConn now. The start of basketball season is a special time for me. ��

Bonus Question: What is your favorite state to visit and why?

I grew up as a military brat, so I’ve had the pleasure of living in many states and countries. I’ve found something to love about everywhere I’ve been. The most unique place which I lived was Midway Island (Atoll). I was only a toddler when we lived there but I have vivid memories of the island being overrun by Gooney birds (Laysan albatrosses) during mating season. The island seemed enormous to me at the time, but in reality, the entire island was only one mile wide by two miles long. Check it out on Google Maps sometime.

To stay up to date on the health market and AI’s evolving impact in the industry be sure to visit our website for insightful white papers, thought-provoking webinar discussions, health blogs and more! Look for upcoming 5-question interviews to learn more about our talented and dedicated team and how they support better outcomes and the success of our clients.

Stay on top of AI trends by subscribing to Advanced Insights , the newsletter for strategies, ideas, and insights on AI in insurance. Delivered monthly to your inbox. Subscribe Now →

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Gradient AI Partners with Connexure to Streamline Risk Management Workflows for Carriers and MGUs

Thu, 09 Oct 2025 13:51:55 GMT

Health Insurer Adopts Gradient AI’s SAIL and New Renewal Analytics Solutions to Improve Underwriting Precision, Boost Operating Efficiencies and Strengthen Risk Management

Boston, MA - October 9, 2025 – Gradient AI, a leading enterprise software provider of artificial intelligence (AI) solutions in the insurance industry, today announced a partnership with Connexure, a leading provider of quoting, underwriting and administrative software for the self-funded market.

The collaboration combines Gradient AI’s advanced Risk Management Life Cycle solution with Connexure’s ESLoffice platform, an integrated workflow management system designed specifically for the unique needs of stop loss insurance carriers and underwriters. Together they strengthen risk management capabilities and empower organizations to increase speed and enhance workflows, enhancing customer experience.

Connexure, an Atlanta-based innovator in vertical software solutions, chose to partner with Gradient AI to advance workflow automation by integrating its intuitive, automated platforms with Gradient AI’s predictive analytics. This partnership is designed to reduce operational friction and enhance efficiency by consolidating data and insights into a single, seamless experience.

“Our partnership with Gradient AI aligns seamlessly with our mission to unify the self-funded ecosystem through system interoperability,” said Connexure Chief Revenue Officer Chris White. “The two organizations are uniquely positioned to drive better outcomes with the pairing of Connexure’s underwriting and administration workbench automation, with cutting-edge, AI-driven analytics from Gradient AI. Based on feedback from our clients, it’s clear that this partnership fills a big need in the market right now.”

For existing customers using both platforms, the partnership provides a more integrated workflow, enabling faster, more informed decision making and improved outcomes.

“We are excited to collaborate with Connexure to bring the joint power of seamless, end-to-end workflow and dynamic AI-driven predictive analytics and insights together to our customers,” said Stan Smith, founder and CEO of Gradient AI. “We are bringing together proven technologies to solve one of the industry’s biggest challenges. The synergy between intelligent automation and predictive analytics positions us to drive real value and better outcomes.”

About Connexure

Connexure, based in Atlanta, GA, is the market leader in providing software solutions to the self-funded marketplace. Connexure’s mission is to unify the self-funded medical ecosystem through integrated technologies, processes, and data insights. With a focus on developing industry-shaping vertical software, Connexure is uniquely positioned to simplify the lives of those in the self-funded space. To learn more, please visit https://connexure.co/ or follow Connexure on LinkedIn .

About Gradient AI

Gradient AI is a leading provider of proven artificial intelligence (AI) solutions for the insurance industry. Its full-cycle platform improves loss ratios and profitability by predicting underwriting and claim risks with greater accuracy, as well as reducing quote turnaround times and claim expenses through intelligent automation. Unlike other solutions that use a limited claims and underwriting dataset, Gradient AI’s software-as-a-service (SaaS) platform leverages a vast industry data lake comprising tens of millions of policies and claims. It also incorporates numerous other features including economic, health, geographic, and demographic information. Customers include some of the most recognized insurance carriers, MGAs, MGUs, TPAs, risk pools, PEOs, and large self-insured employers across all major lines of insurance. By using Gradient AI's solutions, insurers of all types achieve a better return on risk. To learn more about Gradient AI, visit: https://www.gradientai.com or follow on LinkedIn .

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6 Key Questions Every Carrier Should Ask About AI’s Impact on Group Health Underwriting

Wed, 01 Oct 2025 22:09:32 GMT

GROUP HEALTH INSIGHTS BLOG | Nov 13, 2023

The Gradient AI Team

If you're still manually reviewing thousands of census records and medical questionnaires, you're likely leaving money on the table and losing competitive ground to carriers who've already made the shift to AI-powered underwriting.

The question isn't whether AI will transform group health underwriting. It's whether your organization will lead that transformation or scramble to catch up. To help you determine your current position, here are six questions every carrier should be asking right now .

1. What's Really Driving AI Adoption in Group Health Underwriting?

Let's start with the obvious: efficiency. Industry practitioners report that AI can reduce underwriting time from days to hours, or even minutes, for more minor cases. But speed is just the beginning.

The real drivers go deeper:

Labor market pressures: Finding experienced underwriters is harder than ever. AI helps newer team members perform at higher levels more quickly, while freeing up your senior talent for complex cases that truly require human judgment.
Consistency at scale: Human underwriters have bad days. They get tired. They interpret guidelines differently. AI applies the same logic to every case, every time, which means fewer errors and more defensible decisions.
Risk selection accuracy: Modern AI models can identify patterns in data that humans cannot see. This leads to more accurate risk assessment and precise pricing, especially for mid-market and large group cases.

The carriers seeing the most significant wins aren't just using AI to do the same old processes faster. They're rethinking their entire underwriting workflow.

2. What are the Real Implementation Challenges?

Here's what nobody tells you about AI adoption: the technology is often the easy part. The hard part is everything else.

Data quality issues top the list. AI models are only as good as the data you feed them. If your historical underwriting data is messy, inconsistent, or siloed across multiple systems, you'll need to clean it up first.

Change management can be another challenge. Your underwriters and other team members didn't sign up to become AI trainers. Expect resistance, especially from experienced team members who view AI as a threat rather than a tool. You'll need:

Clear communication about how AI will augment their work, not replace it
Training programs that make sense for people who aren't data scientists
Quick wins that demonstrate value early in the process
Ongoing feedback loops so all team members feel heard

Integration with legacy systems can present technical challenges. Most carriers are working with decades-old infrastructure. Getting AI tools to play nicely with existing workflows requires careful planning and often some custom development work.

The carriers who succeed treat AI implementation as an organizational transformation, not just an IT project.

3. How Does AI Actually Impact Different Parts of the Underwriting Process?

AI doesn't affect every underwriting task equally. Understanding where it adds the most value helps you prioritize your investment.

Census analysis and data validation see immediate impact. AI can process thousands of employee records in seconds, flagging anomalies, identifying missing information, and calculating risk factors automatically.

Medical underwriting benefits from AI's pattern recognition capabilities. Models trained on historical data can:

Assess medical questionnaire responses
Identify high-risk conditions or combinations
Flag cases that need human review
Suggest appropriate rate-ups or exclusions

Renewal analysis becomes more sophisticated. AI can analyze claim patterns, predict future utilization, and recommend pricing adjustments based on multiple factors simultaneously.

Quote generation and pricing get faster and more accurate. Once AI has assessed the risk, it can automatically generate quotes within established parameters, freeing underwriters to focus on negotiations and client relationships.

4. What Results Are Carriers Actually Seeing?

Carriers using AI are seeing real, measurable results. Not "someday" ROI, but improvements you can track quarter over quarter.

Accuracy and consistency improve across the board . One practitioner reported significantly fewer pricing errors and more consistent application of underwriting guidelines after AI implementation.
Underwriter productivity increases without requiring additional headcount. Teams can handle higher case volumes, and newer underwriters can work more independently sooner.
Resource allocation gets smarter. With AI handling routine cases, carriers can deploy senior talent where it makes the most significant difference. This improves both efficiency and job satisfaction.
The financial impact varies by carrier size and implementation approach, but practitioners report ROI within 12-18 months for most use cases.

5. How Do You Actually Manage the Organizational Change Around Implementing AI?

You can have the best technology in the world, but if your team members won't use it, you've got nothing. Start with your champions. Find the underwriters who are curious about technology and willing to experiment. Let them test the tools first, provide feedback, and become internal advocates. Their peer endorsement matters more than any executive mandate.

In addition:

Make the "what's in it for me" crystal clear . Underwriters need to understand that AI:
Eliminates the boring, repetitive parts of their job
Makes them more productive and valuable
Isn't trying to replace them

Provide real training , not just software demos. Your team needs to understand not just how to use AI tools, but when to trust them and when to override them. This requires hands-on practice with real cases.
Implement feedback mechanisms that allow underwriters to flag when AI makes questionable recommendations. This serves two purposes: it improves the models over time, and it gives your team a sense of control and partnership in the process.
Measure and communicate wins regularly. Share specific examples of how AI helped close a deal faster, caught an error, or improved pricing accuracy. Make the benefits visible and tangible.

Cultural change takes time. Expect a 6-to 12-month adjustment period before AI becomes fully integrated into your workflows.

6. What's Next for AI in Group Health Underwriting?

The AI capabilities available today are just the beginning. Here's what's coming next and how forward-thinking carriers are preparing.

Predictive analytics will get more sophisticated . Future models won't just assess current risk. They'll predict how a group's health profile and costs will evolve over time, enabling more strategic pricing and risk management.
Natural language processing will transform how underwriters interact with data. Instead of running reports, you'll ask questions in plain English and get instant answers. This makes AI accessible to everyone.
Integration across the insurance value chain will deepen . AI won't stop at underwriting. It will connect to claims data, broker portals, client management systems, and enrollment platforms, creating a seamless flow of information.
Personalization at scale becomes possible . AI can help carriers tailor products, pricing, and services to specific industry segments or employer needs in ways that would be impossible manually.

The Bottom Line

AI in group health underwriting is now a present-day reality that's already reshaping competitive dynamics in the market. The carriers who thrive will be the ones who successfully combine AI capabilities with human expertise, creating underwriting operations that are faster, more accurate, and more scalable than ever before.

If you haven't started your AI journey yet, you're behind. But you're not too late. The key is to start now, learn quickly, and build the organizational capabilities to adapt as the technology continues to mature.

What question will you tackle first?

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The Next Frontier in Workers’ Comp: Agentic AI Built on Predictive Models

Wed, 01 Oct 2025 22:06:44 GMT

P&C INSIGHTS BLOG | Feb 26, 2024

The Gradient AI Team

In Workers’ Compensation insurance , predictive models have been a powerful tool for years. They help insurers better understand risk, price policies more accurately, and intervene earlier in claims that could become more complex or costly.

But what comes next?

The answer is agentic AI. This technology moves beyond predictions and takes meaningful actions . In a market like Workers’ Comp, where accuracy and timing are critical, this shift could transform the way insurers support injured workers and manage claims.

What Are Predictive Models in Workers’ Comp Insurance?

At a basic level, predictive models are systems trained to analyze historical data and estimate what is likely to happen in the future. These systems don’t know the future, but they recognize patterns from the past that can help forecast outcomes.

With Workers’ Comp AI solutions like Gradient AI’s, predictive models help insurers answer questions like:

How likely is it that a worker in a specific job will get injured?
If an injury occurs, how long might recovery take?
What interventions would help the injured worker recover?

These models use data from a variety of sources, including worker demographics, job descriptions, injury types, medical records, and treatment timelines. They help insurers understand how to price policies, manage risk , spot issues early on, and where to prioritize resources.

For instance, if a model shows that a certain type of injury often results in extended time away from work, the system can raise a flag. That gives the insurer a chance to step in early. Still, the decision to act often rests with a human.

What Is Agentic AI?

Agentic AI goes a step further. Instead of just identifying patterns or risks, it acts on them. It doesn’t just highlight that something might happen. It takes the next steps on behalf of the human insurance team member, often automatically and within predefined rules.

Think of the difference this way:

A predictive model acts like a smart advisor. It provides insights and forecasts.
Agentic AI behaves more like a reliable assistant. It acts based on those insights.

These models are not meant to replace people. They are designed to work in conjunction with them . Agentic AI can handle time-sensitive tasks, follow established workflows, and reduce the need for manual input. That allows adjusters to focus on the claims that require their expertise.

How Predictive Models Lead to Agentic AI

Agentic AI builds on the foundation of predictive models. The evolution from prediction to action typically happens in a few stages:

Pattern recognition: Predictive models are trained to recognize patterns in historical claims data.
Automation: Workflows are set up to respond to certain predictions, such as flagging a claim or assigning a risk score.
Decision logic: Business rules are added so the workflows can make conditional choices and recommend steps.
Autonomous action: The technology gains the ability to act on its own, such as triggering notifications, sending messages, or initiating follow-ups.

None of this eliminates the need for human review. What it does is to make sure that the most urgent or high-risk cases get the attention they need without delay.

A Practical Example in Workers’ Comp Insurance

Here’s how this might play out in the real world.

Example predictive model output: “This injured worker is at high risk of delayed recovery.”

That’s valuable information. But without further action, it may sit in a report or require someone to notice it manually.

With agentic AI:

The claims technology platform flags the case in real time
A case manager is alerted immediately
A draft message is prepared for outreach
Next steps are recommended, such as scheduling a follow-up or sharing return-to-work resources

This all happens shortly after the claim is filed or updated. It removes friction and ensures that action occurs when it can make the most impact.

Why It Matters

Workers’ Comp insurance involves real people and complex situations. Delays or missed signals can lead to longer recovery times, higher claim costs, and frustration for all involved. Predictive models help identify potential areas of concern. Then, agentic AI helps respond to them quickly and consistently.

Key benefits include:

Earlier intervention: Claims with high risk can be addressed faster, improving outcomes and controlling costs.
Greater consistency: Rules and logic are applied the same way every time, regardless of individual workloads or experience.
Reduced manual work: Tasks such as flagging, routing, and messaging can be automated.
More personalized care: Injured workers receive more timely support based on their specific needs and risk profiles.

Conclusion: What’s Next in Workers’ Comp?

For Workers’ Comp insurers, agentic AI is a logical next step. It brings together the predictive power of data with the ability to act on it immediately and intelligently.

Agentic AI is not about removing people from the process. It is about giving them better tools. It enables claims professionals to be more strategic. It ensures best practices are applied more reliably. And it provides injured workers with the timely attention they deserve.

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Community Health Options Selects Gradient AI to Accelerate Group Health Underwriting and Renewals

Tue, 23 Sep 2025 22:11:33 GMT

Health Insurer Adopts Gradient AI’s SAIL and New Renewal Analytics Solutions to Improve Underwriting Precision, Boost Operating Efficiencies and Strengthen Risk Management

Boston, MA-September 23, 2025 – Gradient AI, a leading enterprise software provider of artificial intelligence (AI) solutions in the insurance industry, today announced that Community Health Options has successfully adopted its integrated Risk Management Life Cycle solution .

The platform combines SAIL™, a predictive analytics solution for new business underwriting, with Renewal Analytics, a new solution for managing risk in existing business. Together they empower organizations to manage the full risk lifecycle of group health clients. The solution improves underwriting precision, boosts operating efficiencies and supports overall risk management.

Community Health Options, a Maine‐based nonprofit health insurer and plan administrator, selected Gradient AI to support its growth strategy and deliver more accurate risk assessments for both new and renewing business.

By using Gradient AI’s solution at both initial underwriting and at renewal, Community Health Options gains the benefits of a full-cycle risk analysis solution that provides a consistent and unified underwriting framework throughout the policy lifecycle. This helps reduce variability, improve quoting accuracy and support long-term financial performance.

“We needed a platform that could fill gaps in data and provide a clearer picture of risk for new business, while also offering greater insights to help proactively manage our existing book,” said Kevin Lewis, CEO, Community Health Options. “The Gradient AI platform enables our teams to work more efficiently and provide more accurate predictions on member and group risk. Ultimately, it supports our strategic goal of providing a better product to our customers, which supports our growth strategy.”

In addition to the platform’s precision and productivity, Community Health Options chose Gradient AI for its consultative approach and understanding of the health plan’s unique goals and challenges.

“Community Health Options is taking a forward-thinking approach to managing risk across the policy lifecycle,” said Stan Smith, founder and CEO of Gradient AI. “By using data-driven insights at both the underwriting and renewal stages, they are strengthening performance and delivering greater value to their members. We worked closely with their team to ensure our solutions align with their goals and support their long-term strategy.”

For more details on Gradient AI’s Risk Management Lifecycle solution, see the white paper, AI-Powered Integrated Risk Management Lifecycle Solution: The Future of Group Health Insurance .

About Community Health Options

Community Health Options is the only Maine‐based, nonprofit, Member‐led health insurer and plan administrator, providing comprehensive health plans for individuals, families and businesses. The company offers the most robust provider network in Maine, featuring the Community Health Options network, which includes providers in New Hampshire and all Centers of Excellence in Massachusetts, along with plans offering U.S. national coverage. To learn more, please visit https://www.healthoptions.org or follow Community Health Options on Facebook , LinkedIn , TikTok or Instagram .

About Gradient AI

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Gradient AI Launches Risk Management Life Cycle Solution for Group Health

Wed, 10 Sep 2025 22:35:10 GMT

New Platform Combines SAIL and Renewal Analytics to Deliver Predictive Insights Across the Policy Life Cycle

Boston, MA-September 10, 2025 – Gradient AI, a leading enterprise software provider of artificial intelligence (AI) solutions in the insurance industry, today announced the release of its enhanced group health AI-powered Integrated Risk Management Life Cycle solution . The platform integrates Gradient AI’s SAIL™ for new business underwriting with its Renewal Analytics for existing clients, creating a unified, full-cycle underwriting and risk management solution for group health insurers.

The solution unifies the quoting and renewal processes by applying the same AI modeling methodologies across both, creating more reliable risk assessments. It ensures data consistency and provides actionable member-level insights to support care management. Together these capabilities reduce variability, improve pricing precision and strengthen overall portfolio performance. By applying a consistent underwriting framework, the platform boosts efficiency, improves risk segmentation and uncovers new opportunities for growth and retention.

Community Health Options, a Maine-based nonprofit health insurer, has adopted the platform to improve underwriting and renewal processes. “We are always looking for ways to bring more consistency to our underwriting decisions, especially as groups move from quoting to renewal,” said Kevin Lewis, CEO of Community Health Options. “Gradient AI’s platform gives us a unified framework that helps our teams move faster, make clearer decisions and provide more stable pricing to our members.”

“Insurers are under pressure to make faster, more accurate decisions with fewer resources,” said Marc Jeffreys, general manager of Group Health at Gradient AI. “By integrating new business and renewal underwriting, our platform reduces the manual effort of reconciling data. It also gives insurers a clearer view of risk, so they can compete more effectively and build stronger relationships with employer groups.”

Gradient AI’s Risk Life Cycle Management solution is available immediately. For more details on the topic, see Gradient AI’s new white paper , AI-powered Integrated Risk Management Life Cycle solution: The Future of Group Health Insurance.

About Gradient AI

Gradient AI is a leading provider of proven artificial intelligence (AI) solutions for the insurance industry. Its full-cycle platform improves loss ratios and profitability by predicting underwriting and claim risks with greater accuracy, as well as reducing quote turnaround times and claim expenses through intelligent automation. Unlike other solutions that use a limited claims and underwriting dataset, Gradient AI’s software-as-a-service (SaaS) platform leverages a vast industry data lake comprising of tens of millions of policies and claims. Customers include some of the most recognized insurance carriers, MGAs, MGUs, TPAs, risk pools, PEOs, and large self-insured employers across all major lines of insurance. By using Gradient AI’s solutions, insurers of all types achieve a better return on risk. To learn more about Gradient AI, visit: https://www.gradientai.com or follow on LinkedIn .

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Media Contact

Elyse Familant

Results PR

elysef@resultspr.net

Terra, Gradient AI Partner on Workers’ Comp Claims Intelligence

Fri, 22 Aug 2025 16:12:52 GMT

August 22, 2025

By Anthony R. O’Donnell

The integration aims to deliver earlier risk insights to improve claims outcomes.

Terra (College Station, Texas) has announced a partnership with Gradient AI (Boston) to integrate predictive analytics into its cloud-native workers’ compensation claims platform.

James Benham, CEO, Terra.

The collaboration embeds Gradient AI’s risk modeling tools into Terra’s automation suite, which includes optical character recognition and AI-based claim summarization. The integration enables adjusters to identify high-risk cases earlier, prioritize workloads, and support faster, more informed decisions.

“Claims adjusters today are buried in paperwork and struggling to keep up with caseloads,” says James Benham, CEO, Terra. “With Gradient AI, Terra empowers adjusters to quickly identify the most critical claims and act decisively.”

“Workers’ compensation adjusters are under constant pressure to make fast, accurate decisions with limited resources,” says Stan Smith, CEO, Gradient AI. “By embedding our AI claims intelligence into Terra’s platform, we’re helping teams pinpoint high-risk claims earlier and prioritize the cases that need attention most.”

The integration addresses persistent inefficiencies in workers’ compensation systems, many of which rely on outdated infrastructure. According to the companies, the joint platform reduces manual tasks, streamlines triage, and improves outcomes for injured workers.

Terra reports that it continues to focus on practical AI applications as part of its 2025 roadmap, which emphasizes reducing complexity in claims handling and improving transparency throughout the process.

This article first appeared on Insurance Innovation Reporter .

7 Lessons from Carrier Leaders on Integrating AI in Group Health Underwriting

Mon, 11 Aug 2025 19:37:55 GMT

GROUP HEALTH INSIGHTS BLOG | Nov 13, 2023

The Gradient AI Team

An increasing number of group health carriers are using AI to improve underwriting. But getting started isn’t always easy. At a recent roundtable hosted by Gradient AI , underwriting leaders from several well-known carriers talked openly about what worked for them, what didn’t, and what they wish they had known earlier. Their advice is practical, straightforward, and worth hearing.

Here are seven lessons from insurance leaders to keep in mind if you're thinking about bringing AI into your underwriting operations.

1. Carriers are making AI a key part of their underwriting processes now.

Carriers aren’t just experimenting with AI; they’re adopting it out of necessity. As Matthew Agdish, AVP Underwriting, Blue Choice Health Plan, reflected: “When I came into this leadership role, we didn’t have any predictive analytics. That put us behind. Now with Gradient AI’s predictive analytics solution, we’re actually ahead of the curve.”

Takeaway: Embrace AI to catch up - or stay ahead - in the competitive landscape of Group Health underwriting.

2. AI unearths real risk patterns and finds the right business

Platforms powered by AI and machine learning algorithms help uncover underwriting opportunities beyond basic demographics. Nancy Schiel, VP of Underwriting, Commercial Markets Division, Medica shared: “We had a tight corridor of average groups. AI helped us finally identify good‑risk groups more accurately, which was the missing piece.”

Takeaway: Be more discerning - and strategic - in risk segmentation to better manage your portfolio.

3. Watch for integration pitfalls: calibration, blind trust, and delays

There are several significant challenges associated with adopting AI, including over-relying on risk scores without understanding their drivers, misalignment with your cost structures, and slow rollout timelines. As Josh Phelps, Solutions Consulting, Group Health, Gradient AI, warned: “Slow adoption has been a major misstep… Some have waited too long, and now they’re trying to catch up.”

Takeaway: Start early, align outputs with your data and pricing levers, and blend AI with actuarial judgment.

4. AI helps underwriters rather than replacing them

Contrary to fears of job displacement, AI empowers underwriters. Nancy Schiel of Medica noted: “There was initial concern about job security. But our team quickly saw how AI improved their effectiveness.”

Takeaway: When properly implemented, AI enhances consistency, builds confidence, and frees teams for strategic work.

5. Build or buy? Vendor data scale tips the balance

While some carriers consider building in-house AI, the sheer scale of vendor data sets tips the scales. Josh Phelps of Gradient AI emphasized: “Focus on results‑driven outcomes. Trust and verify.”

Takeaway: Leverage high-quality vendor solutions for richer data, faster impact and validation.

6. Understand AI vs. machine learning for smarter deployment

Develop internal AI fluency. Knowing the difference between AI (decision‑making systems) and ML (systems that learn and improve) makes a difference. Josh Phelps of Gradient AI said: “Without clarity, you may get static rules that never improve, or opaque ML models that are hard to interpret.”

Takeaway: Clearly define your strategy up front, then pick the right tool to meet it. Here’s more information on the difference between AI and machine learning.

7. Adopt AI with care: use change agents to secure buy-in

A solid change‑management strategy is key. Change management strategies will help you to set up a successful plan to assess readiness, align stakeholders, and appoint change agents to guide the transition.

Takeaway: Strong governance and intentional communication underpin successful AI adoption. Read Gradient AI’s guide to 6 Change Management Steps for Successful Adoption of AI in Insurance for how to execute a well-planned change management strategy.

Final Thoughts

These seven carrier‑sourced insights illustrate a path to successful AI integration: treat it as essential, manage it smartly, and lead people through the change.

Interested in bringing this to life at your organization? Reach out to Gradient AI for a strategic, results‑driven pilot.

Stay on top of AI trends by subscribing to Advanced Insights , the newsletter for strategies, ideas, and insights on AI in insurance. Delivered monthly to your inbox. Subscribe Now →

Explore the benefits of Gradient AI's Group Health Solutions.

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4 Steps to Accelerate AI‑Enabled Claims Management and Reduce Litigation in Workers’ Comp

Mon, 11 Aug 2025 13:31:09 GMT

P&C INSIGHTS BLOG | Feb 26, 2024

The Gradient AI Team

In Workers’ Compensation insurance, time is money, and delays can be costly in more ways than one. The longer a claim lingers, the greater the chance it will escalate, involve attorneys, and rack up expenses. Once litigation enters the picture, timelines stretch, adjuster workloads grow, and claim costs can skyrocket.

The challenge? High-risk claims don’t always announce themselves early. By the time red flags are obvious, it is often too late to change the outcome. That’s why more Workers Comp insurers are turning to AI-enabled claims management to spot potential problems before they spiral, empower adjusters with real-time insights, and intervene when it matters most.

This article walks through 4 practical steps to integrate AI into your claims process. The goal is to help your team identify risk earlier, resolve claims faster, and reduce litigation rates without overhauling your systems.

1. Use the system you already have

You don’t always need to overhaul your technology to get started. Many AI tools can be activated right within the claims platform your team already uses. With just a few configuration updates, risk scores and alerts become part of the existing workflow.

That means adjusters don’t need to learn a new platform, switch between systems, or attend hours of training. They simply get smarter tools inside the claims system they already know. This keeps disruption low and adoption high, which are both key to getting results fast.

2. Get earlier insight into claims that might go off track

AI can analyze patterns across millions of historical claims and flag early signs of trouble before most adjusters would spot them on their own.

These signals show up early in the claims process, often at the first notice of loss. That gives adjusters more time to take action, reach out to the injured worker, and offer support that may prevent the claim from escalating.

The goal isn’t to replace adjusters. It’s to give them a clearer view, sooner, so they can make better decisions in less time.

3. Lower the chance of legal involvement and reduce costs

Once attorneys get involved in a claim, timelines tend to stretch, and costs increase. AI helps teams identify claims that are likely to become litigated and intervene early, when there’s still time to change the course.

A research study by Gradient AI found that AI-enabled workers’ comp claims management reduced legal involvement in lost-time claims by 15%. This results in an overall 5 percent reduction in claims costs, which translates to more than $3.5 million in annual savings.

These results come from using predictive analytics inside the standard claims workflow, with insights available right when adjusters need them.

4. Roll it out quickly without slowing down operations

One of the best things about these newer AI tools is how easy they are to turn on. Because many are already built into popular claims platforms, it’s often just a matter of flipping a switch and setting up a few rules.

There’s no need for long IT projects or complex integrations. You can activate the AI tools, configure your scoring preferences, and start seeing insights in just a few days or weeks. Operations continue uninterrupted, allowing teams to use the insights immediately.

Final Thoughts: Bringing AI into Claims Management Doesn’t Have to be Complicated

With the right AI solution , you can enhance your current system, give adjusters better visibility into claim risk, and reduce the number of cases that turn into legal battles.

One proven example of this approach is the integration between Gradient AI and Origami Risk, which lets teams activate predictive analytics directly within their existing workflow. Work Comp insurers using this setup have seen faster claims resolution and measurable cost savings without slowing down their teams or adding new systems to manage.

Since Origami Risk and Gradient AI are already fully integrated – providing a simple path to AI-enabled claims management – you’ll gain all the benefits of AI in reducing Workers’ Comp litigation while improving overall claims workflow.

If you're ready to explore how this works in practice, we’re happy to walk you through the results our customers are seeing , and what it would look like for your team.

Stay on top of AI trends by subscribing to Advanced Insights , the newsletter for strategies, ideas, and insights on AI in insurance. Delivered monthly to your inbox. Subscribe Now →

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From Data to Decisions: Differentiating AI and Machine Learning in Group Health Underwriting

Tue, 27 May 2025 13:19:20 GMT

GROUP HEALTH INSIGHTS BLOG | Nov 13, 2023

The Gradient AI Team

Group health insurers operate under constant pressure to lower costs, improve member outcomes and accurately assess population risk. AI and machine learning hold real potential to streamline underwriting and uncover hidden trends, yet the two terms are often used without clarity. And this lack of precision could delay the benefits these technologies promise.

Distinguishing AI from machine learning is the first step to a successful deployment. AI refers to systems that automate decisions at scale, while machine learning describes how those systems teach themselves from data. By understanding the role of each, Group Health Carriers , Stop Loss Carriers , Captives , Brokers , PEOs , and MEWAs can select the right tools, set clear performance goals, and realize more reliable results.

Why Knowing the Difference Between AI and Machine Learning Matters

At its simplest, AI is the umbrella term for software that can mimic human reasoning, while machine learning is a way for that software to teach itself over time. If you treat both as the same, you may pick a tool that promises “intelligence” but never improves as your data grows. Or you might expect rapid insights from a learning system that actually needs months of historical data to gain accuracy. By knowing which approach you are buying, you can set realistic goals, choose the right implementation partner, and align your internal teams on project scope and timeline.

Investing in a pure AI workflow without an adaptive ML component can leave you with static rules that need constant manual updates. On the other hand, relying solely on ML models without an overarching AI framework can make results hard to interpret and integrate into your existing underwriting processes. Recognizing how AI and ML work together - or stand on their own - lets you deploy each technology where it delivers the greatest impact, from faster quotes to more precise risk predictions.

Artificial Intelligence: Decision-Making at Scale

AI refers to computer systems designed to replicate tasks that require human intelligence, like:

Analyzing data
Recognizing patterns
Making decisions
Processing language

In Group Health Carrier underwriting, AI helps simulate the way experienced underwriters evaluate medical risk, combining multiple data points to make quicker, more consistent decisions.

Imagine being able to analyze thousands of employee health applications and claims histories in minutes. AI makes this possible by leveraging automation and real-time reasoning. For Carriers, this translates into streamlined workflows and faster quoting. For Brokers and PEOs, it means better turnaround for clients and increased operational efficiency.

More advanced AI systems can even integrate unstructured data, like physicians' notes or social determinants of health, offering a deeper, more holistic picture of risk beyond traditional health metrics.

"What's revolutionary about AI isn't just the speed, it's the depth of analysis. This isn't about replacing human judgment but enhancing it and augmenting insights that would be impossible to uncover manually at scale.”

– Sumit Rane, Gradient AI Data Scientist

AI excels at seeing the forest and the trees simultaneously. And AI models don't just process more data, they fundamentally reshape our understanding of risk by identifying complex, non-linear relationships that human analysts might miss.

Machine Learning: Predicting What Comes Next

While AI provides the structure and logic, Machine Learning drives continuous improvement through data. Machine learning enables systems to learn from data without being explicitly programmed. Over time, ML models become better at identifying which patterns lead to higher claims, chronic illness development, or even noncompliance with care recommendations.

Consider a Stop Loss Carrier evaluating aggregate coverage for a 500-employee group. ML can highlight emerging high-cost claimants or forecast utilization trends, allowing for more precise risk corridor pricing or tighter laser thresholds. These insights are invaluable in Captives and Pooled Risk Groups environments as well, where long-term risk containment is critical to maintaining stable premiums and reserves.

"Machine learning is like a baseball player who improves batting by analyzing past pitches and outcomes. AI is the complete athlete who understands game strategy, adapts to opponent's styles, and makes split-second decisions based on full context of the play."

– Sumit Rane, Gradient AI Data Scientist

The Data is Already Here and It’s Time to Use It

Group Health insurers and Brokers are sitting on a goldmine of underutilized data. Claims history, biometric data, wellness participation, prescription data, and even socioeconomic indicators are often siloed or analyzed retrospectively. AI and ML can integrate and analyze these diverse data streams in real time, identifying emerging risk patterns, inefficiencies, and cost-saving opportunities.

For Stop Loss Carriers, Captives, and Pooled Risk Groups especially, predictive modeling can significantly enhance underwriting by detecting high-risk claims before they develop. AI can also proactively flag potential large-loss members and recommend targeted interventions, ultimately driving better loss ratio management.

One of the most promising applications of AI is the ability to personalize the healthcare journey. For PEOs and Brokers, who often manage diverse client groups with varying needs, AI can segment populations and predict the best programs or care pathways for each demographic.

AI and Machine Learning Are Rewriting the Rules of Medical Underwriting: 5 Key Benefits with Real Life Examples

For Group Health Carriers, Stop Loss Carriers, Captives, Brokers, PEOs, and Pooled Risk Groups, AI and ML models are creating an opportunity to work smarter, respond faster, and deliver more value to clients.

Here are 5 key benefits of using an optimized AI and Machine Learning system:

1. Seeing Around Corners: Predictive Power at Your Fingertips

Imagine being able to spot a high-cost claimant before they hit your loss ratio. That’s the promise of machine learning. Instead of reactive analytics, AI gives you predictive foresight.

For Stop Loss, Captives, and Pooled Risk Groups, this means smarter underwriting and better control of large claims. For Brokers, it’s the ability to offer clients next-level insights and strategy. AI models can predict who’s likely to experience chronic conditions, when care gaps are emerging, or where cost savings can be realized.

Think of it as X-ray vision for your risk portfolio.

2. Enhancing the Underwriting Process

The integration of AI and ML in group health medical underwriting goes far beyond automation:

Risk Stratification: AI models can identify subgroups within employee populations with varying levels of health risk, enabling customized plan design and wellness strategies.
Behavioral Insights: ML can predict how likely individuals are to engage in certain behaviors, like preventative screenings or medication adherence, helping insurers offer more targeted benefits.
Continuous Improvement: As more data is processed, ML algorithms refine their accuracy—constantly learning from new information to improve decision-making and forecasting.

This creates a self-improving underwriting ecosystem, moving from static risk assessment to a dynamic, learning-driven model.

“In the context of group health underwriting, this marks a paradigm shift moving away from those one-size-fits-all risk calculations to deeply personalized risk assessments. It considers the full context of an individual's unique health journey, which enables more meaningful group-level risk assessments that capture the true complexity of population health."

– Sumit Rane, Gradient AI Data Scientist

3. Improving Recommendations

For Pooled Risk Groups, Captives and Stop Loss Carriers, this means better pricing accuracy, greater risk predictions, and enhanced member outcomes.

For Brokers and PEOs, it's a chance to differentiate – to deliver more data-informed recommendations, identify group risks earlier, and support HR leaders with predictive strategies that resonate.

And for Group Health Carriers, it's about staying relevant in a market where tech-driven agility is fast becoming the baseline.

4. More Transparency and Trust

One of the hidden benefits of AI is clarity. With smarter systems analyzing massive datasets, you can explain decisions and show your clients the “why” behind the recommendations. This helps build transparency and trust which is a big win in today’s market.

For anyone managing or advising on risk, this also means stronger board reporting, clearer reserve forecasts, and a better handle on volatility.

5. It’s Not Just for the Tech Giants Anymore

You don’t have to be Google, Amazon or a large, National Group Health Carrier to use AI. Thanks to accessible platforms and integrations, any organization—regardless of size—can begin embedding AI and machine learning into their operations. Whether you start with a pilot program, a predictive model for chronic care, or an automated quoting engine, small steps lead to big results.

The key is starting.

Conclusion: Master the Difference Between AI and Machine Learning to Capture the Value

Understanding the distinction between AI and machine learning is more than semantics. It shapes your strategy, guides your technology choices and ensures you capture the full benefit of each approach. AI gives you scalable decision-making. Machine learning delivers ongoing, data-driven improvement. When you apply both in harmony, underwriting moves from guesswork to foresight.

By starting with clear definitions, you set realistic goals, avoid costly surprises and accelerate results. The organizations that succeed will be those that treat AI and ML as complementary tools - each with its own strengths and requirements.

Ready to turn clarity into competitive advantage?

Reach out to Gradient AI to learn how a precise, informed approach can transform your group health underwriting today . Whether you’re a Carrier, Captive, MGU, Broker or PEO, Gradient AI can help you leverage AI and ML to drive profitable growth and better outcomes for your clients.

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Wins, Pitfalls, and Best Practices in Integrating AI in Carrier Health Underwriting: Insights from Industry Leaders

Tue, 27 May 2025 13:18:47 GMT

GROUP HEALTH INSIGHTS BLOG | Nov 13, 2023

The Gradient AI Team

As AI becomes increasingly embedded in healthcare, group health insurance carriers are looking to leverage it to enhance underwriting, optimize risk assessment , and improve portfolio performance .

Gradient AI recently sponsored a webinar, The Role of AI in Shaping Carrier Health Underwriting: Proven Approaches and Insights hosted by Becker’s Payer Issues , in which insurance leaders shared their journeys, lessons, and outcomes around integrating AI into their underwriting process.

>> Watch the on-demand webinar now

Expert Panelists Share Their AI Integration Experiences

Our expert panelists from the group health insurance industry:

Matthew Agdish , AVP Underwriting, Blue Choice Health Plan
Nancy Schiel , VP of Underwriting, Commercial Markets Division, Medica
Josh Phelps , Solutions Consulting Group Health, Gradient AI

Matthew Agdish has over 30 years industry experience with National and Regional Carriers and underwritten groups of all sizes and funding options. Nancy Schiel also has extensive experience, with over 30 years in the business. She is currently responsible for developing underwriting and pricing strategies for single employer large groups, fully insured and self-funded products.

Prior to joining Gradient AI, Josh Phelps served in multiple health insurance roles, most recently as Chief Actuary for a major blues plan. Josh’s area of expertise includes actuarial pricing, large and small group underwriting, machine learning, programming and artificial intelligence.

7 Key Insights About AI Adoption: Learn from the Webinar Panelists

This blog post summarizes 7 key insights from the webinar, along with direct quotes from our panelists, plus their actionable takeaways.

1. AI is Now a Must-Have for Carriers:
From staying competitive to refining pricing strategies, insurers realize that AI is no longer optional – it’s a strategic imperative.

Matthew Agdish: “When I came into this leadership role, we didn’t have any predictive analytics. That put us behind. Now with Gradient AI’s predictive analytics solution, we’re actually ahead of the curve.”

Key Takeaways:

AI helps catch up or leap ahead in competitive markets.
Predictive analytics is now essential for assessing risk more accurately.
Carriers not using AI are already seeing the consequences of being behind the curve.

2. AI Provides Better Risk Identification and Portfolio Management:
AI tools allow insurers to go beyond surface-level demographics to uncover deep, actionable insights about group health risks.

Nancy Schiel: “We had a tight corridor of average groups. AI helped us finally identify good-risk groups more accurately, which was the missing piece.”

Key Takeaways:

AI enables more granular risk segmentation, not just high-risk avoidance.
Underwriting becomes more consistent, with reduced person-to-person variability.
First-year renewals are now more predictable and stable.

3. Common Pitfalls and Lessons Learned About AI Integration:
Adopting AI comes with challenges, including these three: over-reliance on risk scores without knowing what’s driving the results, poor calibration with your cost structure and book of business, and slow implementation.

Critical insights from the panelists:

Josh Phelps: “Slow adoption has been a major misstep in the market. Some have waited too long, and now they’re trying to catch up.”

Matthew Agdish: “One of our early mistakes was not benchmarking AI outputs properly. If you put too much or too little weight on it, you risk mispricing.”,

Nancy Schiel: “It’s about guardrails. You need boundaries around the tool, so underwriters don’t go rogue.”

Key Takeaways:

Don’t blindly follow AI. Instead, balance it with actuarial judgment.
Calibrate models to your own cost structure and data reality.
Delays in AI adoption can leave carriers exposed or competitively disadvantaged.

4. AI is Not a Replacement for Seasoned Underwriters:
A common concern is that AI may replace human jobs. Our panelists told us that in reality, it’s more about augmentation than automation or replacement.

Nancy Schiel: “There was initial concern about job security. But our team quickly saw how AI improved their effectiveness.”

Matthew Agdish: “The consistency AI brought took out a lot of guesswork. It helped our underwriters make better decisions without feeling replaced.”

Key Takeaways:

AI reduces subjectivity and variability, improving confidence in decisions.
Underwriters feel more empowered when using AI tools, not threatened.
It frees time for value-added analysis, rather than repetitive tasks.

5. Data Scale and the “Build vs. Buy” Debate:
Some carriers considered developing their own in-house AI tools. But the depth and breadth of vendor datasets tip the scale toward buying. Vendors with access to massive datasets (i.e., millions of members) can train far more effective models than most in-house carrier systems. This makes buying AI tools more practical and powerful than building from scratch for many insurers.

Josh Phelps: “Carriers might have tens of thousands of claims. At Gradient AI, we have over 15 million lives in our models. That level of scale matters.”

Key Takeaways:

External tools often offer more robust models, thanks to larger datasets and refined algorithms.
The build vs. buy decision should account for speed to market and data breadth.

6. AI Helps with Funding Strategy Optimization:
AI doesn’t just help with rating. It also informs funding mechanisms, helping determine whether a group is better suited for fully insured, level-funded, or self-funded plans.

Josh Phelps: “AI helps identify the right fit for a group’s risk profile, which leads to better long-term outcomes for both the client and the insurer.”

Key Takeaways:

Smarter funding recommendations = better client retention and more sustainable books.
Helps brokers and clients understand the “why” behind the rate without exposing raw outputs.

7. Practical Advice for New Adopters Getting Started with AI:
The panelists had plenty of guidance for carriers just beginning their AI journey.

Nancy Schiel: “Do your own analysis and understand the data. Partner with your actuarial team and make sure you have calibrated appropriately based on your cost space and your book of business.”

Josh Phelps: “Focus on results-driven outcomes. Trust and verify. Have your vendor do a Proof of Concept or Pilot so you can see how this solution would have affected your books historically or how you would use it going forward and compare the impact if you did not have the tool.”

Matthew Agdish: “Make sure the tool you use has the ability to drill down and provide you with additional details. It’s nice to have the scores and the dashboard of results on a high-level but having the statistical detail we get with Gradient AI, that level of insight and detail is invaluable.”

Key Takeaways:

Develop internal AI fluency: understanding how to use AI matters just as much as having it.
Run a proof of concept or pilot before full rollout.
Do your homework. Focus on outcomes, not just hype. Make sure your solution gives you the data behind the risk score.
Read Gradient AI’s detailed Guide to Change Management around AI adoption , for a clear roadmap of actionable steps for successful integration across your teams.

The Most Successful Carriers are Those Who Combine AI with Sound Underwriting Judgement, Good Data Practices, and Organizational Alignment

Whether you're at the start of your AI journey or refining an existing approach, one message is clear: AI is here to stay, and those who embrace it carefully and smartly are reaping the rewards.

To Learn More, Watch the On-Demand Webinar!

To hear more from the panelists, watch The Role of AI in Shaping Carrier Health Underwriting: Proven Approaches and Insights, hosted by Becker’s Payer Issues, and sponsored by Gradient AI. To learn more about AI for Carrier underwriting, contact us today .

Becker’s Payer Issues is the leading news organization covering the U.S. health insurance industry. The editorial team delivers daily breaking news, business analysis and expert interviews across the evolving insurance landscape, delivering the latest need-to-know.

Stay on top of AI trends by subscribing to Advanced Insights , the newsletter for strategies, ideas, and insights on AI in insurance. Delivered monthly to your inbox. Subscribe Now →

Explore the benefits of Gradient AI's Group Health Solutions.

Schedule a Demo →

Beyond Big Data: Why Quality Data Matters in Workers’ Comp AI Models

Thu, 15 May 2025 22:56:08 GMT

May 15, 2025

By Stan Smith

For insurance executives evaluating AI solutions, here’s the critical truth: Your AI is only as good as your data. In workers’ compensation, where medical and legal complexities are especially prevalent, quality well-structured quality data separates market leaders from the rest. Underwriters and claims professionals require high-quality data to make informed decisions that reduce risk, control costs, and improve outcomes. As AI plays a larger role in underwriting and claims management, the need for reliable, comprehensive data has never been greater.

Strengthening Workers’ Compensation Underwriting

In workers’ compensation as well as other types of insurance coverage, accurately assessing a group’s risks ensures competitive and sustainable pricing commensurate with the group’s risk. But underwriting is only as good as the data it’s built on.

AI-powered work comp underwriting models need vast amounts of data covering every class code, region, and risk factor. The broader and more representative the data, the better insurers can account for industry-specific risks, regional differences, and emerging trends. Without a dataset that is both broad and deep, AI models may not assess risk accurately—potentially leading to underpriced policies that negatively impact insurers’ loss ratios, or overpriced ones that drive businesses away. Similarly, insurers looking to expand into new geographies or lines of business rely heavily on extensive quality data to help them assess risks in these new markets.

Reliable data is critical for fueling predictive analytics, which helps insurers anticipate workplace injury risks based on historical claims, job conditions, and even economic trends. This foresight lets insurers work with employers to put risk prevention strategies in place-ultimately reducing the frequency and severity of claims.

Improving Claims Management

Quality data also supports claims management. When a workplace injury occurs, insurers must quickly determine the best course of action. AI-driven claims analysis, powered by vast datasets and insights from previous claims, helps insurers recognize patterns that indicate whether a claim is likely to escalate, require litigation, or benefit from an independent medical exam (IME). A study by Travelers found that first-year employees account for 35% of workplace injuries, leading to over 6 million lost workdays.

AI models trained on historical claims data can flag high-risk cases early, allowing claims managers to intervene proactively to help minimize claim duration and cost. If certain injuries in a specific industry tend to lead to extended time off, claims adjusters can implement early return-to-work programs tailored to those cases.

Access to quality data also helps uncover fraud. The Coalition Against Insurance Fraud estimates that fraudulent claims cost the industry over $308 billion annually . AI-powered fraud detection tools analyze claims histories, provider patterns, and inconsistencies in reported injuries to identify red flags early helping insurers take action before costs spiral out of control.

Why High-Quality, Reliable Data Matters

AI models are only as effective as the data they rely on. If data is incomplete, outdated, or inconsistent, predictions become unreliable, leading to flawed risk assessments and poor decision-making. An insurer my company works with recently found that 40% of its most critical data fields were missing or incomplete, making it difficult to build reliable AI models. This issue is widespread—missing or inconsistent data limits an organization’s ability to identify patterns and make informed decisions. AI thrives on detail, whether it’s analyzing claims demographics or tracking operational risk trends, and yet many insurers face similar problems with “messy” data. However, without complete and standardized data, AI models can’t generate accurate insights.

Digital Insurance highlights that top-performing insurers set themselves apart by prioritizing rigorous data validation and reconciliation processes.

To fully harness AI-driven insights, insurers need data that is:

Accurate: Free of errors to support sound risk evaluations and claims decisions.
Comprehensive: Captures all relevant details about policyholders, claims, and medical treatments.
Consistent: Standardized across different sources to prevent discrepancies.
Timely: Continuously updated to reflect real-world risks and conditions.
Unique: Avoids duplicate or conflicting records that could distort insights.

Fortunately, insurers do not need to rely solely upon their own databases to implement AI effectively. A well-structured contributory data lake, which aggregates anonymized claims data from multiple insurers, offers a wider view of industry risk trends. This shared intelligence allows insurers to benchmark their claims experience against broader industry patterns, leading to more accurate underwriting and more effective claims management strategies. Insurers can leverage such a data lake without necessarily addressing their own data hygiene, and access a far larger and higher quality dataset than they ever could within their own four walls.

Steps to Strengthen Data for AI

To maximize AI’s potential, insurers must proactively enhance their data environments.

Steps include:

Assessing data quality: Conduct a data census to evaluate completeness, consistency, and accessibility.
Identifying gaps and clean data: Fill missing fields by integrating external sources or using tools to infer incomplete information.
Establishing governance policies: Implement standardized data collection methods, such as mandatory fields, dropdown menus, and validation checks.
Checking for bias: Analyze datasets to ensure fairness across demographic and geographic segments, reducing skewed AI outcomes.

By implementing these strategies, insurers create a strong foundation for AI-powered decision-making, ensuring models operate with accuracy and fairness.

Measuring AI Success in Workers’ Compensation

Once an AI-driven approach is in place, insurers need clear ways to track its effectiveness. Key methods include:

Defining success metrics: Establish measurable goals, such as improved claims processing speed or risk assessment accuracy.
Backtesting models: Validate AI predictions by applying them to past claims data and comparing results to actual outcomes.
A/B testing: Compare AI-driven processes with traditional approaches to determine effectiveness in real-world scenarios.

By consistently measuring and refining AI performance, insurers can ensure their models continue to drive better underwriting, claims management, and fraud prevention.

The Future of AI in Workers’ Compensation

AI is reshaping workers’ compensation, but its success hinges on the quality and depth of data. Insurers that invest in strong data strategies will gain a competitive edge, developing pricing models that accurately reflect risk, improving claims decision-making, and proactively identifying high-risk claims. Enhanced fraud detection will also help reduce unnecessary payouts, strengthening industry efficiency.

Ultimately, insurers that prioritize high-quality, well-structured datasets will lead the way in delivering fairer, more efficient, and cost-effective workers’ compensation solutions. AI is a powerful tool, but its success hinges on the strength of the data behind it.

Stan Smith is founder and CEO of Gradient AI and has nearly 30 years of experience in AI and technology. Drawing on the latest advancements in AI and machine learning, he launched Gradient AI to help insurers harness data more effectively. The company’s solutions empower insurers to enhance underwriting accuracy, streamline claims processes, and ultimately improve loss ratios and profitability.

Stan Smith, Gradient AI

This article first appeared on WorkCompWire.

The next AI shift: what DeepSeek means for insurance

Mon, 05 May 2025 17:58:04 GMT

April 25, 2025

By Stan Smith

From general AI to industry-specific intelligence

The AI landscape is shifting—fast. With DeepSeek delivering ChatGPT -level performance at a fraction of the cost, we’re witnessing the next evolution of AI: one that makes advanced models more affordable, accessible, and industry-specific.

We’ve seen these technology shifts before:

Mainframes evolved into PCs
On-prem solutions gave way to the Cloud
Custom chips were overtaken by GPUs
Expensive, general-purpose AI is transitioning to more affordable, specialized solutions

Each transition unlocks waves of innovation at the application level. Now, AI is following the same trajectory, and insurance will benefit immensely.

The future isn’t about building massive, expensive foundation models—it’s about developing AI tools that solve real, industry-specific problems.

From General-Purpose AI to Industry-Specific Intelligence

The real impact of AI isn’t in the foundation models themselves—it’s in how businesses apply them. Large language models ( LLMs ) like GPT-4 are powerful, but they weren’t designed for nuanced, industry-specific tasks. That’s why the next wave of AI innovation will focus on specialized, smaller models that are trained on deep, proprietary data sets rather than general internet knowledge.

In insurance, this means AI that understands policy language, risk factors, and claims trends at a granular level. Instead of generic AI tools that require extensive customization, insurers can now adopt purpose-built AI solutions that deliver more accurate risk assessments, streamline underwriting, and optimize claims processing.

The emergence of DeepSeek highlights this shift. Its ability to deliver OpenAI -level performance at a fraction of the cost is a glimpse into the near future—where AI models are cheaper and more efficient, accelerating the development of vertical AI solutions.

Why the Next AI Unicorns Won’t Be Building Foundation Models

For years, AI has been dominated by companies building massive, foundational models—requiring billions of parameters and millions of dollars to train. But history tells us that when core infrastructure costs drop, application-level innovation takes off.

For example, AWS made cloud computing infrastructure widely available, leading to an explosion of SaaS startups. We’re now seeing the same shift with AI: the real breakthroughs won’t come from those training massive models, but from those applying AI in novel, industry-specific ways.

For insurers, this shift is a game-changer. Instead of investing in generic AI tools that require extensive customization, they can now access affordable, purpose-built AI designed specifically for underwriting, claims processing, and risk management. The next AI unicorns won’t be competing with OpenAI or DeepSeek to build foundation models—they’ll be the companies applying these models to solve real-world problems in insurance and beyond.

The Business Case for Specialized AI in Insurance

With AI becoming more cost-effective, insurers have a unique opportunity to embrace vertical AI solutions that provide immediate, tangible benefits.

For example, AI models tailored for insurance can analyze massive amounts of historical claims data to refine risk assessment and pricing strategies. Traditional AI requires insurers to build custom solutions from scratch—often leading to long, expensive development cycles. With specialized AI, insurers can quickly deploy models that understand their business from day one.

In addition, these industry specific models lead to:

Better Accuracy – AI trained on insurance-specific data provides deeper insights, improving underwriting and claims assessment.

Faster Implementation & ROI – Specialized AI solutions require less customization and can be deployed faster, delivering value more quickly.

Enhanced Decision-Making – AI trained on insurance-specific data can process complex data more effectively, leading to smarter risk management.

Cost Efficiency – As AI infrastructure becomes more accessible, insurers can invest in tailored solutions without the high costs of general AI platforms.

This shift also levels the playing field. Previously, only large insurers with deep pockets could afford AI-driven insights. Now, smaller insurers can harness the power of AI without massive infrastructure investments, making them more competitive in the market.

Looking Ahead: AI’s Role in Shaping the Future of Insurance

DeepSeek marks the shift of the next wave of AI—a wave that prioritizes affordability, accessibility, and industry-specific applications. History shows us that technological advancements not only make better tools, but they also change how industries operate. Just as cloud computing enabled the SaaS revolution, affordable AI models will empower insurers to make smarter decisions, reduce risk, and operate more efficiently.

The bottom line? The next AI revolution won’t be led by massive, generalized models. It will be driven by specialized AI applications that solve real problems in insurance and beyond. The companies that embrace this shift early will be the ones leading the industry into the future.

Final Thoughts

For insurers, this is an opportunity to rethink their approach to AI. Instead of viewing AI as an expensive, broad tool that requires extensive customization, they should be asking: What problems do we need AI to solve?

The answer isn’t a massive, general-purpose model—it’s an AI solution purpose-built for their industry.

DeepSeek has shown that the economics of AI are changing. The question isn’t whether AI will reshape insurance—it’s how quickly insurers will adapt to this new reality.

Stan Smith, Gradient AI

Stan Smith is the founder and chief executive officer of Gradient AI

This article first appeared on TechRadarPro .

Helping Brokers and Consultants Compete: Why Brokers and Consultants Are Turning to AI for Smarter Insurance Decisions

Thu, 01 May 2025 22:03:59 GMT

GROUP HEALTH INSIGHTS BLOG | Dec 20, 2023

The Gradient AI Team

Group health underwriting is a high-stakes business. Speed, accuracy, and pricing are critical, but traditional tools often fall short, especially when data gaps or complex client profiles are involved.

Gradient AI understands that brokers and consultants in this market are facing multiple interrelated challenges, stemming from rising healthcare costs, increased client expectations, regulatory complexity, and the growing demand for data-driven solutions.

5 Top Challenges Facing Brokers and Consultants in Group Health Underwriting Today

Here's a breakdown of five of the top challenges:

Limited risk visibility: Brokers and consultants often lack access to complete or timely data, especially for smaller groups, making it difficult to accurately assess risk.
Rising and unpredictable costs : Healthcare expenses continue to climb, with volatility driven by high-cost claimants and specialty drugs.
Pressure to deliver savings : Clients expect strategic cost-containment guidance, but limited underwriting insight makes this hard to achieve.
Tight quoting timelines : Brokers and consultants must respond quickly to RFPs, often with limited data and time for thorough analysis.
The need to differentiate: Many brokers offer similar quoting approaches, making it difficult to stand out without deeper insights.

“Gradient AI provides insights into high-cost drugs and conditions for groups where this data is currently unavailable. This enables our broker and consultant clients to strategically identify cost-containment solutions, alternative funding options, or care management programs — all aimed at maintaining or reducing costs for their customers.”

– Chase Pettus. VP, Group Health, Gradient AI

How AI-Powered Underwriting Helps Brokers and Consultants Succeed

Here’s where AI comes in. AI models for underwriting can help address these top challenges, allowing brokers and consultants to:

See risk more clearly. AI models deliver accurate risk scores even when claims data is limited. By analyzing alternative data like Rx or census files, brokers can confidently assess small and mid-sized groups.
Stay ahead of rising costs. AI can identify likely high-cost claimants and key cost drivers early, helping brokers anticipate costs and recommend strategies to manage volatility.
Respond faster. AI dramatically shortens quote turnaround times. Brokers and consultants can meet tight deadlines and improve responsiveness during RFP season.
Help stand out from the crowd. Using advanced underwriting tools positions brokers and consultants as data-driven advisors - giving them a clear edge over firms still relying on manual methods.
Operate more efficiently. AI automates and scales underwriting processes - freeing up time, reducing errors, and allowing time to serve more clients more effectively
Offer more strategic value, moving beyond quotes. With AI-driven insights, brokers can guide clients toward smarter plan designs, cost-saving programs, and better risk management.

Why Brokers and Consultants Choose Gradient AI for Group Health Underwriting

Gradient AI’s SAIL™ solution gives brokers and consultants the insight they need to guide their clients more effectively and negotiate with greater confidence.

Gradient AI partners with brokers and consultants who need to:

Improve visibility into group health risks
Strengthen carrier negotiations with data-backed insights
Deliver tailored recommendations for each client group

SAIL™ offers predictive analytics that helps fill in the gaps, especially when there’s limited claims history or the group includes a complex mix of covered individuals.

With Gradient AI’s SAIL™ solution , brokers and consultants can:

Identify cost-saving opportunities: Share high-cost pharmacy data to explore PBM alternatives and manage spending more effectively.
Improve stop loss strategies: Use predictive analytics to support smarter stop loss decision-making.
Navigate business changes: Understand the impact of mergers and acquisitions on group risk.
Project future claims: Use Gradient AI’s models to forecast risk and claims more accurately.
Negotiate pricing with confidence: Bring deeper insights to your discussions with carriers.

“We use SAIL™ to give our clients better insight and a better prognosis into claims. Prior to using SAIL™, we could only look through the rear-view mirror at past claims data. Now we can look through the windshield to better predict what costs our clients can expect to incur. With the speed, quick turnaround, and insights we've gained, SAIL™ has more than paid for itself.”

– Chief Strategy Officer (and Gradient AI client), Employee Benefits Agency

A Clearer Picture of Group Risk

SAIL™ provides greater transparency into a group’s health profile. It helps brokers and consultants work with their clients to create solutions that are specifically aligned to their population’s needs. Gradient AI uses a combination of medical claims data, prescription and lab data, and machine learning-powered modeling

Together, these inputs deliver fast, reliable risk projections, even with minimal historical data.

Ready to Learn More?

Gradient AI can help you support better outcomes for your clients while staying competitive in a fast-moving market. To find out how SAIL™ can support your business, download our capabilities brochure , and get in touch with us today!

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The Foundation of Intelligence: How Quality Data Drives Smarter AI Models in Insurance

Wed, 23 Apr 2025 12:26:05 GMT

P&C INSIGHTS BLOG | Feb 26, 2024

The Gradient AI Team

Data is critical for insurers looking to integrate artificial intelligence (AI) models into their underwriting and claims processes. It’s not an overstatement to say that data is the fuel that powers every aspect of AI performance – from accuracy and efficiency to trust and long-term scalability.

Quality data drives better decisions. And better decisions benefit everyone.

Back-to-Basics Review: What are AI Models and How Do They Use Data?

Before we examine how quality data helps insurers leverage AI to make better decisions, let’s first take a quick step back and define what an AI model is, what kind of data is needed for AI models, and what the models do with the data.

1. What Is an AI Model?

An AI model is an advanced software system that learns from large volumes of data to identify patterns, trends, and correlations. Once trained, it can support a wide range of functions for insurers, including:

Predicting future outcomes
Detecting anomalies
Recommending actions
Enabling faster, more informed decision-making

Much like a student improves through practice and feedback, an AI model becomes more accurate and effective as it is exposed to large numbers of real-world examples. Over time, this learning process allows the model to perform complex tasks with increasing precision and reliability.

2. What Kind of Insurance Data Do AI Models Use?

As an insurer, you already know that your organization generates and manages large volumes of data. This same data serves as the foundation for training AI models, and typically includes policyholder information, historical claims data, property characteristics, accident and incident reports, visual assets, notes and documentation from the underwriting and claims teams.

However, if you train AI models only on your in-house data, they can make predictions based only on the outcomes your company has seen. On the other hand, when you combine your in-house data with high-quality data from other carriers, AI models have the opportunity to see and learn from a wider range of policies and claims outcomes. They can enable you to operate more effectively across a wider range of opportunities and circumstances

Each of these data sources contributes to the AI model’s ability to recognize patterns, assess risk more accurately, and enhance decision-making throughout the insurance lifecycle.

3. What Do AI Models Actually Do with the Data?

In very simple terms, AI models:

Look at the past using data from millions of examples.
Find patterns that humans might miss.
Make predictions about things like how risky an underwriting applicant is, or how much a claim might cost.
Get smarter over time as they get exposed to more data and more feedback.

AI is Only as Good as the Data Behind It

AI models learn patterns, make predictions, and generate insights based on the data they're trained on. If the data is:

Incomplete , the model may miss important risk factors.
Inaccurate , it could lead to poor predictions or pricing.
Biased , the model could unintentionally discriminate or underperform for certain populations.

High-quality, structured, and relevant data enables insurers to build models that are accurate, reliable, and compliant – which is especially important in highly regulated sectors like insurance. That’s why its important for insurers to make sure their data is clean, organized, and secure.

If insurers lack the resources to supply clean data, they can partner with an organization like Gradient AI . Over the last few years, Gradient AI has invested significant resources into our contributory industry data lake . Currently, it houses tens of millions of structured and unstructured underwriting and claims records. All of the data is de-identified to ensure anonymity. Data security is paramount, and Gradient AI makes this a top priority by being both SOC2 compliant and HITRUST certified.

Good Data Plays a Central Role in AI Models for Risk Management, Underwriting, and Claims. Here’s How.

1. Risk Management: Moving from Reactive to Predictive

Traditionally, insurers have managed risk using historical models based on the law of large numbers. AI moves the insurance industry to a new level, allowing for more proactive strategies, like:

Modeling systemic or correlated risks across portfolios
Enhancing catastrophe modeling with climate or satellite data

These advanced capabilities require the integration of structured and unstructured quality datasets into a coherent, trustworthy data ecosystem.

2. Underwriting: Precision and Speed at Scale

Insurers process volumes of applications across lines of business and geographies. Good data helps AI tools:

Identify hidden risk indicators more quickly
Automate and accelerate routine decisions
Personalize pricing based on nuanced, granular risk profiles

Quality historical data enables better risk segmentation and pricing models - unlocking both efficiency and enhanced profitability.

3. Claims Management: Predicting Claim Severity and Improving Processing Speed

AI-powered claims models enhance outcomes for both claimants and insurers by:

Flagging suspicious or inconsistent data for fraud detection
Predicting claim severity or litigation potential
Recommending optimal resolution paths or payout amounts

But these capabilities hinge on data that’s accurate, real-time, and comprehensive, including claims history, adjuster and case notes, structured policy information, external third-party data, images, and more.

4. Regulatory Compliance and Explainability

Regulators increasingly require insurers to explain how automated decisions are made. If the data is messy, incomplete, or undocumented, it’s nearly impossible to demonstrate risk controls, model fairness, and compliance.

Insurers need access to models that are designed, trained, and tested to meet regulatory standards, providing ethical AI in insurance underwriting.

Good data enables explainable AI (meaning the ability to understand why an AI model made a certain decision or prediction), which is critical for maintaining regulatory and reputational trust.

Conclusion: Turning Data into Actionable Intelligence Across the Insurance Lifecycle

In summary, high-quality data is essential to making AI work. In the insurance industry, from risk management to underwriting and claims, AI models depend on clean, well-structured, and relevant data to find patterns, make accurate predictions, and support faster, more informed decisions. When data is incomplete, inaccurate, or biased, model performance suffers.

For smart insurers, success with AI is not just about advanced algorithms; it also requires strong data foundations. Investing in reliable data infrastructure or partnering with an experienced provider like Gradient AI , helps ensure that insurance AI models are accurate, effective, explainable, and aligned with regulatory standards.

Stay on top of AI trends by subscribing to Advanced Insights , the newsletter for strategies, ideas, and insights on AI in insurance. Delivered monthly to your inbox. Subscribe Now →

Explore the benefits of Gradient AI's P&C or Workers' Comp Solutions.

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Beyond Automation: How GenAI Informs Next Best Action in P/C Insurance Claims and Underwriting

Tue, 22 Apr 2025 19:45:48 GMT

April 22, 2025

By Stan Smith

The insurance industry is increasingly adopting AI to streamline operations, automate tasks and improve customer experiences. But while AI automation, in particular, has become a standard assistive tool in many insurance professionals’ tool stacks, it has significant limitations—especially in guiding greener professionals through complex claims and underwriting situations.

Generative AI (GenAI)—the latest AI technology that generates data-driven responses and ideas—represents a novel opportunity to support these less-experienced insurers and even seasoned veterans with reliable recommendations for next steps. For example, GenAI models can analyze customer interactions in real time and recommend policy upgrades, risk management strategies or proactive measures to avoid costly claims and losses. In these scenarios, GenAI lifts some of the burden off understaffed and overworked insurance companies, giving employees the tools and data-driven recommendations they need to determine the next best action.

GenAI’s Role: Next Best Action in Smart Claims Management

Insurance claims processing historically has been an intricate and nuanced process, requiring junior staff and experienced adjusters to play different roles. AI models have evolved to handle different aspects of claims management, focusing on basic automation, identification and triage functionality. But even with this support, the general setup and roles played by adjusters have stayed the same—AI has simply sped up the process by quickly identifying complex versus simple claims and routing them accordingly.

With the development of GenAI, AI models have moved beyond simple identification to actionable recommendations for different kinds of claims. For simpler claims, AI classifies the claim and recommends fast-tracking to decrease time to completion, enhance customer satisfaction and reduce administrative overhead. Before this development, insurers relied on their own experiences and manual research to decide when a claim can be fast-tracked.

With GenAI, complex claims can be identified quickly and provide well-grounded, precise and data-backed recommendations. This may include suggesting second medical opinions, independent exams and assessments, alternative treatment plans, or even that the insurance provider engages proactively with a claimants’ attorney to review possible early settlement options. Insurance professionals can get detailed explanations from GenAI outputs, which helps them understand exactly what steps to take and when and how to reduce costs and improve recovery outcomes.

GenAI’s Role: Next Best Action in Smart Underwriting

AI in underwriting relies on large datasets to help insurers prepare and price policies that most accurately reflect the underlying risks. These datasets include historical claims, current market trends, and regional risks such as crime rates and environmental hazards, all of which might impact policy costs or outcomes. AI enhances risk assessment by analyzing massive datasets that insurers would never be able to efficiently process manually.

GenAI takes AI-driven underwriting a step further by providing real-time, detailed recommendations and evaluations of risk based on these large datasets. Recommendations are highly contextual and take into account external factors like localized crime rates, environmental hazards, inflation, interest rates, unemployment, regulations and litigation trends.

AI effectively helps workers compensation insurers effectively identify nuances in risk based on the underlying health of the group. For example, if the data shows that three employees have back problems, AI can provide insight and make recommendations into the relative risk of that group for a workers compensation insurer. With this level of insight, underwriters have the knowledge they need to avoid underpricing or overpricing workers comp policies.

GenAI Among the Workforce: Strategies for Effective Change Management

Many insurance professionals fear GenAI’s growth will reduce their roles or replace them in the workforce. This fear has led to a general resistance to AI technology, especially in firms that have not invested in proper AI education or positioning.

For insurance leaders looking to get started with GenAI but who don’t know how to adopt it without inciting panic, here are a few tips for effective change management:

Frame GenAI as a collaborative tool: It’s important to reassure employees that GenAI will not take their jobs or act as a comprehensive insurance solution. Emphasize how it will supplement their valuable knowledge and experience, allowing them to move through the classification of individual claims and policy preparation quickly and more proactively.

Demonstrate GenAI’s value in a concrete way: Actionable insights lead to confident and informed decisions, so make sure the team knows what GenAI can do and what its limitations are. A live demonstration with Q&A is a great way to illustrate the different ways AI can make their jobs easier and less prone to error.

Provide hands-on training and transparent communication: Clear and frequent communication drives adoption and builds trust across all organizational levels. Employees will feel most empowered if they receive hands-on training for how to use these tools for different tasks, as well as any security or compliance procedures they need to follow while using AI.

The Future of GenAI in Insurance

In this new era of GenAI in insurance, underwriters can access detailed AI insights and solid recommendations to better assess risk and rightsize coverage for their clients. On the claims side, insurance professionals can quickly identify optimal settlement paths based on past performance data. This approach supports insurers at all stages of their career, allowing them to make informed decisions in a fraction of the time it would normally take to analyze and derive insights from their data.

As GenAI continues to develop, insurers looking to maximize its impact should prioritize solutions that integrate multi-source insurance data collections to improve data diversity and model training. Expanding beyond a single company’s historical data to include broader industry-wide insights will lead to more robust predictions and recommendations. This approach not only improves operational efficiency but also positions insurers for long-term success as they expand into new verticals and regions.

Stan Smith, Gradient AI

Stan Smith is the founder and chief executive officer of Gradient AI

This article first appeared on Carrier Management.

Gradient AI enhances workers’ compensation risk scoring solution

Thu, 17 Apr 2025 16:58:25 GMT

April 17, 2025

The upgraded solution offers improvements in risk scoring accuracy, regulatory compliance, and implementation speed, providing a more effective and fairer model for insurers.

Gradient AI, a US-based provider of AI-powered insurance software, has unveiled an enhanced version of its Workers’ Compensation Underwriting Risk Score Solution.

The upgraded solution offers improvements in risk scoring accuracy, regulatory compliance, and implementation speed, providing a more effective and fairer model for insurers.

The advanced AI model, trained on a national dataset comprising tens of billions of dollars in workers’ compensation premiums, enhances risk segmentation.

It allows insurers to price policies more precisely and avoid unprofitable risks.

Also, it aligns with the evolving regulatory standards, incorporating guidance from the National Association of Insurance Commissioners and various state AI circulars.

The new solution significantly reduces implementation times from months to days, eliminating the need for client data during initial setup.

According to early adopters, the solution resulted in a five-point improvement in loss ratios, 80% faster quote turnaround times, and an 86% increase in bound premium.

Unlike traditional models, Gradient AI’s solution integrates job-specific risk scoring, claims data from over 200 carriers and MGAs, and a regulatory-compliant model.

The features offer a market advantage, as they are not present in legacy approaches.

The company is currently offering an enhanced Workers’ Compensation Underwriting Risk Score solution to insurers.

Gradient AI founder and CEO Stan Smith said: “With this release, we’re empowering insurers to underwrite smarter, grow faster, and stay ahead of regulatory scrutiny.

“Our regulatory compliant model and improved accuracy gives carriers the tools they need to make data-driven decisions and grow profitably, while being prepared for future AI regulations.”

In July last year, Gradient AI secured $56.1m in a Series C funding round led by Centana Growth Partners, with participation from existing investors Sandbox Insurtech Ventures, MassMutual Ventures, and Forte Ventures.

The funding aims to support product development, drive innovation, and enhance customer success and sales functions in the insurance industry .

This article first appeared on FDE.

Gradient AI Unveils Next-Generation Workers’ Compensation Underwriting Solution with Enhanced Risk Scoring Designed for Emerging AI Regulations

Wed, 16 Apr 2025 17:08:40 GMT

Advanced AI Model Delivers Enhanced Accuracy and Speed with Improved Compliance for Insurers

BOSTON, MA – April 16, 2025 – Gradient AI , a leading enterprise software provider of artificial intelligence (AI) solutions in the insurance industry, today announced the release of its enhanced Workers’ Compensation Underwriting Risk Score Solution . The new version delivers advances in risk scoring accuracy, regulatory compliance, and implementation speed.

The release also provides an industry leading, risk-segmenting workers’ compensation AI model. Gradient AI designed, trained, and validated its workers’ compensation AI model to ensure fairness using a methodology aligned with evolving regulatory standards, including guidance from the National Association of Insurance Commissioners and various state AI circulars.

Key Innovations

Improved Model Accuracy: The enhanced AI model, trained on a national dataset consisting of tens of billions of dollars in workers’ compensation premiums, along with third-party and proprietary features from Gradient AI’s contributory claims dataset, delivers sharper risk segmentation. This enables insurers to price policies more accurately and avoid unprofitable risks.
Faster Implementation: Built on Gradient AI’s comprehensive dataset, implementation times can now be reduced from months to days, with no dependency on client data for initial setup.
Fairness: The model is designed, trained, and tested to meet regulatory standards, setting a new benchmark for ethical AI in insurance underwriting. For more information, visit the Gradient AI contact page to request the whitepaper on regulatory compliance and ethical AI in underwriting.

Significant Results for Insurers

Early adopters of Gradient AI’s Risk Score Solution have achieved:

5-point improvement in loss ratios
80% faster quote turnaround times
6% increase in bound premium

“With this release, we’re empowering insurers to underwrite smarter, grow faster, and stay ahead of regulatory scrutiny,” said Stan Smith, founder and CEO of Gradient AI. “Our regulatory compliant model and improved accuracy gives carriers the tools they need to make data-driven decisions and grow profitably, while being prepared for future AI regulations.”

Industry-Leading Differentiation

Gradient AI’s solution outperforms traditional and consortium-based models by combining job-specific risk scoring, proprietary claims data from more than 200 carriers and MGAs, and a regulatory compliant model – features absent in legacy approaches.

The enhanced Workers’ Compensation Underwriting Risk Score Solution is available now. Insurers can learn more, schedule a demo, or request a policy scoring assessment here .

About Gradient AI

Gradient AI is a leading provider of proven artificial intelligence (AI) solutions for the insurance industry. Its solutions improve loss ratios and profitability by predicting underwriting and claim risks with greater accuracy, as well as reducing quote turnaround times and claim expenses through intelligent automation. Unlike other solutions that use a limited claims and underwriting dataset, Gradient AI’s software-as-a-service (SaaS) platform leverages a vast industry data lake comprising of tens of millions of policies and claims. It also incorporates numerous other features including economic, health, geographic, and demographic information. Customers include some of the most recognized insurance carriers, MGAs, MGUs, TPAs, risk pools, PEOs, and large self-insured employers across all major lines of insurance. By using Gradient AI's solutions, insurers of all types achieve a better return on risk. To learn more about Gradient AI, visit: https://www.gradientai.com or follow on LinkedIn .

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Elyse Familant

Results PR

elysef@resultspr.net

5 Reasons Stop Loss Carriers, MGUs, and Captives Choose Gradient AI for Underwriting

Thu, 03 Apr 2025 20:20:57 GMT

GROUP HEALTH INSIGHTS BLOG | Nov 13, 2023

The Gradient AI Team

In the competitive world of group health underwriting for stop loss carriers, MGUs, and captives, success hinges on three key factors: speed, accuracy, and pricing. To stay ahead, the ability to assess risk with precision is critical.

What if you could pinpoint valuable opportunities that others overlook, pricing them for success, while managing risk effectively? Imagine significantly reducing quote turnaround times, all while maintaining a healthy loss ratio!

At Gradient AI, we help our Stop Loss, MGU and Captive partners predict group health insurance risk more efficiently and effectively, with our proven AI and machine learning models.

Our Stop Loss, MGU, and Captive Clients Tell Us There are 5 Key Reasons They Choose Gradient AI Over Other Solutions Providers

1. Accuracy. Gradient AI has proven its value time and again. We’ve completed over 150 head-to-head proof-of-concepts against leading competitors and earn our clients’ trust on the basis of these comparisons, because our model allows our clients to price and predict risk with greater precision.

2. Timely Data. We update our data every 30 days, providing clients with the most up-to-date information. Accessing fresh, relevant data delivers greater certainty in pricing and risk projection.

“We chose Gradient AI because, of all the solutions we researched, it made us feel the most comfortable. We felt the Gradient AI solution had equal parts accuracy, with the data to back it up. We increased our overall underwriting production and efficiency this past busy 1/1 quote season. We had no backlog and tripled our production in sold fully insured cases.” - Steve Schroeder, VP for Level Funding, Dallas Risk Management (MGU)

3. Industry-leading match rates. This results from Gradient AI getting a medical, Rx, Rx specialty, and/or lab match on at least 90% of the headcount provided in a census. Superior match rates lead to better, more informed decision making.

4. Innovation. Gradient AI is not just “black box” underwriting, meaning the reasons behind the risk score are not easily explainable or transparent. We’re not just providing a risk score. We’re providing volume of detail around that score which can be applied that to the business case at hand.

For example, among numerous other data points, Gradient AI provides:

Top conditions
Top drugs
High-cost claims reporting
Stop loss risk scores
Condition flags

At Gradient AI, we’re constantly iterating and striving to improve our outputs. We were the first to provide access to medical, lab and prescription data, and the first to provide actionable insights.

“We sought a tool capable of greatly enhancing our risk assessment efforts, especially in areas with limited or no detailed group-specific data. Gradient AI’s SAIL™ solution not only helped us make better-informed decisions but provided us with the critical detail of what is driving that risk. With other competitors’ models, that critical information was missing.” - Jennifer Taylor, Director of Alternative Risk, Evolution Risk Partners (MGU)

5. Great partners . Clients tell us we are easy to work with. Not only from a pricing standpoint, but we make sure we take the time to understand your business, your unique cases, and your unique workflows to build a solution around your goals and challenges. We also understand the potential cost of inaction to your business.

Conclusion: Group Health Underwriting is a High-Stakes Arena Where the Ability to Move Quickly, Assess Risks Accurately, and Offer the Right Pricing Sets Companies Apart

For Stop Loss carriers, MGUs, and Captives , having an innovative AI partner who provides timely, accurate data and industry-leading match rates gives you the clearest picture of risk and the competitive edge you need to grow your business more profitably.

“Gradient AI’s solution is not meant to be a standalone underwriting tool. It is advanced decision support. Conduct your due diligence. Do a pilot. Do a proof of concept. If you want to see where our competitors will be in the future, look at Gradient AI today.” - Matt Weaver, Senior Sales Director, Gradient AI

To learn more about AI underwriting solutions for stop loss carriers, MGUs, and captives contact Matt Weaver , Senior Sales Director, or Jon Russ , Senior Account Executive.

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From Investment to Implementation: Addressing AI Adoption Challenges in Claims Management

Tue, 25 Mar 2025 17:35:36 GMT

P&C INSIGHTS BLOG | Feb 26, 2024

The Gradient AI Team

AI in Claims Management: New Study Reveals a Disconnect Between Investment and Impact

As the property and casualty insurance industry continues to invest heavily in AI, many insurers are still struggling to achieve meaningful results at the operational level — especially in claims. A new research study by Elevate the Narrative titled “From the Front Lines: Claims Adjusters Speak Out,” ¹ exposes a critical gap between insurance executives’ AI ambitions and claims adjusters’ AI usage in day-to-day claims management.

With input from over 1,000 professionals, the report highlights the key challenges and opportunities for insurers seeking to transform their claims operations through AI.

3 Key Findings from the Study: A Candid Look into the Claims Front Lines

The study reveals that while technological investment is on the rise, many claims adjusters remain overwhelmed, under-supported, and disconnected from the tools designed to help them. In this blog post, we summarize 3 key findings from the study, and then suggest ways to address the challenges.

1. Lack of training: 58% say they lack support on new technology, including inadequate training
Frontline claims professionals are expected to adapt quickly to new technology, yet many report receiving little guidance or structured training to support that transition. This gap leaves adjusters feeling unprepared and undervalued, hindering adoption. As AI-driven tools continue to enter the workflow, a proactive approach to training is no longer optional; it’s essential to performance and retention.

Takeaway: These barriers must be addressed to unlock the full potential of AI and digital transformation. Following a successful AI implementation with a client-focused partner, insurers should view training not as a one-time event, but as an ongoing investment in workforce capability. That means offering continuous learning opportunities, practical job-based scenarios, and support channels that reinforce adoption over time.

2. Confidence gap: 58% report low confidence in using their AI tools effectively

The point is closely related to #1 above. The study showed that while 40% of respondents are currently using AI tools for claims processing, a majority report not feeling confident about using the new tools.

Takeaway: There’s a clear disconnect between AI implementation strategies and real-world usage. Insurers must partner with solution providers that deliver AI solutions featuring model explainability, rather than a “black box.” Additionally, insurers need to design and deliver training that builds staff confidence . Confidence directly impacts adoption, efficiency, and overall success of any change initiative. When stakeholders feel capable and comfortable using new technology that they can understand, they’re more likely to embrace the change and apply what they’ve learned effectively. ²

3. Technology frustrations: 65% cite lack of integration between systems

Pain points in the study included lack of, or redundant documentation and outdated platforms that complicate workflows.

Takeaway : Legacy systems and siloed data continue to undermine efficiency and increase friction - further complicating AI rollout efforts. When claims professionals are forced to toggle between multiple platforms or manually input data, it slows productivity and creates opportunities for errors. Insurers must take integration seriously, investing in modern, interoperable solutions that unify the claims ecosystem and provide AI-enablement at the point of decision.

The Path Forward: Align AI Strategy with Claims Frontline Reality, Supported by a Robust Change Management Program

Research shows that for people and process changes to be successful, organizations must prepare, equip, and support their staff, so that they successfully adopt the changes. ³

To bridge the gap between AI investments and practical results, insurers must:

Invest in AI training and onboarding tailored to adjusters' roles
Streamline workflows by modernizing core systems, providing their teams with access to understandable solutions that support their work
Establish a robust change management program

A structured, robust change management program is essential to guide this transformation. A recent study found that 88% of companies with excellent change management programs exceeded expectations, compared to only 13% of those with poor change management programs. As well, effective change management programs reduce common project headaches, such as rework, redesign, and rescoping. ⁴

To help insurers address the people and process challenges that can come with implementing new AI solutions into your claims management operations, Gradient AI has developed a practical framework and checklist for change management success, called 6 Change Management Steps for Successful Adoption of New Technology, such as AI, in Insurance .

This guide outlines practical steps such as:

Pre-planning with the right change agents and communication tools
Conducting a training needs assessment to design role-specific, just-in-time learning
Using stakeholder mapping and feedback loops to tailor communications
Continuously measuring readiness and reinforcing adoption post-launch

These best practices are not just theory — they reflect lessons learned from insurers that have navigated the same adoption challenges you face today. For P&C insurers committed to achieving true operational impact from their AI investments, this guide is a must-read.

DOWNLOAD:

6 Change Management Steps for Successful Adoption of New Technology, such as AI, in Insurance

Elevate the Narrative, From the Front Lines: Claims Adjusters Speak Out , 2025.
Whatfix, Change Management Training in the Workplace , January 16, 2025.
Prosci, What is Change Management, 2025.
Prosci, The Correlation Between Change Management and Project Success , July 31, 2024.

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The AI Advantage: Solving 6 Challenges in Workers' Comp Underwriting

Thu, 20 Mar 2025 13:02:27 GMT

P&C INSIGHTS BLOG | Feb 26, 2024

The Gradient AI Team

Workers' compensation underwriting can be a balancing act — insurers must accurately assess risk, ensure competitive pricing, and maintain profitability while keeping policies accessible for businesses of all sizes. Traditional underwriting processes, often reliant on manual data entry and outdated models, can struggle to keep pace with evolving workplace risks, regulatory changes, and the vast amount of data available today.

Artificial intelligence models are changing the workers’ comp underwriting paradigm by taking on these challenges directly. AI-based solutions allow insurers to increase productivity, make better judgment calls regarding risk, and improve decision-making for the benefit of both the carrier and the policyholder. Work comp underwriting powered by AI gives better outcomes through:

Enhanced risk assessment – AI models analyze vast datasets, identifying patterns that underwriters may overlook, assigning a risk score that provides a more precise risk assessment.

Automated data processing – AI streamlines policy underwriting by extracting insights from unstructured data sources, such as claims history, safety records, and payroll data.

Operational efficiency gains – Automating routine underwriting tasks allows underwriters to focus on high-value decision-making, reducing turnaround times.

In this blog post, we’ll explore how AI is reshaping workers' compensation underwriting, addressing 6 critical challenges, and unlocking new opportunities for insurers to drive profitability while delivering better coverage.

6 Challenges Currently Facing Workers’ Comp Insurers and How AI Addresses these Challenges

Do the challenges below sound familiar? Gradient AI’s Work Comp clients tell us that these are the biggest challenges they face today.

These issues certainly underscore the need for innovative solutions, like AI-driven analytics, to mitigate their impact, and ultimately to enhance underwriting accuracy and efficiency in Workers' Comp.

1. Shifting workforce demographics

An aging workforce may lead to longer recovery times and higher claim severity. Additionally, hybrid and remote work introduce complexities in determining whether an injury occurred during employment. ¹

How AI Helps
AI-powered predictive analytics can assess demographic trends and their impact on risk. Predictive modeling can estimate injury probabilities based on job roles, age distribution, and remote work environments. Underwriters can use AI-driven risk scoring to adjust pricing models dynamically, ensuring that policies accurately reflect emerging workforce risks.

2. Medical inflation

Medical inflation refers to rising costs for medical resources (e.g., physician services, health care facilities and supplies, pharmaceuticals, etc.). These costs comprise the largest share (60%) of workers’ comp expenses, according to the National Library of Medicine. The Centers for Medicare and Medicaid predict healthcare spending will rise by 5.4% each year through 2028, presenting ongoing medical inflation concerns. ²

How AI Helps
AI-driven cost projection models integrate historical claims data, regional medical inflation rates, and provider pricing trends to enhance pricing accuracy. Machine learning algorithms can help underwriters assess potential cost escalation and adjust premium structures accordingly. AI also improves medical provider benchmarking, allowing insurers to tailor underwriting decisions based on provider efficiency and cost-effectiveness.

3. Catastrophic claims and litigation

Catastrophic claims that cost more than $2 million represent less than 0.1% of workers’ compensation claims but account for up to 2% of all costs to insurers, according to a report by the National Council on Compensation Insurance. ³ Related to this, in the Healthe Systems 2025 Workers’ Comp Industry Insights Survey, the insurance professionals surveyed listed Workers’ Comp litigation as the number one challenge in the industry right now. ⁴ How does all of this impact underwriting? High-cost claims add complexity to the underwriting process, as insurers must carefully assess and price the risk of such claims occurring. ⁵

How AI Helps:
AI models analyze historical claims data to identify early indicators of potential catastrophic claims, enabling underwriters to refine risk selection and adjust pricing strategies. Predictive analytics can assess litigation probability by evaluating claimant characteristics, case histories, and legal trends. This allows underwriters to price policies with greater precision and apply exclusions or risk-adjusted premiums where necessary.

4. Pricing pressure

The rise in catastrophic claims and out-sized litigation is creating upward pressure on premiums. However, underwriters must balance this with competitive market forces. The workers' compensation market has seen years of declining rates due to fierce competition, making it challenging to adjust prices adequately to reflect increased risk. ⁶

How AI Helps
AI-enhanced risk modeling helps insurers refine their pricing strategies by incorporating real-time industry data, economic trends, and external risk factors into underwriting decisions , to adjust rates in response to evolving risks while maintaining market competitiveness. AI-powered market intelligence tools provide insights into competitor pricing, helping underwriters fine-tune their pricing models for optimal profitability.

5. Reserve strength and profitability concerns

The increase in catastrophic claims is also contributing to a gradual decline in reserve strength for insurers. This trend, coupled with medical and wage inflation, is putting pressure on underwriting profitability. ⁷

How AI Helps
AI-powered actuarial models improve reserve forecasting by analyzing claims trends, economic conditions, and real-time portfolio risk. Insurers can leverage AI-driven simulations to predict future claims volatility, enabling better financial planning and reserve allocation. AI also enhances underwriting profitability analysis by pinpointing high-risk policy segments and optimizing risk-adjusted pricing.

6. Need for increased operational and technical efficiency

Legacy systems are often ill-equipped to handle the volume and complexities of modern claims data, leading to inefficiencies, delays, and increased administrative costs. ⁸

How AI Helps
AI-driven automation enhances underwriting efficiency by streamlining data ingestion, risk analysis, and decision-making. NLP tools extract insights from vast amounts of structured and unstructured data, improving the speed and accuracy of underwriting assessments. AI can provide underwriters with real-time recommendations, ensuring consistent and data-driven risk evaluation.

Conclusion: AI is Transforming Workers' Comp Underwriting by Enhancing Risk Assessment, Improving Pricing Accuracy, and Driving Operational Efficiency

As insurers face growing challenges — from medical inflation to shifting workforce trends — AI provides a strategic advantage to navigate uncertainty and maintain profitability. By leveraging AI-powered solutions, underwriters can make more informed decisions, optimize pricing strategies, and position their organizations for long-term success in an increasingly complex market.

The Insurer, Workers’ Comp: Preparing for Emerging Challenges After a Decade of Profitability , October 6, 2024.
The Barrow Group, 2025 Workers’ Compensation Insurance Outlook , December 20, 2024.
Business Insurance, Technology helps manage ‘mega claims’ in comp: Experts , December 18, 2024.
Healthe Systems, 2025 Workers’ Comp Industry Insights Survey Report , March 2025.
Insurance Information Institute, Spotlight on: Catastrophes - Insurance issues , February 28, 2025.
The Barrow Group, 2025 Workers’ Compensation Insurance Outlook , December 20, 2024.
Institute of Work Comp Professionals, Five challenges for workers’ comp in 2025 , January 5, 2025.
JobSiteCare, Understanding and Managing Workers’ Compensation Challenges , February 23, 2024.

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AI Is Reshaping Insurance: 6 Trends to Watch

Sun, 16 Mar 2025 16:06:17 GMT

March 16, 2025

By Stan Smith

AI adoption in insurance accelerates as executives embrace real-time analytics and specialized technology for improved operations.

AI adoption in the insurance industry is gaining traction. According to Earnix's recent Industry Trends Report, 70% of insurance executives plan to implement AI models that use real-time data predictions within the next two years — more than double today's adoption rate. It's clear that insurers are increasingly relying on real-time predictive analytics as AI adoption accelerates.

As AI technology advances within the industry, insurers are leveraging both traditional AI, which analyzes data and predicts outcomes, and generative AI, which creates content and explains concepts. As they adopt these technologies, insurers can expect significant improvements across operations.

Here are six trends shaping the future of AI in insurance.

1. AI-Powered Next Best Actions for Underwriting and Claims

Currently, insurers use AI for tasks like processing straightforward data inputs or automatically categorizing claims. In the future, insurers will also leverage AI to provide next best action recommendations for both underwriting and claims management.

"Next best action" recommendations will provide underwriters with specific suggestions such as adjusting coverage limits or gathering additional data sources to better assess risk, based on AI-driven insights. Similarly, for claims adjusters, AI will suggest next best actions such as seeking out additional documentation or identifying an optimal settlement path based on previous claims data. By automating complex decision-making, AI enhances efficiency, consistency, and speed across underwriting and claims operations.

2. Industry-Specific GenAI for Insurance

While many insurance companies have experimented with general generative AI tools, these often fall short when it comes to the industry-specific needs of insurers, like analyzing policy language or understanding regulatory requirements.

As demand grows, insurers will increasingly adopt specialized GenAI models designed specifically for the insurance sector. These advanced tools efficiently interpret intricate policy documents, generate custom pricing models, and draft precise policy language, far beyond the capabilities of general models. This shift enables insurers to better navigate complex regulatory environments and improve their ability to design personalized insurance products, ultimately leading to greater accuracy and customer satisfaction. As technology evolves, insurers will increasingly rely on these specialized solutions to enhance efficiency, streamline operations, and deliver more customized coverage for policyholders.

3. Phased Digital Transformation

Nearly half (49%) of insurers report falling behind in updating legacy systems, according to Earnix's report. While many insurers have attempted to modernize their legacy systems, these efforts often fail due to their complexity and scope. Replacing all core systems at once often overwhelms insurers, leading to costly setbacks.

In the future, insurers will increasingly rely on a phased approach to digital transformation, implementing targeted upgrades instead of complete system overhauls. This approach allows companies to focus on specific areas like customer portals or underwriting modules, integrating AI gradually and minimizing disruption to existing operations. This incremental strategy helps insurers avoid the pitfalls of past modernization attempts while still improving efficiency and competitiveness in the market. The key is to focus on smaller, high-impact projects that deliver immediate value, driving change and building momentum over time.

4. Specialized Data Sources for Smarter Risk Assessment

Insurers today typically rely on broad data sets like historical loss data, demographic information, and general weather reports to assess risk.

In the future, we will see AI models that incorporate more specialized data sources to improve the accuracy of risk assessments. For example, insurers may use climate change information from coastal surveillance or community-based environmental organizations to better assess property or health risks. Health insurers may leverage fitness-related information from smartwatches to differentiate between policyholders who may look the same in a policy application but actually represent vastly different risk profiles (e.g., active triathletes vs. sedentary individuals with significant latent health risks). By integrating precise data into their models, insurers gain a holistic view that enhances risk assessment and policy pricing accuracy.

5. Smarter, Scalable Document Processing

Currently, many insurers use basic document automation for tasks like summarizing brief records or extracting information from structured documents. However, processing large, complex files like medical records still requires significant manual input.

In the future, AI-driven document processing will change how insurers manage documents at scale. Insurers will be able to process thousands of lengthy, multi-format documents efficiently, handling everything from de-duplicating redundant data to categorizing unstructured records. For example, AI will swiftly summarize and organize critical information, eliminating time-consuming, manual reviews for complex claims. This means that claims processing will be faster and more accurate, allowing insurers to manage even higher volumes of claims while reducing error rates and improving customer satisfaction.

6. AI Models That Align With New Regulatory Demands

As insurers expand their use of AI to streamline underwriting and claims management, industry standards for responsible AI use are becoming more uniform across states. Recently, the National Association of Insurance Commissioners (NAIC) adopted a model bulletin outlining compliance requirements for insurers' AI systems. This bulletin clarifies expectations for development, deployment, and documentation of AI technologies to ensure adherence to state and federal laws. So far, at least 11 states, along with Washington, D.C., have issued bulletins incorporating NAIC's language. They are: Alaska, Connecticut, Illinois, Kentucky, Maryland, Nevada, New Hampshire, Pennsylvania, Rhode Island, Vermont, and Washington.

This year, expect to see insurers adopt transparent, AI-driven models that align with both NAIC guidelines and specific state regulations. These models include explainable AI components to ensure clear, auditable insights into predictive processes to meet compliance demands. For insurers operating across state lines, these advancements streamline regulatory adherence and enhance consumer trust by offering transparent, ethical risk assessments. This shift establishes responsible AI use as a foundational standard in the industry.

AI is now an integral part of the insurance industry, helping insurers streamline operations, enhance processes, and navigate regulatory requirements. As its role expands, so does its value, providing deeper insights that support better business decisions. The future of insurance will depend on how effectively companies adopt AI to meet evolving expectations while building customer trust and confidence.

Stan Smith, Gradient AI

Stan Smith is the founder and chief executive officer of Gradient AI

This article first appeared on InsuranceThoughtLeadership.com .

5 Questions with Heather Burgess, Customer Success Manager, Group Health

Fri, 14 Mar 2025 16:44:43 GMT

GROUP HEALTH INSIGHTS BLOG | Nov 13, 2023

The Gradient AI Team

Heather is a Customer Success Manager for Group Health Sales at Gradient AI , where she’s charged with retaining and growing key client accounts. Heather tracks the success of each customer – working with them closely to optimize the value of our AI models and services for them . Leveraging her experience in renewal underwriting, Heather also focuses on Gradient AI’s Renewal Analytics Solution . This solution provides deeper insights into an existing book of business, allowing Gradient clients to project risk with much greater accuracy.

With a decade of medical underwriting experience, Heather has managed diverse cases, from small groups to large-scale customers of 20,000 members. She’s led initiatives to enhance underwriting efficiency and effectiveness, using innovative approaches for continuous improvement. Heather has a bachelor’s degree in marketing from the University of Maryland and a master’s degree in accounting from Georgia State University. She lives in Marietta, Georgia.

Question 1: What are the key factors that led you to pursue this role with Gradient AI?

I’ve always been passionate about the ways that technology can enhance the underwriting experience. In the last three years, I’ve been focused on how AI can have a significant impact on the medical underwriting journey. In the spring of 2024, I attended a webinar, Exploring the Impact of AI on Carrier Health Underwriting: Expert Perspective, where Gradient AI was being touted as a valued solution to the various challenges facing underwriters.

I researched Gradient AI and quickly realized that they not only have amazing AI solutions but also genuinely care about their customers. I knew that was something I wanted to be a part of. And here I am!

Question 2: What is the primary focus of your role as Customer Success Manager, Group Health at Gradient AI?

My focus is on building meaningful relationships with our clients by understanding the unique use case of each and then ensuring they can optimize the full potential of our products. My goal is to see our clients thrive – and their customers and members thrive - once our solutions have been integrated into their workstreams.

Question 3: What unique perspective and skills do you bring to Gradient AI’s customers?

For over 10 years, I’ve sat “in our clients’ shoes,” working in group health medical underwriting as tech solutions have been built and released. I joke with my peers that I remember when insurance quoting was done on the back of a napkin and then passed to the underwriter to fit it into an Excel spreadsheet! We’ve come a long way! I’ve incorporated these tech solutions into workflows and am a huge champion of these changes – while fully understanding that medical underwriters are a critical part of the process and will always be needed.

Question 4: What are two common misconceptions about AI in Group Health underwriting?

Misconception 1: AI solutions will replace humans .
This is simply not true. No tech solution can know the intricacies of the relationships involved in underwriting, which is much more than a mathematical equation. I always say that the formula is the ‘pure science’ component of underwriting. Actuaries or pricing departments set it and typically yield a return. But what makes underwriting so special is that it’s the ‘art component’ that takes the formula and adds the necessary color and insight that is needed so that both parties are happy with the results . AI solutions are part of the art process of underwriting and help add precision and nuance where the formula or science part tends to fall flat.

Misconception 2: AI should only be used for pricing .
There are so many other ways to utilize the data presented by integrating claims information into AI. Care management is a big one that comes to my mind. For example, if you have a member who is just starting treatment for a high-risk condition, being able to reach out at the onset rather than in the middle of their treatment can go a long way and, in some cases, could change the outcome in a positive direction.

Question 5: What do you like to do in your free time?

I spend time with my family, and we try to go on a hike at least once a month. I also have a blonde German Shepherd who takes up much of my time! I enjoy riding my Peloton and watching documentaries when I’m not chasing my daughter around the pool or my dog around the yard.

Bonus Question: What is your favorite dish to cook and why?

I love to cook for my family and cook 6 nights out of the week (everyone deserves at least one day off). My favorite is the dish I make every Christmas. I do a garlic butter standing rib roast with roasted Brussels sprouts. But the star of this meal is my confit garlic roasted potatoes. While they’re super easy to make, they take over two hours – but are worth every minute! I serve them with a sour cream and chives dipping sauce.

Thank you, Heather! (And that holiday dish sounds delicious!) Be sure to check back on our blog as we ask 5 questions to different members of our talented staff across Gradient AI, to learn how they support the success of our clients.

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Enhancing Underwriting Precision: AI-Powered Solutions for Pooled Risk Groups

Wed, 26 Feb 2025 17:58:10 GMT

GROUP HEALTH INSIGHTS BLOG | Dec 20, 2023

The Gradient AI Team

Pooled risk groups such as associations, trusts, and Multiple Employer Welfare Arrangements (MEWAs) face ongoing challenges in risk management and underwriting. As the healthcare landscape evolves, the ability to assess and manage risk effectively has become increasingly important.

Key Underwriting Challenges for Pooled Risk Groups

Group health underwriting for pooled risk groups presents several complexities, including:

Augmenting risk management: Ensuring sustainable coverage while accounting for varying risk levels within a diverse pool
Handling data complexity: Managing large volumes of health-related data to improve decision-making and maintain regulatory compliance
Balancing growth and affordability: Striking a balance between financial sustainability and offering competitive, accessible health plans
Improving operational efficiency: Streamlining processes to improve accuracy and reduce administrative burdens

Addressing these issues requires a data-driven approach to risk assessment — leveraging AI and predictive modeling — to allow pooled risk groups to make more informed decisions.

Recommendation: Leverage Data for Better Risk Assessment

Effective underwriting relies on the ability to identify and price risk with accuracy. Advancements in predictive analytics, AI, and machine learning are enhancing the ability to distinguish between different levels of risk within a group. These tools can help:

Improve pricing accuracy
Reduce underwriting times
Enhance risk management strategies

To manage new and renewing business effectively, pooled risk groups should utilize data-driven predictive models that analyze a broad range of health-related data like historical medical and pharmacy claims.

But just how to do this?

Gradient AI Offers AI-Powered Underwriting Solutions to Manage New and Renewing Business More Effectively

Gradient AI offers two advanced AI-driven solutions designed to help pooled risk groups improve underwriting precision, manage risk more effectively, and optimize business performance.

1. SAIL™ for New Business

Enhancing risk assessment capabilities is critical for pooled risk groups seeking to price policies accurately and competitively. SAIL™ for New Business leverages:

Machine learning models trained on a vast dataset of medical, prescription, and lab data to generate predictive insights
Advanced risk identification that enables insurers to assess risk with unprecedented speed and accuracy
Enhanced pricing strategies to optimize profitability while managing risk effectively

By using SAIL™, underwriters gain a more comprehensive view of risk, helping them:

Price policies with greater accuracy and confidence
Expand market reach by streamlining underwriting processes and reducing quote turnaround times
Improve risk management with deeper insights into group health trends and loss ratios

2. Renewal Analytics Solution for Renewal Business

Managing renewals efficiently is essential for maintaining financial stability in pooled risk groups. The Renewal Analytics solution is designed specifically to help:

Analyze and manage risk for existing groups, both at an individual level and across an entire block of business
Support data-driven decision-making by providing insights into health plan performance and cost drivers
Optimize renewal strategies with predictive modeling and intelligent risk segmentation

Three Core Components of the Renewal Analytics Solution:

Renewal Predictive AI Model
- Forecasts risk trends for renewal underwriting
- Identifies emerging risks early to support proactive decision-making
- Uses historical medical and pharmacy claims data to predict risk for the next coverage period
Analytics and Reporting Dashboards
- Provides detailed visibility into claims trends, enrollment patterns, and performance metrics
- Offers insights into key factors such as industry, geography, and demographics
- Identifies high-cost claimants and underlying cost drivers to guide risk management strategies
Rebanding Calculator
- Assists in adjusting group-assigned risk bands using predictive insights
- Integrates client-specific inputs and business rules for more tailored renewal pricing

Harnessing the power of AI enables pooled risk groups to underwrite with greater precision, streamline operations, and gain a competitive advantage in an increasingly complex market.

Case Study in Data-Driven Underwriting: North Carolina League of Municipalities

The North Carolina League of Municipalities (NCLM), a nonprofit organization that administers several self-insured pools including the Health Benefits Trust which provides benefits to North Carolina local governments, adopted a data-driven approach to underwriting. NCLM successfully implemented Gradient AI’s SAIL™ solution to enhance its underwriting processes and decision-making capabilities. By analyzing a broader set of health-related data, NCLM was able to refine its risk assessment strategies, enabling it to differentiate between groups with similar characteristics but varying levels of risk.

According to Youssou Fall, Director of Strategic Operations at NCLM, Gradient AI’s AI-powered tools have allowed for better premium adjustments, leading to more sustainable underwriting decisions. By integrating comprehensive analytics into their renewal process, NCLM has gained a clearer understanding of loss ratios and other performance indicators.

“Having good KPIs and predictive models has been instrumental in refining our underwriting approach. We can now override and adjust premiums for riskier groups and offer more competitive rates for healthier groups. This precision pricing helps us attract and retain more profitable business while mitigating potential losses by pricing policies commensurate with each group’s unique risk.” - Youssou Fall, Director of Strategic Operations, NCLM

Conclusion: By Leveraging AI, Pooled Risk Groups Can Improve Underwriting Accuracy, Enhance Decision-Making, and Better Manage Risk

As pooled risk groups navigate the complexities of group health underwriting, AI-powered approaches are becoming essential. A structured, data-informed approach to underwriting and renewal management can support long-term sustainability while ensuring access to fair and competitive health coverage options for members.

Unlock the Power of AI-Driven Underwriting with Gradient AI. Contact us today to learn how we can help your pooled risk group achieve greater accuracy and efficiency.

READ THE SOLUTIONS BRIEF:

Gradient AI's SAIL™ Predictive Analytics Solution for Pooled Risk Groups (Associations, Trusts, and MEWAs)

Explore the benefits of Gradient AI's Group Health Solutions.

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AI-Powered Underwriting and Claims Can Address the ‘Silver Tsunami’ in Insurance

Mon, 24 Feb 2025 14:03:49 GMT

P&C INSIGHTS BLOG | Feb 26, 2024

The Gradient AI Team

In an Industry Where Human Expertise is the Backbone of Operations, How Can Insurers Sustain Growth While Overcoming a Widening Talent Gap?

The Property and Casualty insurance industry is at a crossroads. On one hand, it continues to experience strong financial growth, with direct premiums written rising nearly 10% in 2024, following a 10.4% increase in 2023. And AM Best projects a 7.3% increase in net premiums written in 2025. ¹

On the other hand, insurers are facing more difficulties in hiring and retaining talent, particularly in underwriting and claims management. Experienced underwriters and claims adjusters are retiring at a significant rate ² , and they’re creating a talent and skills gap. Sometimes dubbed the “Silver Tsunami”, ³ this wave of retirements threatens to leave insurers struggling to maintain talent in an industry where nuanced decision-making is critical.

As insurers look to address this skilled labor shortage, AI-powered underwriting and claims management solutions are emerging as key tools to support efficiency, consistency, and knowledge transfer.

Just over two years ago, Gradient AI conducted a webinar , featuring a panel of industry practitioners, which looked into industry labor trends and the challenges insurers were facing at the time to recruit and retain talented employees. The panelists discussed the AI solutions they were considering in order to bridge the talent gap. As it turns out, this insurance workforce issue has only grown bigger.

In this post, we explore the P&C insurance industry talent gap issue in 2025 and dive into potential AI-based solutions.

The P&C Insurance Talent Shortage: A Growing Concern

Recruitment and retention have become critical pain points for insurers. Recent studies have found that 52% of P&C insurance carriers plan to expand their workforce in the next 12 months, driven primarily by anticipated business growth. ⁴ However, hiring is becoming increasingly difficult, with 48% of companies reporting greater challenges in recruitment compared to prior years. ⁵

One of the key drivers behind the talent shortage is the aging demographic of the insurance workforce. The median age of insurance employees is significantly higher than in other financial sectors, meaning a wave of retirements is imminent:

Over the next 15 years, 50% of the current insurance workforce is expected to retire , leaving more than 400,000 positions unfilled ⁶
The number of insurance professionals aged 55 and older has increased 74% over the past decade
Only 27% of insurance professionals are under the age of 35 , highlighting the industry's struggle to attract young talent ⁷

As veteran employees retire, they take with them decades of institutional knowledge. The insurance industry relies heavily on human expertise for underwriting, claims adjudication, and risk assessment. Losing this expertise could have significant financial and operational consequences.

On the other end of the spectrum, although the demand for experienced underwriters and claims adjusters remains high, fewer professionals are entering the field. Millennials are showing little interest in working for insurance and insurance-related organizations. The Hartford’s 2015 Millennial Leadership Survey found that just 4% of millennials were considering a career in insurance. ⁸ Another survey conducted by Vertafore showed a drop over the past 4 years of insurance pros who would recommend a career in the industry to a friend (from 85% in 2021 to 65% in 2024). ⁹

AI May Be the Key to Retaining Institutional Knowledge and Enhancing Productivity

With a shrinking workforce and a widening experience gap, insurers should consider leveraging AI tools to bridge the divide. AI-powered underwriting and claims solutions can help capture, retain, and transfer institutional knowledge, ensuring that new employees can quickly develop expertise while minimizing operational disruptions.

Here’s how AI can help insurers address the talent crisis:

Capturing institutional knowledge. AI can analyze historical underwriting decisions and codify best practices, preserving expertise even as senior underwriters retire.
Enhancing decision support. Machine learning models provide real-time risk analysis, enabling newer underwriters to make informed decisions with greater confidence.
Providing additional insight and guardrails for underwriters and adjusters . By identifying potential actions and providing rationale, AI can act as an on-demand mentor, enhancing decision-making for novice adjusters. For underwriters, AI can suggest pricing adjustments based on nuanced factors like regional crime rates or industry-specific risks, supporting greater overall accuracy.
Improving operational efficiency. AI-driven underwriting and claims tools can automate routine tasks, allowing insurance pros to focus on complex cases that require human judgment.

Let’s consider a specific example of a new underwriter entering the industry. Traditionally, it would take years of experience reviewing applications, pricing policies, and analyzing loss ratios before they could become highly proficient.

AI accelerates this learning process by analyzing vast datasets —AI models ingest millions of past policies, claims, and outcomes to identify patterns and trends. While a seasoned underwriter may review thousands of policy applications in their career and remember only a handful, an AI model can analyze tens of millions of historical policies, learning from every single one. In short, AI replicates human learning but at an exponentially greater scale.

The Real-World Impact of AI

Some insurers are already leveraging AI to address talent shortages. For example, at Builders Mutual Insurance , AI is transforming how new adjusters are trained. Ken Bunn, VP of Claims at Builders Mutual , explained:

“Previously, new adjusters had to sit beside an experienced adjuster for years to observe claims handling. Now, thanks to integrated AI solutions, our newest adjusters can learn quickly and have guardrails in place as they make decisions. AI captures all the experience that could otherwise walk out the door and be lost. Instead, it’s retained in our models for future generations of adjusters.”

AI solutions allow insurers to train new employees more effectively, reduce errors, and enhance decision-making—providing a “virtual tap on the shoulder” when needed—without requiring new professionals to have years of on-the-job experience.

Conclusion: The Future of Insurance Will Be a Hybrid of AI-Driven Intelligence and Human Expertise

The P&C insurance industry is standing at a pivotal moment. With a growing percentage of employees approaching retirement, recruiting and retaining skilled professionals has become a critical challenge .

Fortunately, by integrating AI into underwriting and claims management, insurers can:

Retain institutional knowledge despite retirements
Enhance employee productivity and decision-making
Reduce training time for new hires
Improve profitability through better risk assessment and automation

According to a global survey conducted by research firm PwC, 54% of CEOs in the insurance and reinsurance sector have in fact observed improvements in employee efficiency thanks to AI and generative AI. ¹⁰

The future of the P&C insurance workforce will be a hybrid of AI-driven intelligence and human expertis e . While AI cannot replace human expertise, it can serve as a co-pilot , helping insurers navigate the changing workforce landscape. Those who embrace this shift will not only overcome current labor challenges but also position themselves for long-term success.

Are You Ready to Leverage AI for Your Workforce Challenges?

Gradient AI helps insurers harness the power of AI to enhance underwriting, claims management, and operational efficiency. Contact us today to learn how AI can future-proof your workforce and drive long-term success.

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Atlas Magazine, Generative AI in the insurance and reinsurance sector , February 2025.

Explore the benefits of Gradient AI's P&C or Workers' Comp Solutions.

Schedule a Demo →

NCLM Selects Gradient AI to Enhance Underwriting and Decision-Making

Thu, 20 Feb 2025 21:19:14 GMT

February 20, 2025

Member-Driven Organization Gains Competitive Advantage in Assessing Group Health Risk and Optimizing Member Benefits

Gradient AI, a leading enterprise software provider of artificial intelligence (AI) solutions in the insurance industry, today announced that North Carolina League of Municipalities(NCLM) has successfully implemented Gradient AI’s SAIL™ solution to enhance its underwriting processes and decision-making capabilities.

NCLM is a nonprofit organization that administers several self-insured pools, including the Health Benefits Trust, which provides benefits to North Carolina local governments.

Powered by advanced machine learning algorithms and a comprehensive dataset that includes medical, prescription, and lab data, Gradient AI’s SAIL delivers enriched risk insights. This enhanced visibility enables NCLM to distinguish between groups of similar size but with vastly different risk profiles—an analysis its previous statistical models could not provide.

“Having good KPIs and predictive models has been instrumental in refining our underwriting approach,” said NCLM’s Director of Strategic Operations, Youssou Fall. “We can now override and adjust premiums for riskier groups and offer more competitive rates for healthier groups. This precision pricing helps us attract and retain more profitable business while mitigating potential losses by pricing policies commensurate with each group’s unique risk.”

Gradient AI’s solution has significantly improved NCLM’s operational efficiency and decision-making speed. Fall further explained how SAIL’s intuitive dashboard enables his team to quickly assess loss ratios and other key performance metrics, helping them become more data-driven and confident in their decisions, particularly during renewal season and in managing groups with varying benefit cycles.

“We’re proud to support NCLM in achieving its goal of making more precise, data-centric decisions in underwriting and risk management,” said Stan Smith, founder and CEO of Gradient AI. “As a result, the organization can more accurately assess risk profiles and tailor member benefits. This is an important step in optimizing underwriting strategies to better align with the diverse needs of its members.”

This article first appeared on FinTecBuzz.

AI Trends Shaping the Future of PEOs

Mon, 03 Feb 2025 15:49:51 GMT

February, 2025

By Stan Smith

As both traditional AI and generative AI technologies evolve, the insurance industry will see some tremendous changes, which will impact both insurers and PEOs. By leveraging AI, these organizations can address critical challenges, streamline operations and enhance their service offerings.

Here are six key predictions specific to the opportunities they present for PEOs.

AI WILL DRIVE NEXT BEST ACTION RECOMMENDATIONS IN UNDERWRITING AND CLAIMS MANAGEMENT

Managing workers’ compensation and employee benefits on behalf of their clients can be time intensive and complex for PEOs. This year we will see more PEOs leverage AI to provide next best action recommendations for both underwriting and claims management. When underwriting, ‘next best action’ recommendations provide PEOs with specific suggestions to adjust coverage limits or additional data sources to better assess risk, based on AI-driven insights. Similarly, when overseeing claims, AI can suggest specific next best actions such as seeking additional documentation or identifying the optimal settlement path based on previous claims data. These advanced insights will streamline decision making, reduce resolution times and create a more seamless client experience.

SPECIALIZED GENAI WILL BECOME COMMONPLACE IN INSURANCE

PEOs are continually challenged to keep up with a multitude of evolving state and federal employment laws, joint-employer regulations, paid family leave, and minimum wage requirements. AI tools and their capabilities deliver tremendous benefits, yet they may fall short when it comes to addressing industry-specific needs, like analyzing policy language or understanding regulatory requirements.

As demand grows, purpose-built Generative AI models will evolve to produce increasingly specialized tools that can better interpret nuanced insurance data. These tools will be tailored to analyze intricate policy documents and draft tailored policy language, making them far more effective than general-purpose AI. This shift will enable PEOs to better navigate complex regulatory environments and improve their ability to design personalized insurance products to align with the diverse needs of their clients.

PEOS WILL PHASE IN DIGITAL TRANSFORMATION TO AVOID LEGACY SYSTEM OVERHAUL FAILURES

Many PEOs operating with legacy HR, payroll, and benefits management systems know they need to modernize these systems to stay competitive. However, attempting to replace all core systems simultaneously can become unmanageable, leading to delays and costly setbacks.

In 2025, we will see PEOs take a phased approach to digital transformation, implementing targeted upgrades rather than complete system overhauls. This approach will allow them to focus on specific areas like customer portals or underwriting modules, integrating AI gradually and minimizing disruption to existing operations. This incremental strategy delivers immediate value while ensuring continuity of operations for both PEOs and their clients.

SPECIALIZED DATA SOURCES WILL INCREASE INSURANCE RISK ASSESSMENT ACCURACY

A PEO’s ability to provide competitive employee benefits policies depends significantly on having accurate risk assessment. Insurers typically use established data sets like historical loss data, demographic information, and general weather reports to assess risk. Current AI models handle broad categories of risk—they rarely leverage niche data sources.

In 2025, PEOs will start to integrate specialized data sources, such as climate and health data to enhance risk assessment and underwriting for deeper insights. For example, tapping into health data, like fitness-related information from smartwatches, can help PEOs differentiate between policyholders who may look the same in a policy application but actually represent vastly different risk profiles (e.g., active triathletes vs. sedentary individuals with significant latent health risks). This specialized data integration will provide insurers with a holistic view, enabling more accurate policy pricing and claims assessments.

AUTOMATED INSURANCE DOCUMENT PROCESSING WILL BECOME SMARTER AND MORE SCALABLE

PEOs may face challenges in managing tremendous amounts of paperwork, including employee records, payroll documentation, and benefits contracts. In 2025, advancements in automated document processing will enable PEOs to manage these documents faster and with better accuracy.

They will be able to process thousands of lengthy, multi-format documents efficiently, handling everything from de-duplicating redundant data to categorizing unstructured records. For example, claims involving extensive medical histories will no longer require tedious manual reviews, as automated systems will quickly summarize and catalog pertinent details across hundreds of pages. This means that claims processing will be faster, cheaper, and more accurate, allowing PEOs to manage even higher volumes of claims while reducing error rates and improving customer satisfaction.

AI MODELS WILL ALIGN WITH NEW REGULATORY DEMANDS

Regulatory compliance is a high priority for PEOs, especially when managing client employee benefits and workers’ compensation programs across multiple states. Recently, the National Association of Insurance Commissioners (NAIC) adopted a model bulletin   outlining compliance requirements for insurers’ AI systems. This bulletin clarifies expectations for development, deployment, and documentation of AI technologies to ensure adherence to state and federal laws.

In 2025, PEOs will begin to adopt transparent, AI-driven models that align with both NAIC guidelines and specific state regulations. These models will also include explainable AI components to ensure clear, auditable insights into predictive processes to meet compliance demands. For PEOs operating across state lines, these advancements will streamline regulatory adherence and enhance consumer trust by offering transparent, ethical risk assessments. This shift will establish responsible AI use as a foundational standard in the industry, reshaping the role of predictive analytics and data usage in insurance.

PEOs that adopt both traditional and generative AI technologies are positioning themselves to drive meaningful change, unlock new growth opportunities, and deliver greater value to their small and midsized business customers.

Stan Smith, Gradient AI

Stan Smith is the founder and chief executive officer of Gradient AI

This article first appeared on PEO Insider.

AI Underwriting: Enhancing Risk Management for Employee Benefits Providers

Wed, 29 Jan 2025 16:27:18 GMT

January 29, 2025

By Stan Smith

Gradient AI empowers agencies like Michigan Planners to deliver tailored, cost-effective healthcare plans through predictive insights and proactive risk management.

As healthcare costs continue to rise, employee benefits agencies are experiencing increasing pressure to deliver customized, cost-effective health plans for employer groups. Michigan Planners, a full-service employee benefits agency specializing in group health solutions addressed this challenge head-on as they expanded their offerings to employer groups of varying sizes.

To meet these demands and to evolve its business model, Michigan Planners implemented an AI underwriting solution to gain deeper insights into risk and improve the quality of their group health offerings. By leveraging AI-driven insights, Michigan Planners has been able to completely change the way the company designs health plans, providing more personalized and cost-effective solutions to their clients.

Challenge: Limitations in Traditional Risk Assessment
Like many agencies, Michigan Planners struggled with constraints when trying to assess risk, especially for employer groups in the range of 25-500 lives. Limited bandwidth and access to adequate data meant that most of their decisions were based on historical claims information, providing only a backward-looking view of risk. This left them unable to offer forward-looking insights that could help employers better understand and predict future healthcare costs.

“As an agency, we had access to claims data, but we were essentially driving while looking through the rear-view mirror,” explains Aaron MacDonald, chief strategy officer at Michigan Planners. “This made it difficult to tailor plans or prepare for high-cost claims in the future.”

Solution: AI Enhanced Underwriting
AI is completely changing the underwriting process by automating complex tasks and delivering advanced insights into risk evaluation. Advanced algorithms can analyze massive datasets at speeds unmatched by humans, uncovering subtle patterns and correlations that traditional methods often miss.

Michigan Planners chose Gradient AI’s SAIL as its AI enhanced underwriting solution and it its empowering Michigan Planners to move from a reactive approach to a proactive one. The solution provides predictive insights that allow for more precise risk assessments. It also allows the underwriters to analyze vast amounts of data including historical claims data, to identify patterns and trends that were previously undetectable. Michigan Planners can also tailor plans according to the unique needs of each employer group, improving pricing accuracy and delivering better outcomes for both insurers and the policyholders.

“Thanks to SAIL, we can look through the windshield instead,” says MacDonald. “We have a much better idea of what’s coming and can tailor our health solutions accordingly. In fact, the speed and depth of insights we now have access to have more than paid for itself.”

AI-Driven Insights for Better Decision-Making
One of the key advantages of Gradient AI’s solution is its ability to streamline data analysis. Michigan Planners can now access easy-to-understand reports that provide comprehensive views of risk, without requiring additional staff or complex data analytics expertise. This allows their team to spend less time crunching numbers and more time engaging in strategic conversations with clients.

Other AI-driven insights has helped Michigan Planners address some of the most pressing issues challenging employer groups today. For example, in the case of a rapidly growing client following an acquisition, SAIL identified that self-funding was not a viable option. This discovery saved the client tens of thousands of dollars and prevented them from taking on unnecessary financial risk.

In addition, AI empowers Michigan Planners to provide better risk projections for clients dealing with high employee turnover or companies involved in mergers and acquisitions—common scenarios where historical data may be lacking or insufficient.

Custom Solutions That Save Clients Money
Michigan Planners now has the tools to offer highly customized health plans, which have led to significant cost savings for their clients. The AI-driven insights not only improve decision-making but also enable smaller employer groups, which traditionally lack the resources for detailed claims tracking, to make more informed decisions regarding their healthcare offerings.

For example, when working with smaller clients, Michigan Planners is now better equipped to offer insights into the pros and cons of high-deductible health plans and self-funding options. This level of customization helps employers optimize their healthcare offerings, ultimately leading to better financial outcomes for both the employer and employees.

The Future of Employee Benefits
As AI continues to reshape industries, employee benefits agencies like Michigan Planners are leveraging these new tools to offer more tailored solutions that meet the unique needs of their clients. The adoption of Gradient AI’s SAIL solution is just one example of how forward-thinking agencies are using technology to enhance their risk management processes and improve outcomes for employers and employees alike.

“ Michigan Planners is a great example of what’s possible when you combine deep industry knowledge with cutting-edge AI technology,” said MacDonald. “By enhancing our ability to forecast risk more accurately, we are creating more meaningful and cost-effective benefits programs for our clients.”

As healthcare costs continue to rise and businesses are restricted by tighter budgets, the ability to predict and manage risk will become even more critical. With the help of AI-driven solutions, agencies can continue to adapt and deliver smarter, more affordable healthcare plans to employers across the country.

Stan Smith, Gradient AI

Stan Smith is the founder and chief executive officer of Gradient AI

This article first appeared on FinTecBuzz.

Evolution Risk Partners Boosts Risk Projection Capabilities With Gradient AI

Wed, 08 Jan 2025 19:46:21 GMT

January 8, 2024

Gradient AI, a leading enterprise software provider of artificial intelligence (AI) solutions in the insurance industry, today announced that Evolution Risk Partners has successfully adopted Gradient AI’s SAIL TM underwriting solution to drive more accurate and informed decision-making for its customers in the self-funded space. SAIL enables Evolution Risk Partners to refine risk assessment, leading to more precise pricing and improved financial results.

Evolution Risk Partners, a Managing General Underwriter, partners with employers and brokers to provide customizable and innovative medical stop-loss solutions for self-funded medical programs.

Driven by advanced machine learning algorithms and a rich dataset encompassing medical, prescription, and lab data, Gradient AI’s SAIL provides deeper insights into risk. This allows Evolution Risk Partners to address the complex challenges of clients, particularly those that are fully insured or undergoing mergers and acquisitions, where data is often limited.

‘We sought a tool capable of greatly enhancing our risk assessment efforts, especially in areas with limited or no detailed group-specific data,” said Director of Alternative Risk Jennifer Taylor . “SAIL has not only helped us to make quicker, better-informed decisions on risk, but provided us with the detail of what is driving that risk. With other models, that critical information was missing.”

Access to more data and a deeper understanding of risk fueled profitable growth for Evolution Risk Partners, enabling the company to confidently quote on a larger portion of brokers’ business. “SAIL allows us to expedite underwriting decisions and provide guidance on the most challenging cases, which makes us more valuable to our brokers,” said George Lemmon, founder and CEO . “We are able to partner on a much bigger piece of their world and provide a stronger, more consultative approach to our brokers and our customers, which is priceless.”

“We’re excited to be working with Evolution Risk Partners and support its efforts to enhance risk assessment and underwriting in the alternative risk space,” said Stan Smith, founder and CEO of Gradient AI . “By leveraging Gradient AI’s advanced capabilities, Evolution Risk Partners now has the tools necessary to enhance its risk assessment and underwriting process. This allows the company to make more informed, data-driven decisions and support its continued growth and success.”

People In This Post

This article first appeared on Fintech Finance News .

Evolution Risk Partners Boosts Risk Projection Capabilities with Gradient AI

Tue, 07 Jan 2025 19:46:37 GMT

January 7, 2025

The MGU fills gaps in data and claims with AI-driven insights to offer tailored solutions for customers seeking alternative funding options.

By Anthony R. O’Donnell

Evolution Risk Partners (Evanston, Ill.), a Managing General Underwriter providing a variety of stop loss offerings, including captives, pooled risk programs, and level-funded products, has adopted Gradient AI ’s (Boston) SAIL underwriting solution to drive more accurate and informed decision-making for its customers in the self-funded space. SAIL enables Evolution Risk Partners to refine risk assessment, leading to more precise pricing and improved financial results, according to a Gradient AI statement.

Evolution Risk Partners partners with employers and brokers to provide customizable and innovative medical stop-loss solutions for self-funded medical programs.

Gradient AI describes SAIL as driven by advanced machine learning algorithms and a rich dataset encompassing medical, prescription, and lab data to provide deeper insights into risk. The vendor says this allows Evolution Risk Partners to address the complex challenges of clients, particularly those that are fully insured or undergoing mergers and acquisitions, where data is often limited.

“We sought a tool capable of greatly enhancing our risk assessment efforts, especially in areas with limited or no detailed group-specific data,” comments Jennifer Taylor, Director of Alternative Risk, Evolution Risk Partners. “SAIL has not only helped us to make quicker, better-informed decisions on risk, but provided us with the detail of what is driving that risk. With other models, that critical information was missing.”

Access to more data and a deeper understanding of risk has fueled profitable growth for Evolution Risk Partners, enabling the company to confidently quote on a larger portion of brokers’ business, according to the Gradient AI statement.

Stronger, More Consultative Approach

“SAIL allows us to expedite underwriting decisions and provide guidance on the most challenging cases, which makes us more valuable to our brokers,” comments George Lemmon, founder and CEO, Evolution Risk Partners. “We are able to partner on a much bigger piece of their world and provide a stronger, more consultative approach to our brokers and our customers, which is priceless.”

“We’re excited to be working with Evolution Risk Partners and support its efforts to enhance risk assessment and underwriting in the alternative risk space,” comments Stan Smith, founder and CEO of Gradient AI. “By leveraging Gradient AI’s advanced capabilities, Evolution Risk Partners now has the tools necessary to enhance its risk assessment and underwriting process. This allows the company to make more informed, data-driven decisions and support its continued growth and success.”

This article first appeared on Insurance Innovation Reporter .

Evolution Risk Partners Boosts Risk Projection Capabilities with Gradient AI

Tue, 07 Jan 2025 19:45:49 GMT

January 7, 2024

MGU Fills in Gaps in Data and Claims with AI-Driven insights to Offer Tailored Solutions for Customers Seeking Alternative Funding Options

Gradient AI , a leading enterprise software provider of artificial intelligence (AI) solutions in the insurance industry, today announced that Evolution Risk Partners has successfully adopted Gradient AI’s SAIL™ underwriting solution to drive more accurate and informed decision-making for its customers in the self-funded space. SAIL enables Evolution Risk Partners to refine risk assessment, leading to more precise pricing and improved financial results.

Evolution Risk Partners, a Managing General Underwriter, partners with employers and brokers to provide customizable and innovative medical stop-loss solutions for self-funded medical programs.

“We sought a tool capable of greatly enhancing our risk assessment efforts, especially in areas with limited or no detailed group-specific data,” said Director of Alternative Risk Jennifer Taylor. “SAIL has not only helped us to make quicker, better-informed decisions on risk, but provided us with the detail of what is driving that risk. With other models, that critical information was missing.”

Access to more data and a deeper understanding of risk fueled profitable growth for Evolution Risk Partners, enabling the company to confidently quote on a larger portion of brokers’ business. “SAIL allows us to expedite underwriting decisions and provide guidance on the most challenging cases, which makes us more valuable to our brokers,” said George Lemmon, founder and CEO. “We are able to partner on a much bigger piece of their world and provide a stronger, more consultative approach to our brokers and our customers, which is priceless.”

“We’re excited to be working with Evolution Risk Partners and support its efforts to enhance risk assessment and underwriting in the alternative risk space,” said Stan Smith, founder and CEO of Gradient AI. “By leveraging Gradient AI’s advanced capabilities, Evolution Risk Partners now has the tools necessary to enhance its risk assessment and underwriting process. This allows the company to make more informed, data-driven decisions and support its continued growth and success.”

This article first appeared on GlobalFinTechSeries .

Evolution Risk Partners Boosts Risk Projection Capabilities with Gradient AI

Tue, 07 Jan 2025 14:09:56 GMT

MGU Fills in Gaps in Data and Claims with AI-Driven insights to Offer Tailored Solutions for Customers Seeking Alternative Funding Options

Boston, MA January 7, 2025 – Gradient AI, a leading enterprise software provider of artificial intelligence (AI) solutions in the insurance industry, today announced that Evolution Risk Partners has successfully adopted Gradient AI’s SAIL ™ underwriting solution to drive more accurate and informed decision-making for its customers in the self-funded space. SAIL enables Evolution Risk Partners to refine risk assessment, leading to more precise pricing and improved financial results.

Evolution Risk Partners, a Managing General Underwriter, partners with employers and brokers to provide customizable and innovative medical stop-loss solutions for self-funded medical programs.

‘We sought a tool capable of greatly enhancing our risk assessment efforts, especially in areas with limited or no detailed group-specific data,” said Director of Alternative Risk Jennifer Taylor. “SAIL has not only helped us to make quicker, better-informed decisions on risk, but provided us with the detail of what is driving that risk. With other models, that critical information was missing.”

Access to more data and a deeper understanding of risk fueled profitable growth for Evolution Risk Partners, enabling the company to confidently quote on a larger portion of brokers’ business. “SAIL allows us to expedite underwriting decisions and provide guidance on the most challenging cases, which makes us more valuable to our brokers,” said George Lemmon, founder and CEO. “We are able to partner on a much bigger piece of their world and provide a stronger, more consultative approach to our brokers and our customers, which is priceless.”

“We’re excited to be working with Evolution Risk Partners and support its efforts to enhance risk assessment and underwriting in the alternative risk space,” said Stan Smith, founder and CEO of Gradient AI. “By leveraging Gradient AI’s advanced capabilities, Evolution Risk Partners now has the tools necessary to enhance its risk assessment and underwriting process. This allows the company to make more informed, data-driven decisions and support its continued growth and success.”

About Evolution Risk Partners

Evolution Risk Partners, a Managing General Underwriter headquartered in Evanston, IL, provides a variety of stop loss offerings, including captives, pooled risk programs, and level-funded products. They pride themselves on an efficient and seamless client experience with direct access to underwriting, relationship leaders, and claims industry experts. To learn more, visit https://www.Evolution Risk.com/ and follow Evolution Risk Partners on LinkedIn .

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Generative AI is Changing Claims and Underwriting: Insights from Stan Smith, CEO of Gradient AI

Fri, 03 Jan 2025 12:53:45 GMT

P&C INSIGHTS BLOG | Feb 26, 2024

The Gradient AI Team

The insurance industry has always about managing risk, predicting outcomes, and providing a safety net for individuals and businesses. But today, insurers face new challenges: rising customer expectations for personalized services, increasing complexity in global risk factors, and a pressing need for innovation. Generative AI offers transformative potential in addressing these challenges, particularly in claims management and underwriting.

In a recent interview on the Analytics Insight podcast , host Priya Dialani sat down with Stan Smith, CEO and founder of Gradient AI , to discuss how Gen AI is empowering insurers to enhance efficiency, improve profitability, and deliver superior customer experiences.

Stan shared his insights in these 4 key areas:

1. Claims management

2. Underwriting

3. Workforce challenges

4. Gen AI implementation

Listen to the original podcast here: Generative AI and the Future of Insurance: Insights from Stan Smith

1. Rethinking Claims Management with Gen AI

Claims management is one of the most resource-intensive functions in insurance. Customers demand swift, fair processing, while insurers must mitigate costs, detect fraud, and ensure regulatory compliance. Balancing these demands often leads to inefficiencies and delays.

Stan’s Insights

From Gradient AI’s early days, clients posed a common question: “Can you help us identify creeping catastrophic claims?” These are claims that initially appear minor but escalate into significant losses.

Stan shared some important dynamics about insurance claims:

A small percentage of claims drive high costs: Less than 10% of claims can account for over 60-70% of total losses in areas such as workers’ compensation.
AI models can identify high-risk claims: AI models excel at flagging high-risk claims early, empowering insurers to allocate experienced adjusters where they’re needed most.

However, early identification alone isn’t enough. Clients would then ask, “Now what?” And this is where Gen AI steps in. Beyond predicting risks, it provides actionable recommendations — the " next best action " — tailored to each claim’s specifics. For example:

Suggesting targeted interventions or treatments to reduce claim costs and duration.
Offering clear ROI arguments for decisions, such as engaging third-party vendors for high-cost cases.
Streamlining low risk claims for expedited settlement, improving customer satisfaction.

2. Transforming Underwriting with Gen AI

Underwriting, especially in group health insurance, often relies on analyzing conditions and treatments without adequate context. Is a condition newly diagnosed, actively treated, or in remission? Without this clarity, underwriters might overestimate risks and decline viable groups.

Stan’s Insights

Stan shared that Gen AI bridges these gaps in the traditional approach by:

Providing contextual insights: For example, a condition and drug combination might signal a $400,000 annual cost. Gen AI contextualizes this, leveraging additional data points to reveal that the patient is in remission, reducing the estimated cost to $10,000 annually.
Enhancing efficiency: Automating the analysis of complex datasets allows underwriters to make informed decisions faster, reducing reliance on manual reviews by medical experts.

These advancements enable insurers to price policies accurately, mitigate risks, and confidently pursue opportunities previously deemed too uncertain.

3. Addressing Insurance Workforce Challenges

There’s a “perfect storm” happening in the insurance workforce: experienced professionals are retiring, remote work may be hindering mentorship, and there is often high attrition among new hires.

Stan’s Insights
Gen AI can serve as a co-pilot to address these challenges, offering:

Mentorship for adjusters: By identifying potential actions and providing rationale, Gen AI acts as an on-demand mentor, enhancing decision-making for both seasoned and novice adjusters.
Insights for underwriters: Gen AI suggests pricing adjustments based on nuanced factors like regional crime rates or industry-specific risks, supporting more accurate underwriting.

4. Key Success Factors for Implementing Gen AI in Insurance Organizations

Change can be difficult in any organization. In order to maximize the ROI of new technology investments, insurers should target operational areas with significant impact on combined ratios, such as claims payouts and underwriting accuracy, as well as leverage historical data to train AI models for better predictions.

Stan’s Insights

For best chance at success, Stan emphasized the importance of:

Data quality: Inaccurate or incomplete data undermines AI effectiveness.
Transparency: Providing clear reasoning for AI-driven recommendations builds trust with users and regulators.
Proactive regulation compliance: Engage with regulators early to address concerns around data use, bias, and security.

Conclusion: Future Trends in Gen AI

Stan predicts increased adoption of Gen AI across the insurance industry to:

Automate mundane tasks: For example, summarizing documents and streamlining workflows; such automation frees up teams to focus on more strategic activities.
Enhance customer interactions: Continuously improve recommendations by learning from customer interactions.
Support dynamic decision-making: Empower insurers to adapt to evolving risks and market conditions in real time.

Generative AI is not just a technological advancement—it’s a game-changer for the insurance industry. And as Stan Smith underscores, the key to success lies in focusing on high-impact solutions, maintaining transparency, and embracing regulatory collaboration. The future of insurance is here, and with Gen AI, it’s brighter than ever.

Listen to the original podcast here: Generative AI and the Future of Insurance: Insights from Stan Smith

Explore the benefits of Gradient AI's P&C or Workers' Comp Solutions.

Schedule a Demo →

Gradient AI 2025 Predictions: AI, GenAI, and Digital Transformation Trends

Wed, 01 Jan 2025 15:10:20 GMT

January 1, 2025

By Stan Smith

As AI technology continues to evolve - both traditional AI (which perform tasks such as data analysis and making predictions) as well as generative AI (which uses large data sets to generate new content, answer questions, and explain concepts) - the insurance landscape in 2025 is set for more transformative changes. From optimizing underwriting and claims processes to navigating complex regulatory requirements, AI is empowering insurers with enhanced insights, streamlined operations, and greater accuracy in pricing.

Here are three key predictions outlining the future of insurance innovations.

1. AI Will Drive Next Best Action in Underwriting and Claims Management

Today, insurers use AI for simpler tasks like processing straightforward data inputs or automatically categorizing claims. By 2025, I predict AI will be increasingly leveraged to provide next best action recommendations for both underwriting and claims management. Underwriters will receive AI-driven insights such as recommendations to adjust coverage limits or additional data sources to better assess risk. For claims adjusters, AI will suggest actions like seeking additional documentation or identifying the optimal settlement path based on previous claims data. For example, with Medicare Set-Asides, AI will automatically determine whether additional information or approvals are needed to meet compliance requirements. This helps reduce the amount of manual oversight and expedites the process. Advanced insights will help streamline decision-making and allow insurers to achieve greater efficiency and consistency across their operations.

2. Specialized GenAI Will Become Commonplace in Insurance

While many insurance companies have started experimenting with general AI tools, their capabilities often fall short when it comes to the industry-specific needs of insurers, like analyzing policy language or understanding regulatory requirements.

As demand grows, insurers will increasingly adopt specialized genAI models designed specifically for the insurance sector. These specialized tools will be tailored to analyze intricate policy documents, develop custom pricing models, and draft tailored policy language, making them far more effective than general-purpose AI. This shift will enable insurers to better navigate complex regulatory environments and improve their ability to design personalized insurance products, ultimately leading to greater accuracy and customer satisfaction. As technology evolves, the focus will shift to tools that can better interpret nuanced insurance data.

3. Insurance Companies will Transition to Gradual Digital Transformation to Avoid Legacy System Overhaul Failures

Many insurance companies have attempted to modernize their legacy systems, but a significant number of these efforts have failed, often due to the overwhelming scope of the projects. When insurers take on too much at once, such as attempting to replace all core systems simultaneously, the complexity can become unmanageable, leading to costly setbacks.

By 2025, more insurers will pivot to a phased approach to digital transformation, implementing targeted upgrades instead of complete system overhauls. This approach will allow companies to focus on specific areas like customer portals or underwriting modules, integrating AI gradually and minimizing disruption to existing operations. This incremental strategy will help insurers avoid the pitfalls of past modernization attempts while still improving efficiency and competitiveness in the market. The key will be to focus on small, high-impact projects that deliver immediate value, building momentum over time.

As AI becomes further embedded into the fabric of insurance operations, 2025 will be the year for redefining efficiency, compliance, and innovation across the industry. The trends outlined in this piece underscore AI's transformative potential to optimize core processes. By harnessing the capabilities of both traditional and generative AI, insurers are positioned to drive meaningful change, unlock new growth opportunities, and deliver greater value to customers. The future of insurance is here, and those who embrace these advancements will shape the industry in powerful, enduring ways.

Stan Smith, Gradient AI

Stan Smith is the founder and chief executive officer of Gradient AI

This article first appeared on vmblog.com.

What’s Next for AI in Insurance? 6 Trends to Watch in 2025: Two Seasoned Industry Leaders Weigh In

Mon, 09 Dec 2024 16:12:01 GMT

GROUP HEALTH INSIGHTS BLOG | Dec 20, 2023

The Gradient AI Team

As AI technology continues to evolve – both traditional AI (which perform tasks such as data analysis and making predictions) as well as generative AI (which uses large data sets to generate new content, answer questions, and explain concepts) – the insurance landscape in 2025 is set for more transformative changes. From optimizing underwriting and claims processes to navigating complex regulatory requirements, AI is empowering insurers with enhanced insights, streamlined operations, and greater accuracy in pricing.

6 Key Predictions: How Artificial Intelligence Will Impact the Future of Insurance

Gradient AI’s CEO, Stan Smith , and General Manager of P&C, Jeff Snider , offer their expert insights on how these advancements will drive competitive advantage and elevate customer experience. Here are six key predictions from these seasoned industry leaders on AI’s impact in shaping the future of insurance in 2025.

1. AI Will Drive Next Best Action Recommendations in Underwriting and Claims Management

Today, insurers use AI for simpler tasks like processing straightforward data inputs or automatically categorizing claims.

By 2025, insurers will increasingly leverage AI to provide next best action recommendations for both underwriting and claims management. “Next best action’ recommendations mean that underwriters will receive specific suggestions to adjust coverage limits or additional data sources to better assess risk, based on AI-driven insights. Similarly, for claims adjusters, AI will suggest specific next best actions such as seeking additional documentation or identifying the optimal settlement path based on previous claims data. For example, with Medicare Set-Asides, AI will automatically determine whether additional information or approvals are needed to meet compliance requirements. This will help reduce the amount of manual oversight and expedite the process.

AI’s advanced insights will continue to help streamline decision-making and allow insurers to achieve greater efficiency and consistency across their operations.

2. Specialized GenAI Will Become Commonplace in Insurance

While many insurance companies have started experimenting with general generative AI tools, their capabilities often fall short when it comes to the industry-specific needs of insurers, like analyzing policy language or understanding regulatory requirements.

As demand grows, insurers will increasingly adopt specialized Gen AI models designed specifically for the insurance sector. These specialized tools will be tailored to analyze intricate policy documents, develop custom pricing models, and draft tailored policy language, making them far more effective than general-purpose AI. This shift will enable insurers to better navigate complex regulatory environments and improve their ability to design personalized insurance products, ultimately leading to greater accuracy and customer satisfaction. As technology evolves, the focus will shift to increasingly specialized tools that can better interpret nuanced insurance data.

3. Insurers will Transition to a More Gradual Approach to Digital Transformation to Avoid Legacy System Overhaul Failures

Many insurers have attempted to modernize their legacy systems, but a significant number of these efforts have failed, often due to the overwhelming scope of the projects. When insurers take on too much at once, such as attempting to replace all core systems simultaneously, the complexity can become unmanageable, leading to costly setbacks.

In 2025, more insurers will pivot to a phased approach to digital transformation, implementing targeted upgrades instead of complete system overhauls. This approach will allow companies to focus on specific areas like customer portals or underwriting modules, integrating AI gradually and minimizing disruption to existing operations. This incremental strategy will help insurers avoid the pitfalls of past modernization attempts while still improving efficiency and competitiveness in the market. The key will be to focus on smaller, high-impact projects that deliver immediate value, building momentum over time.

4. Specialized Data Sources Will Increase Insurance Risk Assessment Accuracy

Insurers today typically use established data sets like historical loss data, demographic information, and general weather reports to assess risk. The current AI models used today handle broad categories of risk—they rarely leverage niche data sources.

In 2025, insurers will start to integrate specialized data sources such as climate and health data to enhance risk assessment and underwriting. For example, we may see insurance companies leverage climate change information from localized sources such as reports from coastal surveillance or community-based environmental organizations. This information will be used to calculate prospective risks for property damage and climate-induced health concerns. Tapping into health data, like fitness-related information from smartwatches, will allow insurers to differentiate between policyholders who may look the same in a policy application but actually represent vastly different risk profiles (e.g., active triathletes vs. sedentary individuals with significant latent health risks). This precise data integration will provide insurers with a holistic view, ultimately enabling more accurate policy pricing and claims assessments.

5. Automated Insurance Document Processing Will Become Smarter and More Scalable

Currently many insurance companies use basic document automation for small-scale digital document processing. This often involves summarizing brief records or extracting simple information from structured documents. However, when they need to process large, complex files like medical records, this still requires significant manual input.

In 2025, we’ll begin to see large-scale, high-accuracy automated document processing change the way documents are managed. Insurers will be able to process thousands of lengthy, multi-format documents efficiently, handling everything from de-duplicating redundant data to categorizing unstructured records. For example, claims involving extensive medical histories will no longer require tedious manual reviews, as automated systems will quickly summarize and catalog pertinent details across hundreds of pages. This means that claims processing will be faster, cheaper, and more accurate, allowing insurers to manage even higher volumes of claims while reducing error rates and improving customer satisfaction.

6. Insurers Will Align AI Models with New Regulatory Demands

As insurers expand their use of AI to streamline underwriting and claims management, industry standards for responsible AI use will become more uniform across states. Recently, the National Association of Insurance Commissioners (NAIC) adopted a model bulletin outlining compliance requirements for insurers’ AI systems. This bulletin clarifies expectations for development, deployment, and documentation of AI technologies to ensure adherence to state and federal laws. So far, at least 11 states, along with Washington, D.C., have issued bulletins incorporating NAIC’s language, including Alaska, Connecticut, Illinois, Kentucky, Maryland, Nevada, New Hampshire, Pennsylvania, Rhode Island, Vermont, and Washington.

In 2025, insurers are expected to adopt transparent, AI-driven models that align with both NAIC guidelines and specific state regulations. These models will also include explainable AI components to ensure clear, auditable insights into predictive processes to meet compliance demands. For insurers operating across state lines, these advancements will streamline regulatory adherence and enhance consumer trust by offering transparent, ethical risk assessments. This shift will establish responsible AI use as a foundational standard in the industry, reshaping the role of predictive analytics and data usage in insurance.

Conclusion: AI Has the Potential to Optimize Core Insurance Processes

As AI becomes further embedded into the fabric of insurance operations, 2025 will be the year for redefining efficiency, compliance, and innovation across the industry. The trends outlined in this piece underscore AI’s transformative potential to optimize core processes. By harnessing the capabilities of both traditional and generative AI, insurers are positioned to drive meaningful change, unlock new growth opportunities, and deliver greater value to customers. The future of insurance is here, and those who embrace these advancements will shape the industry in powerful, enduring ways.

Explore the benefits of Gradient AI's Group Health Solutions.

Schedule a Demo →

What’s Next for AI in Insurance? 6 Trends to Watch in 2025: Two Seasoned Industry Leaders Weigh In

Mon, 09 Dec 2024 13:19:43 GMT

P&C INSIGHTS BLOG | Feb 26, 2024

The Gradient AI Team

6 Key Predictions: How Artificial Intelligence Will Impact the Future of Insurance

1. AI Will Drive Next Best Action Recommendations in Underwriting and Claims Management

Today, insurers use AI for simpler tasks like processing straightforward data inputs or automatically categorizing claims.

2. Specialized GenAI Will Become Commonplace in Insurance

3. Insurers will Transition to a More Gradual Approach to Digital Transformation to Avoid Legacy System Overhaul Failures

4. Specialized Data Sources Will Increase Insurance Risk Assessment Accuracy

5. Automated Insurance Document Processing Will Become Smarter and More Scalable

6. Insurers Will Align AI Models with New Regulatory Demands

Conclusion: AI Has the Potential to Optimize Core Insurance Processes

As AI becomes further embedded into the fabric of insurance operations, 2025 will be the year for redefining efficiency, compliance, and innovation across the industry. The trends outlined in this piece underscore AI’s transformative potential to optimize core processes. By harnessing the capabilities of both traditional and generative AI, insurers are positioned to drive meaningful change, unlock new growth opportunities, and deliver greater value to customers. The future of insurance is here, and those who embrace these advancements will shape the industry in powerful, enduring ways.

Explore the benefits of Gradient AI's P&C or Workers' Comp Solutions.

Schedule a Demo →

5 Questions with Courtney Yablecki, Gradient AI Client Executive Specialist, Group Health

Tue, 03 Dec 2024 15:01:08 GMT

GROUP HEALTH INSIGHTS BLOG | Nov 13, 2023

The Gradient AI Team

Courtney is a Client Executive Specialist on the Group Health Client Support team, focusing on engaging and supporting our clients during implementation and then once they go live in production. This talented team is dedicated to providing best in class service and support to our clients throughout their relationship with Gradient AI.

We sat down recently with Courtney to understand the primary focus of her role, what she likes most about her job here at Gradient, and what a typical day looks like.

Prior to joining Gradient AI in 2022, Courtney spent nine years at Cigna, where she held both Underwriting and Client Management Roles. She has a bachelor’s degree in business administration from Northeastern University. She currently resides in Savannah, Georgia.

Question 1: What is the primary focus of your role as Client Executive Specialist, Group Health?

My primary objective is to be a trusted partner and advisor to our clients who use Gradient AI’s Health solutions . Building this relationship starts at the first point of contact, builds through the implementation process, and expands throughout the entire relationship to ensure customer trust and satisfaction. This requires a deep understanding of our Group Health clients, their goals in choosing Gradient AI as their AI solution of choice and providing continuous support to help meet their organizational objectives.

Question 2: What are some key factors in a successful customer implementation?

The key to a successful customer implementation is asking the right questions to understand the customer’s goals and how we can best leverage our products to meet their objectives. This is not only critical in setting the foundation for a smooth and efficient implementation, but it helps us provide the training and guidance our Group Health clients need to maximize Gradient AI’s Health solutions .

The next essential step is creating a strong project plan that delivers agreed-upon metrics, measurable deliverables, and attainable timelines. In addition, we provide key artifacts, such as models, charts, or graphs to provide the vital data insight our client needs. Finally, I’d say that constant communication is critical throughout the process to ensure we are hitting our deadlines and our client’s expectations are being met.

Question 3: What does a typical day at Gradient AI look like for you?

Every day is truly different! However, one thing that is constant is collaboration. With customers, we are collaborating on how to best utilize their Gradient tools to meet their goals. This could be providing training, proactively communicating upcoming product enhancements or reviewing model results. This also includes taking in feedback from our customers and within the Gradient AI team, identifying potential improvements to product, process or communication to best meet their needs.

Internally with Gradient teams, we are continuously sharing client feedback so that client challenges can be addressed quickly and smoothly. This may include discussing potential product enhancements with the Product team, reviewing model results with the Actuarial and Data Science teams, or identifying and remedying bugs with the Engineering teams.

Question 4: What do you like most about your job?

I love the fact that every day brings about new opportunities for evolution! In my 2 and a half years at Gradient AI, I’ve seen our products and teams evolve and expand in ways that were beyond my wildest imagination when I first joined the team. We have a truly talented team here that is always looking for new ways to improve the client experience via improved products and processes!

Every piece of feedback from our clients is critical in this evolution and am grateful to be part of a team that utilizes this feedback to drive innovation.

Question 5: What do you like to do in your free time?

Having recently moved to Savannah, I’ve been spending my free time exploring the city. I am always finding new restaurants, antique shops, historical landmarks, tours, parks and events to add to my list of Savannah favorites. I also enjoy doing puzzles while listening to some vinyl from my record collection, getting in a good Peloton session, and playing with my English Bulldog, Boogie.

Bonus Question: What is your favorite place to eat and why?

Dukes in Huntington Beach, California. There is nothing better than eating some delicious food while watching the sunset over the Pacific Ocean.

Thank you, Courtney! Be sure to check back on our blog as we ask 5 questions to different members of our talented staff across Gradient AI, to learn how they support the success of our clients.

Explore the benefits of Gradient AI's Group Health Solutions.

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11 biggest US insurtech equity deals in Q3 2024

Tue, 26 Nov 2024 17:00:14 GMT

November 26, 2024

The three largest global insurtech equity deals in the third quarter had an average funding round of more than $150 million. These three deals earned an average of 17.73% of their total funding in the quarter. In total, all 11 of the top insurtech equity deals in the third quarter earned $719 million in funding.

Read more about the ten biggest global insurtech equity deals in the third quarter.

Insurtechs ranked by funding in Q3

eMaxx tied for 9th with a round amount of $20M.

EvolutionIQ tied for 9th with a round amount of $20M.

Companion Protect tied for 9th with a round amount of $20M.

Roots Automation ranked 8th with a round amount of @22M.

Faye ranked 7th with a round amount of $31M.

Thatch ranked 6th with a round amount of $38M.

Gradient AI ranked 5th with a round amount of $56M.

Cowbell Cyber ranked 4th with a round amount of $60M.

Devoted Health ranked 3rd with a round amount of $112M.

Zing Health ranked 2nd with a round amount of $140M.

Altana Ai ranked 1st with a round amount of $200M.

This article first appeared on Digital Insurance.

The Impact of AI on Health Carrier Underwriting: Insights From Industry Experts

Fri, 08 Nov 2024 20:05:25 GMT

November 8, 2024

By Jason Oswald, Director of Solution Consulting, Group Health at Gradient AI

Artificial intelligence (AI) is reshaping industries across the board, but its role in the group health underwriting in the carrier space is particularly compelling. Who is using AI, and why? How are underwriters leveraging data, AI, and advanced analytics to secure new business and optimize their portfolios?

These questions were central to a recent webinar that I participated in on the impact of AI on health carrier underwriting. I was joined by industry leaders, Mike Harris, vice president of Major Group, Chamber, Associations, and Student Health Plans at BlueCross BlueShield of South Carolina and Jay Sheehy, chief underwriting officer at Gravie. Together, we brought decades of experience, each offering unique insights on how AI is being integrated into the underwriting processes.

For those of you who couldn’t attend, here are the main points and key takeaways from our discussion.

1. What is driving carriers to consider AI in group health underwriting?

The consensus among the panelists was that the primary motivation for adopting AI in group health underwriting is to gain deeper insights into group risk, enabling faster and more informed decision-making. AI allows carriers to streamline workflows, prioritize favorable risks, and manage fewer desirable risks more effectively, leading to better outcomes for both insurers and insureds.

In my view, being able to get a decision faster moves the good risk to the top of the pile and allows sales, underwriting, and management teams to focus on winning those groups, while bad risk gets de-prioritized because companies do not have unlimited resources or time.

2. What challenges are carriers trying to solve with AI?

While speed, accuracy, and profitability are universal goals, the specific challenges that carriers aim to address with AI can vary. For some, AI is a tool for expanding into new markets, while for others, it enhances an understaffed or inexperienced underwriting team.

Harris highlighted the competitive nature of the market, particularly with smaller groups where limited data poses a challenge. “We needed a better way to evaluate risk,” Harris explained. “AI provided us with a new perspective, allowing us to be more aggressive when necessary.” Sheehy, a newer, growing payer, emphasized the need for scalability, noting that AI allows its company’s underwriters to focus on financial decision-making while machines handle the analytics.

3. What impact has AI had on health carrier underwriting?

AI’s strength is ability to combine machine learning with human expertise has proven invaluable. The technology does not replace underwriters but enhances their ability to process and prioritize risks efficiently. It helps identify groups to focus on, and underwriters can use that data to push more quotes through at favored risk levels.

Throughout our discussion, we emphasized the importance of data in making accurate risk predictions. Sheehy remarked, “Predictive models can give you 300 different scenarios in minutes, a task that would take an underwriter hours to do manually.”

4. What results have you seen since integrating AI?

Harris shared how AI has allowed BlueCross BlueShield of South Carolina to target specific accounts and improve their overall risk assessment. After initially testing AI in one segment, the company expanded its use across all segments due to its clear value.

Sheehy described how AI has improved their turnaround times and data accuracy, leading to better risk projections and more accurate pricing.

5. How did you handle change management when implementing AI?

Implementing AI requires careful change management, particularly when introducing new tools to teams. Harris noted that there was an initial pushback from his sales team, but as they became familiar with AI’s speed and accuracy, they embraced the technology. Similarly, underwriters recognized the value of AI after seeing its effectiveness in a test segment.

Sheehy explained that because his company was in a growth phase, they incorporated AI into their processes from the start, avoiding some of the change management challenges faced by more established organizations.

6. What is the future of AI?

In closing, we all agreed on the importance of carriers engaging with AI, evaluating how it aligns with their market goals and technological investments.

Both Sheehy and Harris pointed out that AI is still in its early stages, with unlimited potential for future applications. Whether it’s creating tailored programs for employers or improving financial protections, AI’s role in underwriting is set to expand significantly.

As AI continues to evolve, its integration into group health underwriting offers carriers new opportunities to enhance decision-making, streamline operations, and improve overall plan performance. The insights shared provide a valuable roadmap for carriers considering AI’s potential impact on their business.

This article first appeared on First Report Managed Care .

Reducing Litigation in Workers’ Compensation: The Power of AI Integrated at the Point of Decision

Wed, 23 Oct 2024 16:25:28 GMT

P&C INSIGHTS BLOG | Aug 20, 2024

Co-authored by Jeff Snider, General Manager, Property & Casualty, Gradient AI and Chris Bennett, Head of Strategy, Core Solutions, Origami Risk

Workers’ Compensation litigation can be a big headache for insurers and employers, costing them significant time and money. For varying reasons, lawyers often get involved more than they should, which can result in exorbitant legal fees, drawn-out case durations, and inflated settlement amounts. In fact, research shows that litigated claims can cost up to twice that of non-litigated ones, straining an organization’s resources. Beyond costs, litigation can often delay and prolong the recovery process for injured workers, impacting employee morale and productivity.

Prioritizing effective claims management from the onset is critical to effectively managing the challenges and potential litigation costs. This means gathering, managing, and analyzing data efficiently, coupled with consistent communication to the injured worker. To help achieve these objectives, many insurers are turning to advanced claims management systems with integrated artificial intelligence (AI).

In a 2024 Origami Risk study , 76% of P&C insurers indicated they were already using, accelerating investment, or planning to use AI to support claims management. By integrating AI at the point of decision, claims adjusters and managers are able to identify potential litigation risks early on and take proactive measures.

Spot Claims Risks Early and Take Action

One of the key benefits of integrating AI into claims management is its ability to spot high-risk claims before they spiral out of control. By analyzing various data points like injury types, claimant history, and other risk factors, AI can identify claims that are more likely to result in litigation. AI-driven predictive analytics can significantly reduce claim severity and optimize reserve allocation. By continuously monitoring claim data in real-time, AI flags high-risk cases early, preventing escalation and allowing adjusters and claims managers to intervene sooner, using tools such as early settlement offers, mediation, or focused case management, thereby reducing the chance of legal disputes.

A research study by Gradient AI found that AI-enabled workers’ compensation claims management reduced legal involvement in lost-time claims by 15%. This reduction translates into a 5% savings in lost-time claim costs, equating to an estimated annual savings of $3.5 million based on the study’s insurers managing an average of $70 million in lost-time claims.

AI insights become even more influential when paired with a configurable core platform that provides the ability to take subsequent action in a claim workflow. For example, when AI uncovers factors contributing to a high risk or a fraudulent claim, new tasks, alerts, follow-up actions, or SIU referrals can be automatically generated by the platform. Proactive claims management can lead to faster resolutions, less conflict, and ultimately, fewer cases requiring legal intervention.

AI Delivers Improved Decision Making

AI-powered claims management solutions can also help claims professionals allocate resources more efficiently. For claims managers, these systems identify high-risk claims that require the attention of more seasoned adjusters to manage the claim to a better outcome and enable injured workers to recover and return to work faster.

Similarly, AI-enabled solutions help adjusters to concentrate their efforts on high-risk claims and prioritize cases that need extra attention, while allowing lower risk claims to move through the process with fewer touchpoints. This not only reduces the potential for litigation but also speeds up the claims process overall.

AI also delivers another benefit, bringing consistency to decision-making. AI ensures that claims are handled based on objective data, reducing the likelihood of errors or inconsistencies that might lead to disputes. When claimants feel they are being treated fairly and consistently, they are less likely to seek legal recourse, which reduces the overall rate of litigation.

When paired with a flexible core platform, implementing AI across a claims operation expands AI-derived insights that serve to inform the business on their overall strategy. This translates to improved lifecycle processes from reserving to settlement, while accelerating return–to–work with better operational efficiency.

AI-Driven Analytics Cut Legal Costs and Speed Up Claims

Litigated claims are not only more expensive but also take significantly longer to resolve. Legal involvement is a major cost driver in casualty claims, especially in cases where an injury keeps the employee out of work for an extended period. The ability to identify and address high-risk claims early on can prevent prolonged legal battles, resulting in shorter claim durations. By resolving disputes more quickly, organizations can reduce the burden on both the injured worker and the claims team.

With AI-driven analytics integrated into their core platform, claims professionals can make faster, more accurate decisions, leading to earlier settlements and a reduced chance of lengthy legal disputes. This results in lower legal costs and allows organizations to redirect resources toward other critical areas, such as injury prevention and employee wellness programs.

Similarly, newer, less experienced employees can leverage AI, further enhancing the value of AI integrated into the core platform. AI models capture the collective institutional knowledge of seasoned team members and impart their wisdom, providing “guideposts” to help them perform more effectively and reduce the risks of costly litigation.

Looking Ahead - The Future of Claims Management

Workers’ Compensation litigation can drag on for weeks, months, or even years, in challenging cases. As litigation continues to drive up costs in workers' compensation , it is clear that more effective claims management solutions are needed. While litigation poses significant challenges for insurers, AI is transforming the insurance industry by streamlining operations and facilitating data-driven decisions.

AI-integrated core platforms offer a path to not only cut legal expenses but also improve the overall claims experience for insurers, employers and injured workers.

By adopting these technologies, organizations can manage high-risk claims more proactively, resolve disputes more quickly, and avoid litigation whenever possible. In the long run, this will lead to significant cost savings, shorter claim durations, and better outcomes for injured workers.

“By investing in innovative technology, we’ve been able to improve the claims journey because of the increased capacity to triage incoming claims, decrease processing times and reduce the costs of claims.”

– Insurance Executive on the use of AI for Workers’ Comp claims

Accelerate Your Journey to AI-Enabled Claims Management

Origami Risk and Gradient AI are already fully integrated, providing insurers a simple path to AI-enabled claims management :

Turnkey activation. No complex engineering or IT investment required using configurable SaaS-based technology
As easy as opening an app. “Turbocharge” your platform and use AI-based insights
Enable Staff. Immediately leverage AI at the point of decision
Easy Implementation. Claims professionals continue to use their system, screens, and workflows, but gain the benefits of AI for enhanced operations and decision-making

Gain all the benefits of AI in reducing Workers’ Compensation litigation while improving overall claims workflow with Origami and Gradient AI.

Explore the benefits of Gradient AI's P&C or Workers' Comp Solutions.

Schedule a Demo →

Guest Post: Reducing Litigation in Work Comp – The Power of AI Integrated at Point of Decision

Fri, 18 Oct 2024 15:26:51 GMT

October 18, 2024

By Jeff Snider, General Manager, Property & Casualty, Gradient AI and Chris Bennett, Head of Strategy, Core Solutions, Origami Risk

In a 2024 Origami Risk study, 76% of P&C insurers indicated they were already using, accelerating investment, or planning to use AI to support claims management. By integrating AI at the point of decision, claims adjusters and managers are able to identify potential litigation risks early on and take proactive measures.

Spot Claims Risks Early and Take Action

AI Delivers Improved Decision Making

AI-Driven Analytics Cut Legal Costs and Speed Up Claims

Looking Ahead – The Future of Claims Management

Workers’ Compensation litigation can drag on for weeks, months, or even years, in challenging cases. As litigation continues to drive up costs in workers’ compensation, it is clear that more effective claims management solutions are needed. While litigation poses significant challenges for insurers, AI is transforming the insurance industry by streamlining operations and facilitating data-driven decisions.

AI-integrated core platforms offer a path to not only cut legal expenses but also improve the overall claims experience for insurers, employers and injured workers.

This article first appeared on WorkCompWire.

Predict Risk with Greater Confidence with Gradient AI’s Renewal Analytics Solution for Group Health

Tue, 15 Oct 2024 19:15:12 GMT

GROUP HEALTH INSIGHTS BLOG | Apr 24, 2024

The Gradient AI Team

Manage Your Existing Group Health Business More Accurately

In today’s unpredictable market, the health insurance industry faces an increasing number of challenges, including the rising cost of expensive treatments, evolving consumer expectations, changing demographics, regulatory uncertainty, technology disruption, and increasing market competition.

Managing and accurately pricing risk has become even more important than ever, and the ability to predict rather than react is essential to stay ahead of the competition.

“Our customers have found great success in leveraging our SAIL™ product to help underwrite new business,” says Gradient AI’s Director of Solution Consulting, Jason Oswald. “And it became increasingly evident that the same algorithms could be leveraged for a customer’s existing business utilizing a client’s own claims data.”

Cutting-Edge AI and Machine Learning Algorithms

Gradient AI’s Renewal Analytics solution leverages AI and machine learning algorithms to help manage your existing business. By extracting previously hidden insights from health claims records belonging to your existing groups, our Renewal Analytics (RA) solution enables you to get a more in-depth understanding of your current and prospective group and member-level risk profiles in order to price more accurately for the future, manage current costs, and get ahead of emerging trends.

Fact Sheet: Renewal Analytics Solution for Existing Business

Download the Fact Sheet →

What are Renewal Analytics?

The ability to track performance across your entire book of business and monitor current and future exposure is critical, particularly as your population can change due to new members, mergers, and acquisitions.

You can now manage and project risk more accurately by utilizing three key components of Renewal Analytics Solution:

1. Renewal Predictive Al Model delivers risk predictions valuable for renewal underwriting or repricing a block of business, early identification of developing trends, and the implementation of risk management and cost containment strategies. The Renewal model utilizes your own historical medical and pharmacy claims data to develop a risk expectation for the upcoming 12 months.

2. Analytics and Reporting Dashboards produce summary and detailed views of group claims, enrollment trends, and performance metrics across factors such as industry, geography, group size, and demographics. Quickly discern underlying cost drivers for groups, members, and high-cost claimants, along with other predictive insights.

3. Rebanding Calculator provides recommendations for group-assigned risk bands, based on client- specific inputs, model predictions, and customer-defined scenarios and business rules at the time of renewal.

“Renewal Analytics provides deeper insights into our customers’ existing book of business, allowing them to project risk with much greater accuracy,” says Vice President of Group Health, Chase Pettus. “This tool provides clients with the flexibility to view performance metrics across the entire book or drill down to the group or member level to optimize plan performance.”

Gradient AI’s Renewal Analytics Solution is Easy to Use and to Access

In-house analytics can be time-consuming and expensive, often limited to select inquiries defined by just a few individuals within an organization, and results can often be skewed if data was not standardized or mapped correctly. 

With our Renewal Analytics solution, you can access your data with a click of a button, extract the information you seek, and in the desired format you wish. It allows you to track performance utilizing key metrics across your entire book of business.

What’s Next for Gradient AI’s Renewal Analytics Solution?

RA is evolving into a dynamic reporting and analytics suite allowing clients to:

identify opportunities for intervention, cost savings, and cost benchmarking
project group turnover under specific conditions
automate pricing and payments
understand and predict treatment pathways at a deeper member level

As this product continues to grow and scale with the creation and expansion of models and new data sources, added features, new reporting capabilities, and more intuitive designs, customers using it will benefit without any additional effort.

Gradient AI’s Lead Product Manager Jennifer Pigott, says, “We are currently only scratching the surface when it comes to Renewal or more broadly, Claims Analytics. Gradient AI will continue to engage our customers and assess the market to target product investments that will bring the most value.”

Explore the benefits of Gradient AI's Group Health Solutions.

Schedule a Demo →

Gradient AI Partners with Michigan Planners to Boost Risk Assessment and Health Solutions

Mon, 14 Oct 2024 15:17:22 GMT

October 14, 2024

Gradient AI, a provider of enterprise artificial intelligence solutions, has announced its partnership with Michigan Planners, a leading insurance agency specialising in employer group benefits.

Through this collaboration, Michigan Planners will use Gradient AI’s SAIL underwriting platform to enhance risk assessment and create customized health plans for employer groups, leading to significant cost savings.

Michigan Planners, known for offering risk financing and group health solutions, faced challenges in efficiently assessing risks for small- to mid-sized companies, particularly those with 25 to 500 employees. Limited data and bandwidth made it difficult to conduct accurate risk assessments. By implementing Gradient AI’s SAIL platform, the company can now generate more comprehensive insights without the need for additional staff.

The SAIL platform enables Michigan Planners to tailor health plans to the unique needs of each client, offering deeper analysis into pooled risk groups and improving risk projections for companies with high turnover or recent mergers. For smaller employer groups, the platform also provides valuable tools for tracking claims and overall plan performance.

In one instance, SAIL revealed that a growing client, following an acquisition, would benefit from avoiding self-funding, ultimately saving the company tens of thousands of dollars. The AI-powered insights have also helped clients make better decisions on high deductible plans and other funding options, allowing for more strategic and informed discussions between Michigan Planners and its clients.

With Gradient AI’s solution, Michigan Planners is better equipped to offer customized, data-driven health plans that align with the specific needs of its diverse client base.

Aaron MacDonald, chief strategy officer at Michigan Planners said: “We use SAIL to give our clients better insight and a better prognosis into claims. Prior to using SAIL, we could only look through the rear-view mirror at past claims data. Now we can look through the windshield to better predict what costs our clients can expect to incur. With the speed, quick turnaround, and insights we’ve gained, SAIL has more than paid for itself.”

Stan Smith, CEO and founder of Gradient AI, also commented, saying: “Michigan Planners is an excellent example of how the SAIL solution can help insurance agencies better serve their clients. By providing a deeper understanding of risk and offering more actionable insights, Michigan Planners is customizing health plans to their clients’ unique needs, improving outcomes and driving cost savings.”

He added: “We are proud to partner with them in changing how employee benefits are delivered.”

This article first appeared on Insurtech Insights .

Gradient AI Signs Up Michigan Planners to SAIL Solution

Thu, 03 Oct 2024 13:55:19 GMT

October 3, 2024

Gradient AI continues to sign up new clients as they roll out their SAIL product- here’s the word:

Gradient AI , a leading enterprise software provider of artificial intelligence (AI) solutions in the insurance industry, has announced that Michigan Planners is using Gradient AI’s SAIL ™ underwriting solution to gain deeper insights into risk. This solution enables Michigan Planners and its clients to create customized group health solutions and achieve significant cost savings by tailoring plans to the specific needs of each employer group.

Michigan Planners is a full-service insurance agency specializing in employer group benefits, including risk financing, customized plan and group health solutions for groups of all sizes. The company turned to Gradient AI to overcome limitations in bandwidth and data, which previously hindered its ability to make accurate risk assessments for its target market of 25-500 life groups. Gradient AI’s SAIL provides valuable insights without requiring additional staff. It generates easy to understand outputs, far beyond what Michigan Planners previously received from carriers. This enables more meaningful, strategic conversations with its clients.

“We use SAIL to give our clients better insight and a better prognosis into claims,” said Aaron MacDonald, chief strategy officer at Michigan Planners. “Prior to using SAIL, we could only look through the rear-view mirror at past claims data. Now we can look through the windshield to better predict what costs our clients can expect to incur. With the speed, quick turnaround, and insights we’ve gained, SAIL has more than paid for itself.”

Gradient AI’s SAIL has allowed Michigan Planners to align solutions to the unique needs of each client. It helps identify and address high claims costs in pooled risk groups, improve risk projections for companies with high turnover rates or recent mergers, and offers smaller groups a new tool to track claims and plan performance. In one case, SAIL revealed that self-funding wasn’t suitable for a rapidly growing client after an acquisition, saving them tens of thousands of dollars. SAIL also allows clients to make more informed decisions about high deductible plans and self-funding.

“Michigan Planners is an excellent example of how the SAIL solution can help insurance agencies better serve their clients,” said Stan Smith, CEO and founder of Gradient AI. “By providing a deeper understanding of risk and offering more actionable insights, Michigan Planners is customizing health plans to their clients’ unique needs, improving outcomes and driving cost savings. We are proud to partner with them in changing how employee benefits are delivered.”

This article first appeared on Insurance Edge .

Gradient AI’s SAIL Empowers Michigan Planners to Improve Risk Projections for Employer Groups

Tue, 01 Oct 2024 13:54:18 GMT

AI-driven Insights Help Employee Benefits Agency Tailor Health Solutions and Cut Costs for Clients

Boston, MA October 1, 2024 - Gradient AI , a leading enterprise software provider of artificial intelligence (AI) solutions in the insurance industry, today announced that Michigan Planners is using Gradient AI’s SAIL ™ underwriting solution to gain deeper insights into risk. This solution enables Michigan Planners and its clients to create customized group health solutions and achieve significant cost savings by tailoring plans to the specific needs of each employer group.

Gradient AI’s SAIL has allowed Michigan Planners to align solutions to the unique needs of each client. It helps identify and address high claims costs in pooled risk groups, improve risk projections for companies with high turnover rates or recent mergers, and offers smaller groups a new tool to track claims and plan performance. In one case, SAIL revealed that self-funding wasn't suitable for a rapidly growing client after an acquisition, saving them tens of thousands of dollars. SAIL also allows clients to make more informed decisions about high deductible plans and self-funding.

About Michigan Planners

Michigan Planners specializes in providing comprehensive employee benefits solutions to businesses of all sizes, from small to large enterprises. With decades of experience, their team delivers personalized service and expert guidance, ensuring tailored benefit plans that meet the unique needs of each organization. Beyond employee benefits, they offer individual coverage and Medicare products, helping individuals and retirees navigate their healthcare options with confidence. Michigan Planners is committed to being a trusted partner, delivering solutions that protect and empower workforces, while providing peace of mind for individuals and families.

To learn more about Michigan Planners, please visit www.miplanners.com and follow Michigan Planners on LinkedIn , Facebook , Youtube , and Instagram .

About Gradient AI

Gradient AI is a leading provider of proven artificial intelligence (AI) solutions for the insurance industry. Its solutions improve loss ratios and profitability by predicting underwriting and claim risks with greater accuracy, as well as reducing quote turnaround times and claim expenses through intelligent automation. Unlike other solutions that use a limited claims and underwriting dataset, Gradient AI's software-as-a-service (SaaS) platform leverages a vast industry data lake comprising tens of millions of policies and claims. It also incorporates numerous other features including economic, health, geographic, and demographic information. Customers include some of the most recognized insurance carriers, MGAs, MGUs, TPAs, risk pools, PEOs, and large self-insured employers across all major lines of insurance. By using Gradient AI's solutions, insurers of all types achieve a better return on risk. To learn more about Gradient AI, visit https://www.gradientai.com or follow on LinkedIn or X .

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Media Contact

Elyse Familant

Results PR

elysef@resultspr.net

5 Questions with Gradient AI’s Senior Manager, People Team, Rachel Drake

Thu, 26 Sep 2024 17:23:55 GMT

P&C INSIGHTS BLOG | Feb 26, 2024

The Gradient AI Team

Introduction: What Does an HR and Recruiting Manager Do?

Rachel is Senior Manager, People Team, here at Gradient AI – working to support Gradient AI’s recruiting efforts , as well as our current staff.

Rachel began her recruiting career over 10 years ago at a technical staffing agency. From there, she advanced to various tech companies in the Boston area, including Wayfair and Toast. When the pandemic hit, she was laid off like so many others, but came back to take a contract position at Gradient AI. At the time, Gradient had only 30 employees -- with most people reporting directly to Founder and CEO, Stan Smith . Four years and many more employees later, we’re thrilled that Rachel chose this opportunity and is now leading our recruiting and HR function!

Question 1: What is the primary focus of your role at Gradient AI?
At its core, my team's role is to attract and hire the right talent for our open positions while ensuring that our employees have a positive and fulfilling experience during their time at Gradient AI.

Question 2: What does a typical day at Gradient AI look like for you?
I enjoy the variety of my work, as no two days are the same. Currently, I’m involved in projects like organizational design, end-of-year reviews, and planning for our headcount in 2025.

Question 3: What do you like most about your job at Gradient AI?
I love connecting with all of our employees, from newcomers to senior staff, to understand their needs and challenges. By addressing these issues, we can ensure that our team thrives—after all, we are only as strong as our staff!

Question 4: What would you say to candidates considering a position at Gradient?
I recommend reviewing our company values which are: do the right thing, teamwork, and innovate . Consider whether they align with your own values and working style because they truly reflect the essence of all of us at Gradient AI.

Question 5: What do you like to do in your free time?
In my free time, I enjoy spending time with my 10-month-old daughter, our Corgi, and my husband. I love being outdoors to make the most of the good weather before winter arrives!

Bonus Question: What’s your favorite place to travel?
I have a passion for travel and have visited over 15 countries. I look forward to exploring even more destinations in the coming years with my daughter.

Thank you, Rachel! Be sure to check back on our blog as we ask 5 questions to different members of our talented staff across Gradient AI, to learn how they support the success of our clients.

Explore the benefits of Gradient AI's P&C or Workers' Comp Solutions.

Schedule a Demo →

Breckpoint Enhances Underwriting and Risk Management with Gradient AI

Mon, 23 Sep 2024 17:08:54 GMT

Self-funded Insurance Provider Improves Accuracy, Efficiency, and Risk Projection for Employee Benefits Programs

Phoenix, AZ September 23, 2024 – Gradient AI , a leading enterprise software provider of artificial intelligence (AI) solutions in the insurance industry, today announced at the SIIA National Conference that Breckpoint® has successfully adopted Gradient AI’s SAIL ^TM Solution to enhance underwriting for its self-funded employee benefits programs. SAIL has helped Breckpoint improve risk assessment, leading to more accurate pricing and better financial outcomes.

Breckpoint, a self-funded insurance provider recognized for its advanced risk financing strategies and customized self-funding arrangements for employers and groups of all sizes, chose Gradient AI to refine its existing rating tools. Gradient AI’s SAIL Solution has created a stronger, more precise data driven process in underwriting and pricing. Powered by advanced machine learning algorithms and an extensive dataset composed of medical, prescription, and lab data, SAIL has provided Breckpoint with comprehensive insights into group health risks, significantly enhancing its medical loss ratio (MLR) performance.

Breckpoint implemented SAIL to better understand specific group risks and achieve several critical goals. These included filling data gaps for new business by accessing more detailed medical and pharmacy utilization reports, improving match rates for more accurate risk projections, and streamlining processes to accelerate quote turnaround times.

“We needed a tool that could significantly enhance our risk assessment capabilities, particularly in areas where detailed group-specific data was lacking,” said Chief Revenue Officer Derek Atkinson, at Breckpoint. “SAIL has helped us to better define risk, which is critical when underwriting populations that don’t have sufficient data. These insights have been invaluable in refining our risk assessment and pricing strategies.”

By providing more accurate risk assessments, SAIL has enabled Breckpoint to price its plans more precisely, leading to better overall financial outcomes. The insurer is able to price more aggressively and win more business when the risk is low, and price commensurately with higher risk to maintain its MLR goals. Atkinson noted that recent cases involving high-cost drug utilization and pivotal treatment status were impacted by Gradient AI’s technology. “Gradient AI’s tool provided the insight we needed. Combined with our thorough analysis with brokers and customers, the information allowed us to properly assess the risk involved and price accordingly.”

“Breckpoint’s successful adoption of SAIL is a testament to the value of advanced AI in underwriting and risk management,” said Stan Smith, CEO of Gradient AI. “We support Breckpoint in its efforts to deliver more accurate and efficient insurance solutions that enhance the company’s operational performance and client outcomes.”

About Breckpoint

Breckpoint a self-funded insurance provider headquartered in Las Vegas, NV, offers bespoke, captive solutions for employers that are looking for stand-alone, group, or agency-type captive solutions. Breckpoint's programs include medical stop loss, workers compensation, risk management, and more. Its goal is to help agencies and brokers remove barriers to self-funding for their clients.

To learn more, visit https://www.breckpoint.com/ and follow Breckpoint on Linked In .

About Gradient AI

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Media Contact

Elyse Familant

Results PR

elysef@resultspr.net

Meet Sandy Boyle, Gradient AI's New P&C Customer Success Director: How She Fosters Engagement and Champions Growth for Clients

Wed, 18 Sep 2024 23:36:52 GMT

P&C INSIGHTS BLOG | Feb 26, 2024

The Gradient AI Team

What Is a Customer Success Director? And Why Is This New Role So Important to the Client Experience at Gradient AI?

Sandy Boyle serves as the Customer Success Director for the Property & Casualty line of business at Gradient AI. In this capacity, she collaborates closely with Sales, Product, and Implementation teams to advocate for clients and to ensure the successful deployment of solutions that drive profitable growth, reduce claims and underwriting costs, and enhance operational efficiency for them.

Sandy’s primary objective is to champion Gradient AI's P&C customers by understanding their workflows, aligning with their strategic goals, and facilitating their growth.

With over 20 years of experience in the insurance and Insurtech sector, Sandy is a seasoned leader in service and support. Over the past decade, she’s held leadership roles in customer success, where she has focused on developing and implementing strategies to boost adoption and enhance overall customer satisfaction with insurance claims, policy, and risk management systems. Sandy holds a bachelor's degree in mathematics from San Francisco State University, and is an active member of the Customer Experience Professionals Association (CXPA). She resides in the mountains of Western North Carolina.

We recently sat down with Sandy to ask her about her new role, and how it will help Gradient AI clients maximize their integration of AI.

Pass the Mic: An Interview with Gradient AI Customer Success Director, Sandy Boyle

Watch now →

Question 1: What can customers expect from Gradient’s Customer Success role and how do we measure success?

My role focuses on fostering a strong relationship with our customers, providing continuous support beyond implementation, and a deep understanding of customer needs to enhance the overall value of our products and services. A customer success professional is responsible for ensuring that customers achieve their goals using our products, maximizing the value of their purchase, and alleviating challenges.

Success in this role is normally measured by customer satisfaction. Many companies will do this with a standard red, yellow, or green status which is determined by a few interactions per quarter with a particular person. To truly understand if a customer is happy, we look at key metrics:

engagement
referenceability
retention
growth

My goals are to foster relationships and drive engagement at all levels, from executives to end-users, optimizing these key metrics, and ensuring clients realize the full value of our products. I believe that when customers get the most out of a product, they want to build on and share their experiences.

Question 2: Gradient recently introduced this new role of Customer Success Director. Why do you think this will appeal to clients and to those interested in Gradient’s products?

To answer this, let me start with an example from a prior position. I’ve had the opportunity to observe and analyze customer engagement levels before and after implementing a customer success team. I remember one of my first assigned customers at a prior company, I was told “not to spend too much time with them because they were on their way out”. After one call I realized the issue was that this client didn’t know how to get the value from their system because no one had spent time helping them do that. There was a mistaken belief that they were causing issues in interactions with the support team. But what they really needed was someone to advocate for them, stand by their side, and get them the information and resources needed to achieve their goals.

I stepped into that role, spent time with their administrator and a team of end users to understand what they needed to make their work easier. I worked with the configuration team to update the system to meet those needs and followed up to ensure the customer understood what had changed and how to use it. I went back to their leadership team to discuss a renewal and they were fully on board for a 5-year commitment based on the time I spent and changes we made. They went from “one foot out the door” to being a case study – and all they needed was someone to step in and help, which is the definition of the role of customer success.

Here at Gradient AI , the integration of a Customer Success Director enhances the appeal of our P&C solutions by ensuring that our AI technology seamlessly integrates with existing customer processes and systems to improve efficiency.

Gradient AI’s P&C solutions focus on these benefits:

automating administrative tasks
enhancing training
highlighting critical areas that could incur significant costs if overlooked

These benefits underscore the value of our AI offerings. With numerous AI solutions available, our emphasis on delivering consumable and accurate outputs distinguishes Gradient AI as a preferred choice for organizations seeking effective and reliable AI solutions. Adding a customer success role here helps to ensure each customer receives full service and understands how to get the best value from their AI solutions.

Question 3: How can AI best help improve the claims process, and address other challenges in the P&C insurance industry?

The value of artificial intelligence in insurance is substantial, particularly in addressing the challenges of burnout among claims adjusters and examiners, the difficulty in attracting new talent, and increasing workloads. As technology advances, the insurance industry requires corresponding advancements to remain efficient. AI can facilitate the early detection of red flags, allowing for timely interventions that support injured parties in their recovery.

AI can also help:

streamline administrative tasks through straight-through processing
enhance the identification of claims suited to specific workflows
direct the attention of supervisors, managers, and mentors to claims that may otherwise be overlooked, saving time and cost

One significant challenge for insurers is the limited talent pool, exacerbated by burnout from repetitive, routine administrative tasks. Additionally, the insurance industry may appear outdated and unappealing to younger professionals, leading to difficulty attracting new talent. AI can address these issues by modernizing the industry, making it more attractive to newer examiners. Automating routine tasks, thereby alleviating the burden on examiners, allows experienced adjusters to concentrate their expertise on more complex cases and mentoring where their skills can have the greatest impact on supporting injured parties.

Question 4: How does the Customer Success Director partner with Gradient AI’s Sales and Implementation teams, and how does that benefit our clients?

The Customer Success role is integral to both the Sales and Implementation teams, ensuring alignment and clarity throughout the customer journey. This role collaborates with Sales to provide insights that refine customer requirements, set realistic expectations, and offer feedback to bridge the gap between sales promises and delivery. During the implementation phase, Customer Success ensures that the agreed-upon solutions are effectively executed and supports the customer through full system adoption. This partnership ensures that the commitments made by Sales are fulfilled. Customer Success focuses on long-term relationships by continuously advocating for the customer throughout our partnership and highlighting new advancements that are of value to the customer.

Question 5: There is a wave of transformative AI innovation currently impacting the insurance industry. What prior industry innovations would you equate this wave to?

The current wave of AI innovation in the insurance industry can be likened to the transformative impact of transitioning to a paperless system. Going paperless streamlined insurance processes, enhanced collaboration across platforms and vendors, and significantly reduced turnaround times for invoices, bills, and correspondence. AI can revolutionize the industry in a similarly profound manner.

AI will automate routine file handling, allowing examiners to concentrate on complex claims that require their expertise. It will also assist less experienced examiners by guiding them through each claim and directing their attention to high-risk claims, thereby reducing the likelihood of significant errors. AI will not eliminate the need for mentors, training, or supervisory oversight – but it will help focus attention on the claims that need it most, thereby optimizing the overall efficiency and effectiveness of the claims process.

Question 6: We know that client feedback is critical. How do you ensure that this feedback is communicated effectively and efficiently to the product teams and then used to influence product development?

As a dedicated customer advocate, customer feedback is crucial to my role. I gather and organize relevant information to support each request, presenting it in a compelling manner that highlights its impact on the customer experience. This involves direct, proactive engagement with client executives, product managers, and engineering teams. By actively participating in meetings and discussions, I ensure that each request is addressed promptly and not lost among other priorities. This approach helps to integrate customer feedback into product development more effectively, driving meaningful improvements based on real-world insights.

Conclusion: Sandy Helps Drive Meaningful Outcomes for Gradient AI’s P&C Clients

In her role as Customer Success Director, Sandy Boyle exemplifies Gradient AI's commitment to delivering exceptional value and fostering lasting partnerships. By deeply understanding each customer's unique needs and challenges, she ensures that our AI solutions not only meet expectations but exceed them, driving meaningful outcomes. Sandy’s proactive approach, combined with her dedication to empowering customers, reinforces Gradient AI’s position as a trusted partner in the P&C insurance industry. Check back on our blog to hear more from Sandy in the future!

Pass the Mic: An Interview with Gradient AI Customer Success Director, Sandy Boyle

Watch now →

Explore the benefits of Gradient AI's P&C or Workers' Comp Solutions.

Schedule a Demo →

5 Questions with Joe Gould, Business Development Director, Property & Casualty, Gradient AI

Wed, 18 Sep 2024 12:55:18 GMT

P&C INSIGHTS BLOG | Feb 26, 2024

The Gradient AI Team

Joe Gould serves as Business Development Director, P&C, the head of Gradient AI's Property and Casualty Sales team. He’s spent nearly two decades consulting with leaders at insurance and financial services companies as they seek to leverage data and analytics to achieve their business objectives. Joe is passionate about the centrality of insurance to our society and has firsthand experience helping carriers and MGAs leverage artificial intelligence to improve claim outcomes, decrease operating expenses, and profitably expand into new markets.

A native of Maine, Joe graduated from Boston University prior to moving to the New York City area where he remains today with his family. When not working with Gradient’s clients and prospective clients, Joe likes to spend his time outdoors and with his family.

Question 1: What is the primary focus of your role as Business Development Director?

I make sure as many firms as possible can realize the positive impact of Gradient AI’s solutions for P&C insurance . More simply, I’m in Sales and collaborate with prospective clients to determine if our software can help them solve a pressing business challenge or achieve a strategic objective.

Question 2: How does AI impact the Underwriting and Claims process?

The impact varies depending on the line of business, as needs differ wildly between small business, middle market, and specialty lines for example. Broadly speaking, AI can make you more effective (think using machine learning to more accurately predict losses on a policy application for an underwriter) or more efficient (think using generative AI to save an adjuster hours by reviewing medical records). Then things get exciting when you start to stack solutions like we do at Gradient AI to make insurers both more effective and more efficient.

Question 3: What are common misconceptions insurers may have about AI and Machine Learning?

I see a concerning combination of a) underestimating the extent to which their early adopter peers and competitors already leverage AI/machine learning and b) not realizing how rapidly the underlying technology is advancing. What was cutting edge technology 12-18 months ago is now table stakes in many cases.

Question 4: What do you like most about your job?

Every day is interesting. I sometimes describe my job as having interesting conversations, about interesting technology, with interesting people. Every meeting I have with a client or prospective client puts me right in the mix of tackling important insurance issues with cutting edge technology alongside the best talent in the industry. I can’t ask for much more than that!

Question 5: What do you like to do in your free time?

These days my 9-, 7-, and 2-year-old children are the focus of any “free” time I have. We’re happy anytime we can get outside – whether it be camping, a hike, or just a trip to the park.

Bonus Question: Where is your favorite summer activity and why?

The beach! I grew up near the coast in Maine and still live in the northeast, so the beach has always played a big part of a short season for me. These days it is an easy environment for me to put my phone & laptop away for a bit and enjoy the moment.

Thank you, Joe! Be sure to check back on our blog as we ask 5 questions to different members of our talented staff across Gradient AI, to learn how they support the success of our clients.

Explore the benefits of Gradient AI's P&C or Workers' Comp Solutions.

Schedule a Demo →

Navigating AI in Workers' Comp: Insights from Industry Experts

Mon, 26 Aug 2024 12:48:02 GMT

August, 2024

By Chase Pettus

Nothing seems untouched from AI these days. Even PEO risk management. Earlier this year, I had the privilege to join a panel at NAPEO’s Risk Management Workshop on the growing influence of AI and how it impacts the world of PEO risk management.

I moderated the panel, “AI & Risk – The Role of AI in Insurance Underwriting,” and was joined by industry experts: Frank Huang, Managing Director and Consulting Actuary, Davies Group North America; Julie Cirillo, Chief Risk Officer at Engage PEO, and Justin Rowley, Vice President Risk Management, Helpside.

Case Pettus

Vice President of Sales, Group Health

Gradient, AI

The panelists provided a unique perspective of AI in the workers’ comp industry, sharing their own experiences and insights. The engaging session revolved around key discussion points, including:

What is driving the use of AI for PEOs and in the Workers’ Comp Market?
What challenges are companies trying to solve with AI?
What is the Impact of AI in Workers’ Compensation?
What results have you seen since incorporating AI?
Why are some companies slow to implement AI as part of their operations?
What advice do the panelists have for those considering AI?

For those of you who missed the event, here are the main points and key takeaways.

1. What is driving AI for PEOs and in the Workers’ Comp Market?

There are many reasons companies are motivated to use AI in insurance, including maintaining competitiveness to enhancing efficiency and addressing data limitations. Frank Huang of Davies Group North America, highlighted profitability as a common driver. He noted, “The overarching theme is profitability. Not only are companies always looking to improve the bottom line, but they want to make sure they are not being adversely selected against. Essentially, incorporating AI helps them avoid being left behind.”

2. What challenges are companies trying to solve with AI?

While Workers’ Comp is still in a soft market, insurers face pricing pressure and struggle to assess risks accurately. Justin Rowley of Helpside noted that AI helped their company add certainty to the underwriting process, especially in scenarios where data was scarce or in unfamiliar territories. He said, “we wanted to add another piece of data to the model we already used, especially in states we weren’t familiar with and for companies that didn’t have loss information, such as start-ups. AI provided that additional data.”

Julie Cirillo from Engage PEO echoed these sentiments, stressing the need for accurate risk prediction to support business expansion into new territories. She said, “As Engage PEO grew across the country and moved into new territories or industries, we wanted to accurately predict the loss for each prospect so we can price accordingly. For example, California can be a challenging state for workers’ compensation, and you had better have a tool that helps you understand the difference between underwriting a prospect in Los Angeles versus one in Sacramento.”

3. What impact has AI had on Workers’ Comp?

AI has revolutionized claims management. It has facilitated better triage and proactive risk management. Frank Huang highlighted its role in identifying high-risk cases early on, to aid in better claims management and underwriting decisions.

“With AI, you could have 100 cases come in and you will be able to identify and triage the cases that are most likely to incur higher costs, result in potential litigation, or involve greater claims management,” said Huang. “AI models also impact claims because of the year-round and heavily manual processes that have historically been used to manage claims, aggravated by the turnover in experienced complex claims adjusters.”

4. What results have you seen since incorporating AI?

Justin Rowley shared that AI has not only been helpful to Helpside in pricing new clients, but also in its renewal business. “The biggest impact AI has had on us, has been increased certainty in the underwriting process,” said Rowley. “We still use loss runs and an actuarial model when possible. I have built a dashboard that allows me to see the actuarial predicted losses as well as the AI predicted losses and we make decisions from there.”

Rowley also noted that AI also provided greater insights into prospects with data gaps, enhancing underwriting efficiency.

Julie Cirillo noted that AI has allowed Engage PEO to build a sustainable underwriting model and support their M&A activities effectively. “I needed to know that regardless of which underwriter looked at the potential risk of a prospect, we would get consistent results,” said Cirillo. “This solution has assisted in integrating our acquired companies, allowing for fast and accurate review of both the portfolio and individual clients to ensure profitability is maintained with the transition to our risk-bearing program.”

5. Why are some companies slow to implement AI as part of their operations?

Fear and lack of knowledge are key factors hindering AI adoption, according to Frank Huang. The audience provided feedback on potential barriers as well, expressing concerns around cost and change to underwriting teams experience and habits. Julie Cirillo pointed out concerns about costs and changes to established workflows.

“An AI model may not seem cheap,” said Julie Cirillo. “But you have to trust in your investment. If I could show you the profitability for Engage PEO on the Workers’ Comp line, it is significant. And it is significantly improved year over year. I credit that to our AI model that we are selecting the right risk and pricing it correctly. The investment has paid for itself.”

6. What advice do the panelists have for those considering AI?

Frank Huang advised understanding the specific problems AI can solve, while Julie Cirillo stressed the importance of trusting the investment in AI for long-term profitability. Justin Rowley emphasized that AI should complement, not replace, solid business practices.

“I think we all want to work towards better profitability,” said Justin Rowley. “But keep in mind, AI does not replace solid underwriting practices or business practices. Utilize AI to enhance these practices.”

In conclusion, the panelists agreed that AI presents a significant opportunity for PEOs in enhancing efficiency, profitability, and risk management. Embracing AI strategically can position PEOs for success in an increasingly competitive landscape.

This article first appeared on PEOInsider.

Transforming Insurance: How Centana Growth Partners and Gradient AI are Redefining Risk Management

Tue, 20 Aug 2024 16:40:20 GMT

P&C INSIGHTS BLOG | Aug 20, 2024

The Gradient AI Team

This is a summary of an article that first appeared on Centana’s website on July 30, 2024. You can read the original here: “Why Centana is Leading Gradient AI’s Series C”.

Centana Growth Partners recently led a $56 million Series C round in Gradient AI . This investment was based on Centana’s deep experience investing in enterprise solutions serving the insurance and financial services ecosystem, including a strong focus on underwriting software and AI infrastructure. The firm’s thematic work and long-time connections to multiple companies in the insurance industry including insurance carriers, brokers, employer groups, and more, highlighted the necessity of data-driven tools to help insurers grow profitably.

Centana’s decision to lead the round underscores the transformative potential of artificial intelligence in the insurance sector. Here’s a peek into why Centana believes that this partnership is poised to revolutionize the industry.

The Critical Role of Risk Management in Insurance

Risk management is crucial in the insurance industry, in both underwriting and claims. Effective underwriting involves evaluating new and existing policyholders to assess risk factors and price policies accurately, enabling the insurer to maintain healthy loss ratios and drive profitable growth. On the other end of the business, claims management involves processing thousands of complex claims annually, ranging from workplace injuries to high-cost medical cases. Efficiently prioritizing and managing these claims helps insurers control costs, while enabling policyholders to recover faster.

AI’s Impact on Underwriting and Claims Management

Traditional underwriting can require significant manual effort. Health insurance underwriters, for instance, have relied on health questionnaires and actuarial consultants, often resulting in suboptimal outcomes, and missed opportunities due to gaps in claims and health histories. Regulatory changes, such as the Affordable Care Act, have further complicated the process by restricting the use of disclosed health histories in underwriting.

In claims management, insurers must consider numerous attributes to determine the appropriate action for each claim, whether it involves settling, contesting, or requesting additional information. This complex process requires navigating many questions and decisions related to costs, claim reserves, potential legal representation, and other factors. Handling serious claims, particularly those involving major injuries, demands particular attention and expertise. Some of the most problematic cases begin as minor issues but escalate into high-cost claims. In the worst scenarios, these claims grow unnoticed until they become catastrophic cost drivers, often too late for insurers to control expenses effectively.

AI addresses these underwriting and claims management issues:

AI in Underwriting: AI-driven models leverage third-party data sources, such as anonymized medical, lab and prescription records, to enhance the accuracy of risk assessments, helping to predict health indicators and future claims. This leads to better pricing and improved customer experiences.
AI in Claims Management: Managing a claim involves numerous attributes and decisions, from settlement to legal considerations. AI provides insights into claim histories and future trajectories, enabling insurers to prioritize and proactively address potential high-cost outcomes.

Gradient AI’s Cutting-Edge Insurance Solutions

Centana believes that Gradient AI’s AI solutions for insure r s stand out for their ability to deliver predictive analytics and data-driven insights across the group health and property and casualty insurance markets, offering insurers a deeper understanding of risk and performance with:

Enhanced Underwriting: Gradient’s AI-driven health underwriting product, SAIL, provides risk scores for new employee groups, aiding underwriters in making informed decisions. This solution is both explainable and HIPAA-compliant, ensuring transparency and regulatory adherence.
Improved Claims Management: Insurers using Gradient AI’s P&C and Workers’ Comp solutions benefit from superior visibility into claims, allowing them to act swiftly and effectively to mitigate costs.

Looking Ahead: Centana and Gradient AI Bring Complementary Strengths to the Partnership

Centana brings extensive experience in investing in enterprise solutions for the insurance and financial services sectors. Their team focus on underwriting software and AI infrastructure aligns with Gradient AI’s mission to transform insurance operations.

Centana believes that Gradient AI, under the leadership of Founder & CEO Stan Smith , is well-positioned to lead the industry in AI-driven underwriting and claims management. With decades of experience in insurance, healthcare, and software, Stan and his team have built a strong foundation for continued innovation and growth. Centana’s investment will enable Gradient AI to further invest in product development and market expansion, solidifying its competitive edge and enhancing customer satisfaction.

The partnership between Centana and Gradient AI marks a milestone in the evolution of the insurance industry. By leveraging AI to enhance underwriting and claims management, insurers can achieve better risk assessment, improve operational efficiency, and ultimately drive profitability. As the global market for AI in insurance continues to grow, Gradient AI’s innovative solutions and Centana’s backing are set to lead the way in transforming the sector.

>> This is a summary of an article that first appeared on Centana’s website on July 30, 2024. You can read the original here: “Why Centana is Leading Gradient AI’s Series C”.

Explore the benefits of Gradient AI's P&C or Workers' Comp Solutions.

Schedule a Demo →

Transforming Insurance: How Centana Growth Partners and Gradient AI are Redefining Risk Management

Tue, 20 Aug 2024 16:40:17 GMT

GROUP HEALTH INSIGHTS BLOG | Aug 20, 2024

The Gradient AI Team

This is a summary of an article that first appeared on Centana’s website on July 30, 2024. You can read the original here: “Why Centana is Leading Gradient AI’s Series C”.

The Critical Role of Risk Management in Insurance

AI’s Impact on Underwriting and Claims Management

AI addresses these underwriting and claims management issues:

AI in Underwriting: AI-driven models leverage third-party data sources, such as anonymized medical, lab and prescription records, to enhance the accuracy of risk assessments, helping to predict health indicators and future claims. This leads to better pricing and improved customer experiences.
AI in Claims Management: Managing a claim involves numerous attributes and decisions, from settlement to legal considerations. AI provides insights into claim histories and future trajectories, enabling insurers to prioritize and proactively address potential high-cost outcomes.

Gradient AI’s Cutting-Edge Insurance Solutions

Enhanced Underwriting: Gradient’s AI-driven health underwriting product, SAIL, provides risk scores for new employee groups, aiding underwriters in making informed decisions. This solution is both explainable and HIPAA-compliant, ensuring transparency and regulatory adherence.
Improved Claims Management: Insurers using Gradient AI’s P&C and Workers’ Comp solutions benefit from superior visibility into claims, allowing them to act swiftly and effectively to mitigate costs.

Looking Ahead: Centana and Gradient AI Bring Complementary Strengths to the Partnership

>> This is a summary of an article that first appeared on Centana’s website on July 30, 2024. You can read the original here: “Why Centana is Leading Gradient AI’s Series C”.

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Understanding AI’s Impact on Group Health Underwriting: The Carrier Perspective

Mon, 19 Aug 2024 12:48:22 GMT

GROUP HEALTH INSIGHTS BLOG | Aug 19, 2024

The Gradient AI Team

Artificial intelligence is being discussed over many media platforms and across multiple industries. But as it pertains to Group Health Underwriting in the Carrier space, who is using it and why? How are underwriters using data, coupled with artificial intelligence and advanced analytics, to win new business and optimally manage their book of business?

Gradient AI recently sponsored a webinar, “ Exploring the Impact of AI on Health Carrier Underwriting: Expert Perspectives” hosted by Becker’s Payer Issues , that explored these questions and more.

Our expert panelists were:

Mike Harris , VP of Sales for Major Group, Chamber, Association, and Student Health Plans, BlueCross BlueShield of South Carolina
Jay Sheehy , Chief Underwriting Officer, Gravie
Jason Oswald , Director of Solution Consulting, Group Health, Gradient AI

Mike Harris has over 35 years of industry experience and in-depth knowledge of the fully-insured, ASO, and Stop Loss business. Jay Sheehy has an extensive background in health benefits, underwriting and product innovation. Prior to joining Gradient AI, Jason Oswald spent 15 years in health insurance actuarial and underwriting roles including managing the underwriting department for a Regional Payer. These three industry practitioners each brought a distinct perspective to the conversation.

Below, we’ve summarized the important highlights from the engaging 45-minute panel discussion.

Insights from the Webinar “Exploring the Impact of AI on Carrier Health Underwriting: Expert Perspectives”

In this on-demand webinar , the panelists provided a unique view of AI in the Group Health Carrier space and shared their first-hand experiences with the AI in their own companies.

The discussion focused on these 6 questions:

What is driving Carriers to consider AI in Group Health Underwriting?
What challenges are Carriers trying to solve with AI?
What impact has AI had on Group Health Carrier underwriting?
What results have you seen since integrating AI?
How did you handle change management when implementing AI?
What is the future of AI? What advice do you have for other Carriers considering AI?

Let’s dive deeper into these discussion points.

1. What is Driving Carriers to Consider Using AI in Group Health Underwriting?

The panelists discussed how Health Carriers are looking to gain deeper insights into group risk to make better and faster underwriting decisions. They described how AI can help Carriers capture more business, expand business opportunities, and improve overall plan performance.

The panelists agreed that by leveraging AI, insurers can speed up decision-making, prioritize favorable risks, and manage less desirable risks more effectively. This approach not only optimizes the workflow but also ensures that resources are allocated more strategically, leading to better outcomes for both the insurer and the insured.

Jason Oswald believes that “being able to get a decision faster moves the good risk to the top of the pile and allows Sales, Underwriting, and Management teams to focus on winning those groups, while bad risk gets de-prioritized because companies do not have unlimited resources and time.”

2. What Challenges Are Carriers Trying to Solve with AI?

While the underlying goal across Group Health Carriers is speed, accuracy and profitability, the specific challenges or objectives for implementing AI can vary from company to company. This can range from planned expansion and growth into new markets or industries or bolstering an understaffed or inexperienced underwriting team.

BlueCross BlueShield of South Carolina (BCBSSC)’s Mike Harris was looking for alternative ways of looking at potential risk, particularly in the under 150 market, and for ways to fend off the competition. “As a market leader, we have a target on our backs,” said Harris. “And the market is very competitive. The challenge, particularly with smaller groups, is you get limited data and experience. We needed a better way to evaluate risk and sales, and underwriting needed to better validate where we needed to be aggressive.”

Jay Sheehy of Gravie, a new but growing Payer, had a unique challenge and advantage. Unlike larger Carriers who have years of usage with a legacy system, Gravie needed to build out their processes and tools from scratch. “We really needed to address scale,” said Sheehy. “We were trying to have the appropriate balance between machines (AI) and humans. We really want our underwriters to focus on the financial decision-making part and let the machines do a lot of the analytics.”

3. What Impact has AI Had on Health Carrier Underwriting?

Providing a broad industry perspective, Jason Oswald reiterated Jay’s comments about the value of combining Machine Learning, AI, and underwriters. AI helps identify groups to focus on and can even slot them into risk tiers. Underwriters can then use that as a data point and push more quotes through at the favored risk levels. “AI doesn’t run the ship,” said Oswald. “It doesn’t operate on its own but when paired with a skilled underwriting team that has been trained and understands the tool, it helps gain efficiencies and allows the team to focus time and resources on the groups you want to win.”

Collectively, the panel emphasized the importance of getting as much data as possible to make accurate risk predictions, while the lack of data makes underwriting that much more challenging. AI can not only help fill in blind spots but reduce time and resources by helping guide risk projections. “Predictive models can give you 300 different scenarios in minutes as opposed to an underwriter trying to manually calculate even one scenario in an hour,” said Jay Sheehy. “Having more data in real time is essential for doing a good job for the customer.”

4. What Results Have You Seen Since Integrating AI?

For Mike Harris, leveraging AI was a great opportunity for BCBSSC to figure out how to target specific accounts under 150 and to carefully select which business to write. AI enhanced whatever data the customer or consultant provided. “With Gradient AI’s tool, it just opened the door for sales to go to our underwriting or actuarial teams when we needed to get extremely aggressive on a new piece of business,” said Harris. “Having the Gradient AI scores, coupled with the information from the prospect or consultant to evaluate the risk, gave us an extra viewpoint to make the proper risk assessment.”

BCBSSC began using AI on a test basis before expanding its use. “We initially rolled out the Gradient AI tool for just one of my segments, chamber block and associations,” said Harris. “In year two, my underwriting and actuarial teams said, ‘let’s use this for all of your segments.’ I can honestly tell you that would not have happened if we were not seeing the value of the program.”

Gravie’s Jay Sheehy felt measuring results based on turnaround times, accurately loading data, and running risk models quickly through their actual risk predicators was extremely important. He shared Gravie’s improved outcomes as a result of using AI:

Consistently beating their 6-day turnaround time goal
Notable improvement in data load times and accuracy
Repriced and refined risk projections for over 200 groups

“Our initial pricing was accurate to start with,” said Sheehy. “But AI showed us some areas where we may have been a little too conservative and had room for improvement. Plus, I think our underwriting team actually has fun. They realize they are not just calculating numbers; they are empowered to make financial decisions.”

5. How Did You Handle Change Management When Implementing AI?

“From a sales perspective, I think the main pushback initially was the perceived extra work involved,” said Mike Harris, whose sales teams run the scores and present them to the underwriters as part of the RFP package. “But once they got comfortable and they recognized the speed and accuracy the AI tool delivers, I think they were very comfortable.”

As for the underwriting team, Harris noted “Our underwriters were hesitant at first, but once we did a test segment and they saw the results, they suggested utilizing the tool across all segments because they recognized the value of AI.”

Jay Sheehy said that Gravie did not have the same change management challenges as more mature companies. “I was employee 75 when I started,” said Sheehy. “We are now up over 650 employees and so as a company, AI allows us to scale more accurately and more effectively. We had the underwriting team be a big part of building our process with AI, so they don’t view AI as a solution, they look at AI is as their solution.”

6. What is the Future of AI? What Advice Do You Have for Other Carriers Considering AI?

The panelists wrapped up the webinar with these insights:

Jason Oswald: “AI is here to stay. It behooves Group Health Carriers to investigate and engage at some level. Companies need to evaluate how they might use AI to match market goals, how they integrate it based on the experience of their team as well as the level of technological investments they are willing to make.”

Jay Sheehy: “At Gravie, we vigorously tested the technology and how it was used and then trained individuals on how to use it. We also recognized it is just one tool of many to get the best results… We’re just scratching the surface on how you really provide better financial protections though AI. And that’s just one element. How do you use the information from AI to better understand what an employer and their members truly need and customize solutions and products that provide significant value?”

Mike Harris: “I agree with Jay in that we are just scratching the surface with AI. We will be able to leverage AI to create particular programs that will help employers with their specific employee needs and with more intelligence and more data yet to come. There will be unlimited opportunities.”

To Learn More, Listen to the On-Demand Webinar

>> To hear more from the panelists about AI for Carriers, listen to the on-demand webinar, Exploring the Impact of AI on Health Carrier Underwriting: Expert Perspectives , hosted by Becker’s Payers Issues, and sponsored by Gradient AI.

About Becker’s Payers Issues

Becker's Payer Issues is the leading news organization covering the U.S. health insurance industry. The editorial team delivers daily breaking news, business analysis and expert interviews across the evolving insurance landscape, delivering the latest need-to-know information for the industry's executive leaders.

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CIO Influence Interview with Stan Smith, Founder and CEO of Gradient AI

Tue, 13 Aug 2024 16:30:37 GMT

August 13, 2024

By Rishika Patel

Stan Smith, Founder and CEO of Gradient AI, shares about the complexity and scale of data in the insurance industry, AI-driven strategy for insurance carriers to improve underwriting and claims operations and more in this Q&A:

Hi Stan, welcome to our CIO Influence Interview Series. Take us through your journey to founding Gradient AI and what it’s like as a SaaS CEO today?

Thank you! My background is in growing AI and technology organizations that address significant business needs, which I have been doing for the past 30 years.

At a previous company, I witnessed first-hand the inefficiencies and mistakes occurring within the insurance industry because of compounding data issues. Insurers were spending an awful lot of time collecting data scattered across siloed business units on paper or in other unstructured digital formats. Underwriting and claims management professionals didn’t have access to the information they needed to make decisions, and they were assessing risk using traditional approaches that have largely been in place for decades, if not centuries.

Determined to address these business challenges, I launched Gradient AI, a SaaS organization focused on applying AI and machine learning technologies to help the insurance industry operate more efficiently and effectively. As a SaaS CEO today, it’s both challenging and rewarding. Every day involves making strategic decisions to drive innovation, managing a talented team, and ensuring we deliver exceptional value to our clients. The dynamic nature of AI technology keeps us constantly adapting and growing.

Gradient AI has been at the forefront of integrating AI and machine learning in the insurance industry. How do you see these technologies evolving in the next five years?

We’re very focused on helping insurers to leverage AI for underwriting and claims processing, and as a result, they are better able to assess risk and take action accordingly.

In the next five years, I see AI and machine learning expanding to become even more integral to the insurance industry. Technologies like large language models (LLMs) and generative AI will accelerate adoption and usability. These advancements will make AI tools more accessible for insurers. For example, to summarize medical records and other documents, and even query vast previously inaccessible knowledge bases, consulting decades of accumulated claims data, medical notes, and outcomes to improve decision-making in their underwriting and claims management operations.

AI will also evolve to provide more predictive and prescriptive analytics, helping insurers make even better decisions faster and more accurately.

What are the key challenges you faced when founding Gradient AI, and how did you overcome them to establish a leading position in the industry?

One of the key challenges I faced and continue to face is overcoming misconceptions about AI. Many potential clients have false beliefs about what can and can’t do. We address this by demystifying AI and demonstrating its practical applications in the insurance sector. It isn’t a black box, but rather a data-driven, trained computer model, which enables insurers to assess risk, make decisions, and take action in a much more informed way than with traditional approaches.

We focus on specific use cases and provide clear proof points through data and results. This pragmatic approach has helped us build trust and establish a strong reputation in the industry.

Given the complexity and scale of data in the insurance industry, how does Gradient AI ensure the accuracy and reliability of its AI models and predictions?

We ensure accuracy and reliability through a contributory data model. This approach involves aggregating data from multiple clients across various systems. By having access to a broader dataset, we can validate our models against diverse information, ensuring they perform well across different scenarios. This not only helps in assessing model accuracy but also in identifying and mitigating biases.

Our industry data lake contains tens of millions of structured and unstructured underwriting and claims records. All of the data is de-identified to ensure anonymity. Beyond the contributory sources, we’ve further enhanced it by adding third-party data sources, such as medical, prescription, and laboratory data, as well as weather, crime, and other demographic data points, and more. We added these additional features that go beyond basic data points because they enable AI models to be more accurate predictors.

Of course, ensuring the security of data is paramount, and Gradient AI is SOC2 compliant and HITRUST certified.

In your experience, what are the essential components of a successful AI-driven strategy for insurance carriers to improve underwriting and claims operations?

The essential components include:

Clear Objectives: Define what you want to achieve with AI. Is it growth, profitability, or operational efficiency?
Data Integration: Leverage contributory data models to enhance the robustness of AI predictions.
Employee Support: AI should assist employees by suggesting actions and streamlining processes, especially in environments with high turnover and distributed workforces.
Proof Points: Demonstrate the effectiveness of AI through clear, tangible results.

We also think it’s important as an AI solution provider to partner with our clients to ensure they get the maximum value from their AI implementation. Whether that has to do with helping them identify the most pressing business challenges for which AI is well-suited, preparing their data, or proving the value of AI in their operations, we work closely with them to make sure all those needs are fully addressed.

How important are integrations and partnerships in the AI and insurance landscape, and how has Gradient AI approached fostering such relationships?

Integrations and partnerships are critical. We believe AI should be seamlessly integrated into existing workflows, where users don’t even realize it’s there. Our strategy involves deeply embedding our products into partner solutions, enhancing their capabilities and making the AI experience smoother for end-users. We often refer to it as “AI at the point of decision,” since it’s about integrating with the ways our customers already do business rather than requiring their teams to learn new processes or take additional steps. Collaborating with other companies allows us to offer the best solutions because we can leverage each other’s strengths.

What innovations at Gradient AI are you particularly excited about, especially those that might redefine industry standards in the near future?

I’m particularly excited about our work in the next-best-action models. These models use AI to provide straightforward applications for handling complex data quickly. On the underwriting side, they help underwriters by highlighting key episodes of care, allowing for more accurate pricing. On the claims side, they suggest the most efficient ways to handle claims, improving cost efficiency and customer satisfaction. These innovations are set to redefine industry standards by automating mundane tasks and allowing professionals to focus on high-value activities.

Finally, what advice would you give to IT leaders who are looking to leverage AI and ML to drive digital transformation in their organizations?

The insurance industry is 500-years old and understandably the people who work in it are risk-averse. That said, insurers that don’t leverage AI for their businesses will be at a major disadvantage. Other firms will leapfrog over them in much the same way that Uber, Amazon, and Airbnb disrupted entire industries with digital transformation.

I would advise insurers to not procrastinate, as a similar disruption is underway in the insurance industry. They should start by developing a clear vision of the strategic levers they want to move, whether that is to reduce costs, improve the customer experience or offer a new and differentiated service. The objective should be to invest in AI in ways that benefit your business model, to achieve a positive, significant, and sustainable impact on your business.

Stan Smith , founder and CEO of Gradient AI, has been working with AI and technology companies for nearly 30 years. By applying the latest Artificial Intelligence and Machine Learning advancements to the data-intensive insurance industry, Stan founded a company that enables insurers to significantly improve their underwriting and claims operations, resulting in enhanced loss ratios and profitability.

Prior to Gradient AI, Stan held founding or executive-level roles with multiple startup companies including MatrixOne, Agile Software, and OpenRatings. He also led development of several patents including technology that predicts bankruptcies, a global database to improve supplier performance, and technology that enhances performance management through lean initiatives. Stan earned his bachelor’s degree from Dartmouth College.

This article first appeared on CIOInfluence .

Artificial Intelligence Helps Mitigate Social Inflation in P&C Insurance

Mon, 05 Aug 2024 18:21:40 GMT

P&C INSIGHTS BLOG | Apr 29, 2024

The Gradient AI Team

Social inflation has become a significant issue in the P&C insurance industry , marked by an uptick in claim severity that outpaces typical economic inflation and standard claim trends. This challenge threatens the financial stability of insurers by straining their reserve levels. However, AI provides powerful tools for enhanced risk assessment and claims management, offering a way for P&C insurers to effectively navigate these complex pressures.

Key Drivers of Social Inflation

Several factors contribute to the rise in social inflation:

Increasing Litigation Rates - The number and severity of litigated P&C casualty claims are rising faster than overall claim trends, adding financial strain on insurers.
Costly Verdicts - There has been a big increase in 'nuclear verdicts,' where juries award settlements far exceeding expectations, heavily impacting insurers financially.
Public Distrust of Corporations - Growing public distrust of large corporations is being leveraged by some attorneys to secure larger settlements or avoid trial on defendable cases.

In addition, at Gradient AI we’ve also seen an influx of lawsuits, with so-called "bad actors" increasingly using sophisticated methods, including AI, to target specific geographic areas or types of organizations, such as long-term care facilities. These actors identify regions with a high concentration of potential claimants and engage with specialized lawyers who focus on such high-risk cases. What this means for insurers is a heightened level of unpredictability and risk in their portfolios which means they face an uphill battle in maintaining profitability and ensuring adequate reserves.

AI Strategies Can Mitigate Social Inflation

To combat social inflation, insurers need advanced methods, including AI, to identify litigation patterns and minimize impacts. Here are three ways artificial intelligence can assist:

Predictive Analytics to Prevent Catastrophic Claims
AI-driven predictive analytics can significantly reduce claim severity and optimize reserve allocation. By continuously monitoring claim data in real-time, AI flags high-risk cases early, preventing escalation and allowing cases to be resolved out of court. AI's ability to analyze extensive datasets and accurately predict outcomes, allows insurers to anticipate high-severity claims and allocate resources efficiently.
Streamlining Claims Management with AI
AI enhances claims processing by automating routine tasks, speeding up decision-making, and improving overall efficiency. This reduces the time and resources needed for each claim, minimizing litigation and settlement costs. AI also aids in triaging claims, prioritizing those needing immediate attention for timely resolution.
Improving Underwriting Practices with AI
Social inflation is driving insurers to refine their underwriting and pricing processes too. AI uses a broader range of data points for risk assessments, leading to more accurate pricing models that reflect true risk profiles. By analyzing historical loss data, economic indicators, and social media trends, AI provides comprehensive risk assessments, enabling insurers to set accurate pricing and make informed underwriting decisions.

Helping Insurers Build a Stronger, More Profitable, and Customer-Centric Industry

While social inflation poses significant challenges, AI offers viable solutions. Advanced tools for predictive analytics, risk assessment, and claims management enable insurers to tackle social inflation effectively. AI is transforming the insurance industry by streamlining operations and facilitating data-driven decisions.

To stay ahead, insurers must continually refine their AI strategies to address new challenges and regulatory demands. Embracing AI's potential helps insurers build a stronger, more profitable, and customer-centric industry, effectively managing social inflation and other emerging issues.

>> Discover how Gradient AI's solutions can help you tackle social inflation and enhance your insurance operations. Visit our P&C insurance solutions page for more details.

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Guest Post: 6 Change Management Tips for Achieving AI Success in Workers’ Compensation

Thu, 01 Aug 2024 20:06:55 GMT

August 1, 2024

By Jim Bowers

AI offers P&C insurers enhanced workers’ compensation outcomes including:

Improved Claims Management– AI provides early warnings for costly claims, allowing proactive interventions that reduce claim duration and costs. It can reduce the legal involvement in lost-time workers’ comp claims by up to 15%, lowering claim costs by about 5%, according to a study of more than 200,000 claims.
Enhanced Underwriting– Insights driven by AI enable insurers to identify underwriting risks with greater speed and accuracy, improving loss ratios and competitive positioning.

Operational Efficiency– AI supports straight-through processing, guiding newer employees for consistent and effective claims handling, accelerating market expansion, and managing risk effectively.

Jim Bowers

P&C Senior Solutions Consultant, Gradient AI

However, successful AI implementations often require a strategic change management process to manage the unique challenges involved. Implementing AI into workers’ compensation programs is like being a tour guide. You wouldn’t start leading a group without first planning the route, checking the weather forecast, and knowing the hours of operation for points of interest. Similarly, thoughtful change management activities will help your organization successfully prepare for the journey of AI implementation.

Consider these 6 steps as your playbook for success. Your plan details may vary, but having an intentional and structured approach in place removes the worry and supports successful adoption.

Step 1: Pre-Planning Activities

Pre-planning should start 30-90 days before the change initiative.

Complete Change Management Overview
Understand the fundamentals of change management and familiarize yourself with tools like the ADKAR model , based on the principle that organizational change can only happen when individuals change.

Review Best Practices for Leading Change
Consider the importance of your managers and leaders in creating your plan. Prosci Research shows that most employees prefer to receive personal messages about changes directly from their managers.

Get to Know the Change Management Resources Available
Leverage your internal change management tools to simplify the change process.

Create a Change Management Community of Practice
Implement a Community of Practice to share experiences, issues, and success stories. This is a great way to learn from peers and other change management stakeholders. Consider this to be like your favorite travel blog or online travel community.

Step 2: Change Preparation

Change preparation is the first step after preplanning and will run concurrently with other steps as your plan ramps up.

Identify Stakeholder Group
Identify stakeholders and determine their required level of engagement.

Evaluate Stakeholder Change Readiness
Complete a stakeholder mapping exercise to understand the level of impact of the change on each stakeholder team and to determine their readiness. This is also a perfect time to complete a baseline change readiness survey.

Facilitate Discovery Sessions
Gather insights from key stakeholders to gain a wider perspective, as an early way to communicate the upcoming change, and to determine the level of communication and involvement expected and needed.

Coordinate with the Technical Project Manager for Alignment
This step helps align technical and change management implementation activities and timelines.

Create an Initial high-level Change Plan
This will help keep things on track and assist in other steps. In addition to the Change Plan, this is a perfect time to complete a RACI (Responsible, Accountable, Consulted, Informed) assessment to align all change team members, internal and external, on roles and responsibilities.

Step 3: Change Impacts

The purpose of this step is to identify and assess key business process changes to incorporate into your Change Plan as mitigation strategies. Typically, you will start this step shortly after Change Preparation and they will run concurrently as the project progresses.

Complete a Change Impact Assessment
Work with the core team that will be impacted as well as upstream and downstream teams to identify key process changes. This is like calling out detours and highlights on your tour route.

Develop and Incorporate Mitigation Strategies into the Change Plan
Understanding the new business process changes and risks will allow you to develop mitigation strategies to support success and minimize risk.

Consider Impacts to People, Process, and Technology
This includes changes to how, when, and where work gets done; customer impacts; skill gaps and related training needs, and more.

Step 4: Communication

Build awareness, understanding, and successful adoption. A comprehensive communication plan will deliver the right amount of communication to the right stakeholders at the right time.

Develop an Initial Communication Plan
This plan will involve multiple channels of delivery and will be customized to each stakeholder based on the prior work you have done. Be consistent and timely with your communications.

Tailor Communications to the Right Stakeholders
While the messaging and content may be similar, the level of detail, the timing, and the format of the communication will likely look different for different stakeholder groups. Tailor communications to optimize their impact.

Utilize Leaders, Managers, and Change Agents
Meet people where they are. Leverage current team meetings and other current communication forums to spread the word and keep everyone updated. Remember that most employees would rather hear change news and updates from their direct manager.

Step 5: Training

Training may look different for each organization and specific implementations, but there are some best practices to follow. Consider training your Change Agents first.

Conduct a Training Needs Assessment
Determine who needs training, what do they need to learn, and the best delivery methods. Tailor training to specific roles. Define what successful training looks like. Are there other learning events and job changes to consider?

Develop and Deliver Training
Consider just-in-time training to promote retention and quick practical use of what they have learned. The plan should be detailed and should build staff confidence while addressing any knowledge gaps.

Step 6: Organizational Readiness

Organizational readiness begins with your first survey and stakeholder interviews and continues throughout the change cycle.

Readiness Assessments
Conduct the initial assessment as a baseline and follow-up readiness assessments to gauge preparedness. Use informal feedback, conversations, and surveys to identify gaps. Those gaps could be awareness, skills, sponsorship, organizational culture, and more.
Pulse Surveys
Regularly distribute pulse surveys to track progress and address any emerging issues. Balance the frequency to avoid survey fatigue.

Post AI Implementation: Sustaining the Change

Post-implementation, it’s important to keep your process changes moving forward smoothly to ensure long-term success. This involves continuous measurement, after-action reviews, and a solid sustainment plan.

Key post-implementation activities include:

Measuring Success -Use predefined metrics to evaluate the effectiveness of the change and identify areas for improvement. Think of it as asking tour participants to fill out feedback surveys at the end of the tour to improve future experiences.
After-Action Reviews -Reflect on what worked and what didn’t to learn and improve future initiatives, like reviewing the tour with your team afterward.
Sustainment Plan -Ensure business-as-usual resources are in place to support ongoing training needs and address any post-deployment issues, much like ensuring your tour guides are always prepared for the next group.

Implementing AI into a workers’ compensation program is a complex but rewarding endeavor. By following a structured change management process, organizations can overcome resistance, build stakeholder engagement, and achieve successful AI adoption, leading to enhanced workers’ compensation outcomes.

About Jim Bowers
Jim Bowers is the P&C Senior Solutions Consultant at Gradient AI , and brings over 25 years of leadership in the Workers’ Compensation and P&C insurance industry. He collaborates across teams to deliver innovative solutions that drive profitable growth and enhance customer experiences. With a background in underwriting and claims leadership, Jim is recognized for his expertise in implementing technology-driven strategies. He’s had numerous speaking engagements at industry conferences, publication in leading journals, and receipt of the MPower award for pioneering programs. Jim was a 2024 speaker at the NCCI Annual Insights Symposium and a prior speaker at the National Workers’ Compensation & Disability Conference. He also previously received the MPower award with his team for an innovative medical bill review straight-through- processing program. Jim has a bachelor’s degree in business from Grove City College, and an MBA from Bowling Green University.

This article first appeared on WorkCompWire.

Startup that harnesses AI for the insurance industry gets $56M investment

Wed, 31 Jul 2024 13:46:33 GMT

July 31, 2024

By Lucia Maffei – Technology Reporter, Boston Business Journal

The Seaport startup will be putting the latest fundraise to use to increase its top and bottom lines. "The faster we grow, the faster we have other options.

Want to continue reading?

CLICK HERE to read the full article.

Stan Smith, founder and CEO of Gradient AI, acquired the business from then-parent company Milliman in 2018. Gradient AI is based in the Seaport.

This article first appeared on Boston Business Journal.

Gradient AI raises $56 million

Tue, 30 Jul 2024 13:45:49 GMT

July 30, 2024

Gradient AI, a provider of artificial intelligence solutions for the insurance industry, announced a $56.1 million Series C funding round led by Centana Growth Partners, with participation from existing investors MassMutual Ventures, Sandbox Insurtech Ventures and Forte Ventures.

The funds will be used to support product development and to further bolster customer success and sales functions.

Founded in 2018, Gradient AI offers solutions for group health, P&C, and workers’ comp insurance companies. The software analyzes “tens of millions of policies and claims” to help companies make better underwriting decisions.

“While we are gratified to secure this significant investment from both Centana and our existing investors, this is just the first step. Now it’s up to us to use this funding wisely, enhancing our platform and delivering unparalleled value to our customers. Insurers are becoming increasingly sophisticated in their risk assessment and are focused on improving their operational efficiencies. We are helping them achieve these goals by automating processes, reducing costs, and significantly improving results.” – Stan Smith, CEO of Gradient AI.

“Gradient AI has an experienced executive team and products that produce demonstrable ROI for its customers which, together, have helped Gradient deliver strong financial results. The company is at an important inflection point, and we are excited to partner with Stan and his stellar team. In addition to growth capital, we look forward to providing our knowledge and industry connectivity to assist Gradient in product expansion and go-to-market activities that continue to benefit customers and extend their competitive advantage.” – Matt Alfieri, Partner at Centana Growth Partners.

This article first appeared on Coverager .

Gradient AI Secures $56 Million in Series C Funding to Expand AI-Powered Insurance Solutions

Tue, 30 Jul 2024 13:11:15 GMT

Led by Centana Growth Partners, Funding Will Accelerate Product Development, Expand Range of Solutions, Invest in R&D

Boston, MA-July 30, 2024 - Gradient AI , a leading enterprise software provider of artificial intelligence (AI) solutions in the insurance industry, today announced that it has raised $56.1 million in Series C funding.

The round was led by Centana Growth Partners , with participation from existing investors MassMutual Ventures, Sandbox Insurtech Ventures and Forte Ventures. The funds will be used to support product development to continue driving innovation and efficiency in the insurance industry and to further bolster customer success and sales functions.

"While we are gratified to secure this significant investment from both Centana and our existing investors, this is just the first step," said Stan Smith, CEO of Gradient AI. "Now it’s up to us to use this funding wisely, enhancing our platform and delivering unparalleled value to our customers. Insurers are becoming increasingly sophisticated in their risk assessment and are focused on improving their operational efficiencies. We are helping them achieve these goals by automating processes, reducing costs, and significantly improving results.”

“Gradient AI has an experienced executive team and products that produce demonstrable ROI for its customers which, together, have helped Gradient deliver strong financial results,” said Matt Alfieri, Partner at Centana Growth Partners. “The company is at an important inflection point, and we are excited to partner with Stan and his stellar team. In addition to growth capital, we look forward to providing our knowledge and industry connectivity to assist Gradient in product expansion and go-to-market activities that continue to benefit customers and extend their competitive advantage.”

According to McKinsey & Company , AI could deliver up to $1.1 trillion of annual value in the insurance industry. Gradient AI is uniquely positioned to be at the forefront of delivering solutions to unlock that value.

Gradient AI’s solutions stand out in the market by providing industry-leading AI and machine learning models which leverage a vast industry data lake, including tens of millions of policies and claims, as well as economic, health, and geographic information. As a result, insurers can better assess risks and increase operational efficiencies to improve profitability.

The new capital will be used to:

Improve and develop new features and functionality for the existing product suite
Launch new products to expand the range of solutions for P&C and Health insurers
Invest in research and development to continue driving AI innovation in the insurance market to further extend the company’s technological leadership

This funding round marks another significant milestone for Gradient AI, which has consistently delivered strong performance and customer satisfaction since its inception. With this new investment, the company is well positioned to achieve its growth targets.

>> Read more about Centana's perspective on why they led Gradient AI's Series C round.

About Gradient AI

About Centana Growth Partners

Centana Growth Partners is a specialized growth equity firm that invests in the future of finance. Centana collaborates with companies across the entire financial services ecosystem, from institutions to fintech disruptors, to add strategic and long-term value to their portfolio companies. Centana empowers entrepreneurs with a combination of capital, network, and expertise to accelerate their growth. The Centana team taps into decades of financial services expertise and insight to invest in companies that bring real value and innovation to the industry. Visit centanagrowth.com for more information.

Media Contact

Elyse Familant

Results PR

elysef@resultspr.net

CEO Viewpoint: Leveraging AI to Combat Social Inflation in P/C Insurance

Fri, 26 Jul 2024 19:48:34 GMT

July 26, 2024

By Stan Smith

Social inflation is an ever-present concern in the property/casualty insurance industry, putting pressure on their reserve levels and overall financial stability. According to research from Swiss Re Institute, the U.S. is in a wave of social inflation, which is likely to continue for a while. The good news is that AI offers promising solutions to address these challenges, providing tools for better risk assessment and claims management.

Various well-known factors contributing to social inflation include high-cost litigation, increasingly resulting in nuclear verdicts (where juries award settlements that are substantially higher than expected based on case facts), as well as changes in societal attitudes—growing public distrust towards large corporations that is exploited by some attorneys.

In addition, we’ve also seen an influx of lawsuits, with so-called “bad actors” increasingly using sophisticated methods, including AI, to target specific geographic areas or types of organizations, such as long-term care facilities. These actors identify regions with a high concentration of potential claimants and engage with specialized lawyers who focus on such high-risk cases.

What this means for insurers is a heightened level of unpredictability and risk in their portfolios. As a result, they face an uphill battle in maintaining profitability and ensuring adequate reserves.

How Can P/C Insurers Address Social Inflation?

This new landscape requires insurers to be sophisticated in their methods, using AI to uncover potential litigation patterns and mitigate the impact of social inflation. By understanding where these claims are likely to arise and how they are developing, insurers can make more informed decisions about claims dispensation—and at the other end (underwriting)—about pricing and risk management.

Here are three ways that insurers can use AI to help mitigate the impact of social inflation.

Predictive Analytics Can Help Mitigate Catastrophic Claims

Claims are not static. They evolve as medical conditions change and if attorneys get involved. With traditional claims management, adjusters need to evaluate cases regularly, reading and understanding new material as it becomes available. AI offers a more accurate and faster alternative. It works 24/7, monitoring incoming claim data in real-time, and promptly flagging cases for managers to prevent escalation. This increases the likelihood of resolving high-risk claims without ever going to court.

Integrating AI-driven predictive analytics into the claims process can result in reduced claim severity and more efficient allocation of reserves. By analyzing extensive datasets, AI can identify trends and predict outcomes with high accuracy, allowing insurers to anticipate potential high-severity claims and allocate resources more effectively.

AI Optimizes Claims Management

AI streamlines claims processing, reducing the time and resources required to handle each claim. It automates routine tasks, improves decision-making speed, and enhances overall efficiency. Faster, more accurate claims processing helps minimize litigation and settlement costs. AI can also assist in triaging claims, prioritizing those that require immediate attention and ensuring timely resolution.

By leveraging this approach, customers are flagged about potential claim escalation, leading to substantial savings in litigation costs and more efficient claims management.

AI Enhances Underwriting

Social inflation is driving insurers to refine their underwriting and pricing processes too. AI enhances underwriting by incorporating a broader range of data points into risk assessments, leading to more accurate pricing models that reflect the true risk profile of policyholders. AI can analyze factors such as historical loss data, economic indicators, and social media trends to provide comprehensive risk assessments.

In addition, it’s important for insurers to understand how different factors, including geographic and facility-specific risks, contribute to rising claim frequencies and severities. Certain regions or types of facilities—such as long-term care centers—may experience higher claim rates due to socio-economic conditions or industry-specific risks. AI can identify these high-risk areas and facilities by analyzing patterns in claims data. This allows insurers to refine their underwriting approaches, set more accurate pricing, and make informed decisions about which risks to underwrite. By adopting this data-driven approach, insurers can better manage their exposure and enhance their overall risk management.

Challenges and Future Directions

Social inflation can be a big headache for the P/C insurance industry, but AI offers some real hope. With advanced tools for predictive analytics, risk assessment, and claims management, insurers can tackle social inflation more effectively and keep their operations running smoothly.

AI isn’t just a cool new tech craze; it’s reshaping the insurance industry. It helps insurers get ahead of the factors driving social inflation, streamline operations, and make smart, data-driven decisions. As the industry evolves, insurers need to stay nimble, continuously tweaking their AI strategies to handle new challenges and regulatory demands. By embracing AI’s potential, insurers can create a stronger, more transparent, and customer-friendly industry. This will help them stay profitable and manage the pressures of social inflation and other emerging issues effectively.

Stan Smith, Gradient AI

Stan Smith is the founder and chief executive officer of Gradient AI

This article first appeared on Carrier Management.

How AI is Revolutionizing Underwriting: Enhancing Accuracy, Efficiency, and Profitability for P&C Insurers

Thu, 25 Jul 2024 15:14:07 GMT

P&C INSIGHTS BLOG | Apr 29, 2024

The Gradient AI Team

Introduction: Embracing the AI Revolution in Underwriting

Artificial intelligence is transforming the insurance landscape, offering significant customer insights, better risk management, and improved underwriting operations for insurers. By leveraging AI, P&C and workers’ comp insurers can enhance claims management, driving growth and improving outcomes.

This blog post delves into how AI is revolutionizing underwriting, highlighting the key points from our white paper "Artificial Intelligence Brings Effectiveness and Efficiency to the Underwriting Process” .

Download the white paper to learn more about the benefits and applications of AI in underwriting.

The Challenge: Traditional Underwriting Limitations

Underwriting is a complex process involving numerous factors that influence the risk and price of a policy. These factors include demographics, loss history, weather patterns, local crime statistics, and more. Insurers often lack the market depth and necessary data to accurately assess the risk involved in underwriting a new policy. Traditional methods can fall short due to:

Limited data on new markets or lines of business
Historical losses from miscalculating risks
The cognitive limits of human underwriters

AI's Solution: Leveraging Limitless Data

AI and machine learning algorithms can analyze virtually limitless amounts of data, providing insights into new markets, past policies, and unforeseen risks – enhancing the role of the underwriter, rather than replacing it . This capability enables AI to:

Improve risk assessment with comprehensive data analysis
Facilitate no-touch or low-touch processing for low-risk policies
Allow underwriters to focus on more strategic, complex decision-making

By utilizing AI, insurers can make better-informed underwriting decisions , improving both efficiency and effectiveness. This transformation leads to reduced effort and cost, more processed policies, and ultimately, increased profitability.

Impact on Profitability: Enhancing Key Metrics

Improving key insurance operating metrics significantly impacts performance, profitability, and valuation. Since insurance operates on narrow profit margins, even minor improvements in loss ratios, claims costs, or operating costs can have substantial effects. For example, reducing the loss ratio by just 2-3 points can increase profit margins by 20-30%. AI models enhance key operating levers, allowing insurers to:

Quote business more accurately with less effort
Reduce quote turnaround time
Enable straight-through processing

AI as a Risk Radar: Gaining Better Insights

AI acts as a "risk radar," providing insurers with deeper insights into both good and bad risks. This improved visibility helps insurers capture good risks overlooked by competitors and avoid undesirable ones. For example, a workers’ compensation insurer can use AI to reveal hidden risks in a construction company or uncover lower-than-expected risks in another company. These insights allow insurers to underwrite with greater confidence and precision.

Four Ways AI Increases Accuracy and Efficiency in P&C Underwriting

Better Identification of High-Risk Policies: AI models can assess millions of policies, identifying attributes that increase risk. This enhances human underwriters' ability, reducing losses from high-risk policies.
Recognizing Good Risks: AI helps insurers identify and price lower-risk policies accurately, even in new markets. This ability supports geographical or business line expansion with informed risk assessments.
Reducing Quote Turnaround Time: AI can quickly score policies, enabling no-touch or low-touch processing for low-risk policies. This efficiency improves the agent and customer experience and captures more business.
Focusing on High-Value Work: By flagging appropriate policies for straight-through processing, AI frees underwriters up to concentrate on complex policies, maintaining standards while increasing throughput.

Conclusion: Reaping the Benefits of AI in P&C Underwriting

In summary, AI and big data are revolutionizing underwriting, enabling insurers to grow more profitably. By leveraging industry data lakes and machine learning, underwriters can make better decisions, operate efficiently, and improve pricing accuracy. Insurers integrating AI will outperform those relying on traditional methods, minimizing risk while maximizing rewards. The benefits extend to insurers, policyholders, and investors, highlighting the transformative impact of AI in underwriting.

>> To dive deeper into the benefits and applications of AI in underwriting, be sure to read the full white paper . As a bonus , included in this white paper is a case study showing specific results from a monoline workers’ comp MGA which improved underwriting profitability after implementing Gradient AI’s solutions for work comp .

Explore the benefits of Gradient AI's P&C or Workers' Comp Solutions.

Schedule a Demo →

Embracing Work-Life Balance: Gradient AI Introduces Recharge Days for Dedicated Staff

Wed, 17 Jul 2024 11:36:35 GMT

P&C INSIGHTS BLOG | Apr 29, 2024

The Gradient AI Team

In today’s fast-paced work world, the pursuit of excellence often demands significant effort and dedication. Gradient AI's senior management recognizes that the drive to achieve goals can sometimes lead to stress, and that balancing professional responsibilities with personal well-being is a challenge that many team members may face. Understanding this, senior management recently announced a benefit aimed at promoting work-life balance: Recharge Days.

Recharge Days are dedicated days off for all staff, designed to provide a break from work. These days are an opportunity for Gradient AI's team to step back, unwind, and recharge their mental and physical batteries - with the goal of fostering a healthier work environment where creativity and productivity can thrive.

The Importance of "Recharging" in Today’s Work Environment

Incorporating Recharge Days into Gradient's company culture is not just a token gesture; it reflects our commitment to the well-being of our employees and living up to our value of “doing the right thing.” There are several compelling reasons why we believe this initiative is crucial:

Recognizing hard work. Our employees are the backbone of our success. Their dedication, innovative spirit, and relentless pursuit of excellence are what drive Gradient AI forward. Recharge Days are a way of acknowledging their hard work and showing appreciation for their contributions.
Promoting work-life balance. Achieving a healthy work-life balance is essential for sustained performance and personal well-being. Recharge Days recognize the importance of family time, personal interests, and mental health.
Boosting productivity and creativity. Studies have shown that well-rested employees are more productive and creative. Taking time off to relax and recharge can lead to enhanced focus, innovative thinking, and improved problem-solving skills. Recharge Days are an investment in our employees’ ability to perform at their best.
Attracting top talent. In a competitive job market, offering unique benefits like Recharge Days can make Gradient AI an attractive place to work. Talented professionals are increasingly looking for employers who prioritize work-life balance and employee well-being.
Building a resilient workforce. A well-rested workforce is more resilient and better equipped to handle challenges. Recharge Days can help build a team that is not only capable but also adaptable and forward-thinking.

Making the Most of Recharge Day

Some of our team made the most of their recent Recharge Days by relaxing by the water, while others were able to use the extra time to visit family. Some had summer cookouts, some hiked in the mountains, and some used the time to enjoy a quiet day at home.

Conclusion

At Gradient AI , employees are our greatest asset. Their hard work, dedication, and creativity are the driving force behind our success. Recharge Days are a testament to our appreciation and recognition of their efforts. By offering these days off, we aim to create an environment where work-life balance is not just a concept but a reality.

>> Connect with our Careers page .

BTIS Adopts Gradient AI for Workers’ Comp Risk Assessment

Mon, 08 Jul 2024 16:01:56 GMT

July 8, 2024

Boston, MA – Gradient AI recently announced that Builders & Tradesmen’s Insurance Services, Inc. (BTIS), has adopted its underwriting solution to better predict risk and improve the pricing accuracy of workers’ compensation policies.

BTIS, a Managing General Underwriter (MGU), is a nationwide insurance intermediary focused on serving small businesses with a wide range of commercial insurance lines. The company is dedicated to building strong partner relationships through outstanding service and innovative insurance solutions. BTIS has experienced significant growth over the past 25 years. To sustain and build on this momentum, the company wanted to enhance its underwriting process to write more profitable policies while efficiently scaling its workers’ compensation business.

BTIS selected Gradient AI’s underwriting solution to evaluate risks with greater precision and speed, enabling the company to capture more business and reduce loss ratios. Gradient AI’s risk scoring and underwriting model helps BTIS assess risk more quickly and guide pricing decisions. This allows BTIS to create highly customized and cost-effective workers’ comp policies for its carrier customers, and deliver quotes faster than before.

“After seeing the success of Gradient AI with one of our carrier partners, we decided to integrate it with another carrier and into our proprietary workers’ compensation underwriting platform,” said Paul Hohlbein, president of BTIS. “We evaluated several options and chose Gradient AI because of its leadership in the field. Leveraging AI is key to enhancing the underwriting process for our customers and carrier partners. We are committed to delivering a world-class experience through our workers’ compensation marketplace, and Gradient AI is a crucial part of our strategy.”

Stan Smith, CEO of Gradient AI, added, “We are excited to partner with BTIS in its efforts to improve underwriting accuracy and efficiency. Our AI-driven underwriting solutions are designed to help MGAs like BTIS and other insurers achieve better outcomes by leveraging large, industry specific datasets and advanced machine learning models. We look forward to supporting BTIS as they continue to expand their work comp book of business aggressively and profitably.”

In the future, BTIS plans to leverage Gradient AI’s underwriting solution with additional carriers as its business further expands.

This article first appeared on WorkCompWire.

BTIS Adopts Gradient AI to Improve Workers’ Comp Risk Assessment

Mon, 08 Jul 2024 16:01:47 GMT

July 8, 2024

AI-driven underwriting solution enables BTIS to assess risk more accurately and accelerate quote turnaround time.

By Anthony R. O’Donnell

Builders & Tradesmen’s Insurance Services, Inc. (BTIS; Rocklin, Calif.), has adopted Gradient AI ’s (Boston) underwriting solution to better predict risk and improve the pricing accuracy of workers’ compensation policies.

BTIS, a Managing General Underwriter (MGU), is a nationwide insurance intermediary focused on serving small businesses with a wide range of commercial insurance lines. The company vaunts its dedication to building strong partner relationships through outstanding service and innovative insurance solutions. BTIS reports significant growth over the past 25 years. To sustain and build on this momentum, the company says it wanted to enhance its underwriting process to write more profitable policies while efficiently scaling its workers’ compensation business.

BTIS selected Gradient AI’s underwriting solution to evaluate risks with greater precision and speed, enabling the company to capture more business and reduce loss ratios, according to a Gradient AI statement. The vendor says its risk scoring and underwriting model helps BTIS assess risk more quickly and guide pricing decisions. This allows BTIS to create highly customized and cost-effective workers’ comp policies for its carrier customers and deliver quotes faster than before.

“After seeing the success of Gradient AI with one of our carrier partners, we decided to integrate it with another carrier and into our proprietary workers’ compensation underwriting platform,” comments Paul Hohlbein, President, BTIS. “We evaluated several options and chose Gradient AI because of its leadership in the field. Leveraging AI is key to enhancing the underwriting process for our customers and carrier partners. We are committed to delivering a world-class experience through our workers’ compensation marketplace, and Gradient AI is a crucial part of our strategy.”

Aggressive, Profitable Expansion

Stan Smith, CEO of Gradient AI, adds, “We are excited to partner with BTIS in its efforts to improve underwriting accuracy and efficiency. Our AI-driven underwriting solutions are designed to help MGAs like BTIS and other insurers achieve better outcomes by leveraging large, industry specific datasets and advanced machine learning models. We look forward to supporting BTIS as they continue to expand their work comp book of business aggressively and profitably.”

In the future, BTIS plans to leverage Gradient AI’s underwriting solution with additional carriers as its business further expands, according to the Gradient AI statement.

This article first appeared on Insurance Innovation Reporter .

5 Questions with Jim Bowers, Gradient AI Senior Solutions Consultant

Wed, 03 Jul 2024 16:59:30 GMT

P&C INSIGHTS BLOG | Feb 26, 2024

The Gradient AI Team

Jim is the Senior Solutions Consultant for the Property & Casualty business at Gradient AI. In his role, Jim collaborates with the Sales, Product, and Implementation teams to advocate for and help customers successfully implement meaningful, actionable, and impactful solutions to support profitable growth, reduce claims and underwriting costs, and improve operational efficiencies. Jim’s goal is to provide a positive experience for Gradient AI clients in the Workers’ Comp industry and their policyholders.

Jim is a leader in the Workers’ Compensation insurance industry with more than 25 years of experience. He spent the last 4 years in Underwriting leadership roles, developing and implementing innovative technology and process solutions for carriers. He has a Bachelor’s degree in Business from Grove City College, and an MBA from Bowling Green University. He received the MPower award with his team for an innovative medical bill review straight-through- processing program. Jim lives in Wadsworth, Ohio.

What do you see as the value of artificial intelligence in insurance?
AI is a strategic imperative for Group Health and P&C underwriting and claims organizations . AI is a game changer! It helps underwriting teams achieve profitable growth and a competitive advantage by helping to optimize pricing, reduce quote turnaround time, and support low touch, no touch experiences, and automation.

For claims organizations, AI helps Workers’ Comp teams achieve the best possible medical and return to work outcomes for employers and their injured workers, by getting the right resources deployed at the right time. AI also assists General Liability and Commercial Auto teams to enhance their efficiency and impact.
More specifically, how can AI best help improve the claims process and address challenges in the Worker’s Comp industry?
AI has a big role to play in providing institutional knowledge and guidance through the experience of tens of millions of claims.

The power of AI for a Workers’ Comp claims organization is being able to see the end of a claim at the beginning and being able to deploy the right expertise, tools, and programs early in the process to reduce claim duration, claim expenses, and indemnity spend, while enhancing the experience for the injured worker and employer. Claims professionals now have a ‘digital assistant’ that will allow them to focus their experience and expertise where it can have the biggest impact.
What drew you to work at Gradient AI?
This is an exciting time for the insurance industry. Innovation is happening at the fastest pace ever. Gradient AI is at the forefront of this seismic change - helping the industry with unmatched AI and Machine learning (ML) solutions and expertise.

I was drawn to Gradient AI so I could leverage my passion for Workers’ Comp and process improvement. I look forward to helping insurers with their AI journey by using my experience leading a Workers’ Comp claims organization and working with underwriting teams to transform underwriting operations.
You started working with us recently. What aspect of your role at Gradient AI are you most looking forward to?
I am energized to help our clients with the “How?” I consider myself an advocate for our clients and look forward to helping them actualize their vision and achieve their goals of improved efficiency, automation, and better financial outcomes. I’m excited to use my extensive experience to support the partnership and collaboration that our clients feel when they work with Gradient AI.
What do you like to do in your free time?
I am a ‘learner’ and love to read – literally just about anything, including fiction, non-fiction, and almost anything else. Send me recommendations! In addition, family is everything to me. So, you’ll often find me hiking, biking, playing tennis, playing board games, binge-watching shows and movies, or travelling with my wife, three daughters, and extended family.

Bonus Question: Where is a favorite place you travelled recently?
We love to travel to places off the beaten path. A recent example is Verona, Italy. We enjoy Rome and the Amalfi coast, but really appreciated Verona. We had a chance to see an Italian opera with 20,000 others in the 2000-year-old Verona Arena one night and to see 20,000 attend a KISS concert the next night. If you go there, try the bigoli and risotto with a local red wine!

Thank you, Jim! Be sure to check back on our blog as we ask 5 questions to different members of our talented staff across Gradient AI, to learn how they support the success of our clients.

Explore the benefits of Gradient AI's P&C or Workers' Comp Solutions.

Schedule a Demo →

Get Ready for AI: 6 Change Management Steps to Integrate into P&C Insurance

Wed, 03 Jul 2024 12:55:15 GMT

P&C INSIGHTS BLOG | Apr 29, 2024

Jim Bowers

AI offers P&C insurers enhanced underwriting and claims processing. However, as discussed in our previous blog , successful AI implementations often require a strategic change management process to manage the unique challenges involved.

Implementing AI in the P&C insurance industry is like being a tour guide. You wouldn’t start leading a group without first planning the route, checking the weather forecast, and knowing the hours of operation for points of interest. Similarly, thoughtful change management activities will help your organization successfully prepare for the journey of AI implementation.

Six Key Change Management Steps for P&C Claims and Underwriting Organizations to Ensure Smooth AI Adoption

Consider these 6 steps as your playbook for success. Your plan details may vary, but having an intentional and structured approach in place removes the worry and supports successful adoption.

Step 1: Pre-Planning Activities

Pre-planning should start 30-90 days before the change initiative.

Complete Change Management Overview
Understand the fundamentals of change management and familiarize yourself with tools like the ADKAR model , based on the principle that organizational change can only happen when individuals change.

Review Best Practices for Leading Change
Consider the importance of your managers and leaders in creating your plan. Prosci Research shows that most employees prefer to receive personal messages about changes directly from their managers. Read this Prosci CLARC article for more information.

Get to Know the Change Management Resources Available
Leverage your internal change management tools or lean on Gradient AI to provide you with important tools to simplify the change process.

Create a Change Management Community of Practice
Implement a Community of Practice to share experiences, issues, and success stories. This is a great way to learn from peers and other change management stakeholders. Consider this to be like your favorite travel blog or online travel community.

Step 2: Change Preparation

Change preparation is the first step after preplanning and will run concurrently with other steps as your plan ramps up.

Identify Stakeholder Group
Identify stakeholders and determine their required level of engagement.

Evaluate Stakeholder Change Readiness
Complete a stakeholder mapping exercise to understand the level of impact of the change on each stakeholder team and to determine their readiness. This is also a perfect time to complete a baseline change readiness survey.

Facilitate Discovery Sessions
Gather insights from key stakeholders to gain a wider perspective, as an early way to communicate the upcoming change, and to determine the level of communication and involvement expected and needed.

Coordinate with the Technical Project Manager for Alignment
This step helps align technical and change management implementation activities and timelines.

Create an Initial high-level Change Plan
This will help keep things on track and assist in other steps. In addition to the Change Plan, this is a perfect time to complete a RACI (Responsible, Accountable, Consulted, Informed) assessment to align all change team members, internal and external, on roles and responsibilities.

Step 3: Change Impacts

Complete a Change Impact Assessment
Work with the core team that will be impacted as well as upstream and downstream teams to identify key process changes. This is like calling out detours and highlights on your tour route.

Develop and Incorporate Mitigation Strategies into the Change Plan
Understanding the new business process changes and risks will allow you to develop mitigation strategies to support success and minimize risk.

Consider Impacts to People, Process, and Technology
This includes changes to how, when, and where work gets done; customer impacts; skill gaps and related training needs, and more.

Step 4: Communication

Ultimately you want to build awareness, understanding, and successful adoption. A comprehensive communication plan will deliver the right amount of communication to the right stakeholders at the right time. This step starts early in the change planning cycle and continues throughout the project.

Develop an Initial Communication Plan
This plan will involve multiple channels of delivery and will be customized to each stakeholder based on the prior work you have done. Be consistent and timely with your communications.

Tailor Communications to the Right Stakeholders
While the messaging and content may be similar, the level of detail, the timing, and the format of the communication will likely look different for different stakeholder groups. Tailor communications to optimize their impact.

Utilize Leaders, Managers, and Change Agents
Meet people where they are. Leverage current team meetings and other current communication forums to spread the word and keep everyone updated. Remember that most employees would rather hear change news and updates from their direct manager.

Step 5: Training

Training may look different for each organization and specific implementations, but there are some best practices to follow. As the project progresses and the above steps have been completed, decisions on training will come naturally. Consider training your Change Agents first.

Conduct a Training Needs Assessment
Determine who needs training, what do they need to learn, and the best delivery methods. Tailor training to specific roles. Define what successful training looks like. Are there other learning events and job changes to consider?

Develop and Deliver Training
Consider just-in-time training to promote retention and quick practical use of what they have learned. The plan should be detailed and should build staff confidence while addressing any knowledge gaps.

Step 6: Organizational Readiness

Organizational readiness begins with your first survey and stakeholder interviews and continues throughout the change cycle.

Readiness Assessments
Conduct the initial assessment as a baseline and follow-up readiness assessments to gauge preparedness. Use informal feedback, conversations, and surveys to identify gaps. Those gaps could be awareness, skills, sponsorship, organizational culture, and more.

Pulse Surveys
Regularly distribute pulse surveys to track progress and address any emerging issues. Balance the frequency to avoid survey fatigue.

Post AI Implementation: Sustaining the Change

Post-implementation, it's important to keep your process changes moving forward smoothly to ensure long-term success. This involves continuous measurement, after-action reviews, and a solid sustainment plan.

Key post-implementation activities include:

Measuring Success. Use predefined metrics to evaluate the effectiveness of the change and identify areas for improvement. Think of it as asking tour participants to fill out feedback surveys at the end of the tour to improve future experiences.

After-Action Reviews. Reflect on what worked and what didn’t to learn and improve future initiatives, like reviewing the tour with your team afterward.

Sustainment Plan. Ensure business-as-usual resources are in place to support ongoing training needs and address any post-deployment issues, much like ensuring your tour guides are always prepared for the next group.

Conclusion

Implementing AI in P&C insurance is a complex but rewarding endeavor. By following a structured change management process, claims and underwriting organizations can overcome resistance, build stakeholder engagement, and achieve successful AI adoption. Partner with Gradient AI for help with your change management goals as you transition to AI and automation solutions. We want to help you see your AI projects through to success!

Explore the benefits of Gradient AI's P&C or Workers' Comp Solutions.

Schedule a Demo →

Engage PEO Expands into New Risks and Territories with Gradient AI

Sat, 29 Jun 2024 13:04:48 GMT

June 29, 2024

Through a new tool based on the vendor’s Underwriting Solution, Engage PEO has been able to increase its risk appetite and expand into to new markets.

By Anthony R. O’Donnell

Business has been booming for Engage PEO (Hollywood, Fla.), but to ensure that profitability marched along with growth, the professional employer organization adopted Gradient AI’s Underwriting Solution to enhance the accuracy and efficiency of its underwriting processes. The company built a tool based on the Gradient AI solution that lets its underwriters leverage real-time loss data to evaluate the projected loss of a prospective client. The AI outcome becomes the basis for the premium charged to clients for their WC insurance. With the new tool, Gradient AI-based tool, Engage PEO has achieved enhanced risk assessment capabilities, letting it increase its risk appetite and expand into to new markets in order to service a broader range of clients.

Engage PEO offers HR outsourcing solutions to small and mid-sized businesses across the United States, including ancillary products, such as workers compensation insurance. For its insurance program, Engage PEO enters into a co-employment situation with its clients and operates similarly to an MGA or MGU and works with insurance carriers. The company has grown rapidly in recent years, including the completion of five mergers and acquisitions, the addition of three operational centers, and a significant increase of clients and worksite employees.

In late 2019, Engage PEO began looking for solutions that would enable the company to take on a wider range of risks. Engage PEO had been struggling to underwrite in certain jurisdictions that were challenging for workers’ compensation and was taking on higher-exposure risk.

“We would have liked to place something like a heavier manufacturing exposure in the book, but we did not feel like I had the experience on my underwriting team, and nor did we have the appetite for that kind of risk,” recalls Julie Cirillo, Chief Risk Officer, Engage PEO. “We were taking very low-rated risks, for example, restaurant and hotels, clerical offices, doctors. We did a lot of nursing homes, which is a tough risk, but we know it really well.”

Cirillo says Engage PEO was familiar with Gradient AI and its WC claims products which had been successfully utilized by many TPA’s and Carriers in the WC industry. She says the integration of the Gradient AI’s claims tool was critical in Engage PEO selection of TPA partners as the firm moved to a high deductible WC program.

Superior Product

While evaluating tools and partners necessary to move to a high deductible WC program, it became apparent that Engage PEO needed a tool to assist in underwriting new business that would be placed into that program. “We evaluated multiple actuarial based options and initially started with an actuarial based model, but this model required a lot of knowledge and discretion by the underwriters leading to inconsistent results,” Cirillo recalls. “The concept of an AI model was new to us, and we evaluated two different options that were in the marketplace. It became obvious very quickly in the review that Gradient AI was the superior product.”

The main reason for selecting Gradient AI was that the vendor already leveraging an industry leading data set, according to Cirillo. “We saw first-hand the success of its claims tool,” she notes. “Second, they were willing to work alongside us and our consultants to build the nuanced product we wanted, which was different than the existing product they had for underwriting. They became our partner.”

It took Engage PEO approximately nine months to build and validate its model. After extensive testing the firm launched the model in January 2021. “We leveraged our internal data management team, our broker’s data management team, our external actuarial consultants, and our underwriting team manager in the planning, building and testing of the model,” Cirillo says.

Increasing Profitability, More Jurisdictions and Riskier Exposures

Cirillo’s team delivered the model four months behind schedule, but within budget and without serious challenges. “Most of the issues were resolved during the testing phases and before the rollout to the team,” she says.

Through the adoption of the Gradient AI Underwriting Solution to build the new tool, Engage PEO has achieved everything it set out to accomplish—and more, according to Cirillo. “The profitability of our program continues to increase year over year,” she reports. “We are now writing in more jurisdictions and riskier exposures than we ever have in the program.”

Engage PEO is also now using the model to improve selection in its M&A processes and ensure seamless integration when we acquire other PEO’s.

Making Underwriters’ Jobs Easier

“We improved the efficiency and consistency of our risk evaluation process—reducing evaluation times from hours to minutes—and this has brought great satisfaction to the underwriting team. We have made their jobs easier!”

Through the success of the AI underwriting model, Engage PEO is now focusing on engaging AI in its broader business model to continue to improve its overall operation, its growth engine, and client satisfaction, Cirillo reports. Gradient AI has also had a significant impact on the company’s sales and renewal season.

“This year, we relied on Gradient AI during our busiest season from October through January, and the results were remarkable,” Cirillo says. “Since the sales and renewals were driven by AI powered data insights, the experience was seamless for both our team and clients.”

This article first appeared on Insurance Innovation Reporter .

BTIS Adopts Gradient AI to Improve Risk Assessment in Workers' Compensation Policies

Thu, 27 Jun 2024 12:52:48 GMT

AI-Driven Underwriting Solution Enables BTIS to Assess Risk More Accurately and Accelerate Quote Turnaround Time

Boston, MA June 27, 2024 - Gradient AI , a leading enterprise software provider of artificial intelligence (AI) solutions in the insurance industry, today announced Builders & Tradesmen's Insurance Services, Inc. (BTIS), has adopted its underwriting solution to better predict risk and improve the pricing accuracy of workers' compensation policies.

“After seeing the success of Gradient AI with one of our carrier partners, we decided to integrate it with another carrier and into our proprietary workers' compensation underwriting platform,” said Paul Hohlbein, president of BTIS. “We evaluated several options and chose Gradient AI because of its leadership in the field. Leveraging AI is key to enhancing the underwriting process for our customers and carrier partners. We are committed to delivering a world-class experience through our workers' compensation marketplace, and Gradient AI is a crucial part of our strategy."

Stan Smith, CEO of Gradient AI, added, "We are excited to partner with BTIS in its efforts to improve underwriting accuracy and efficiency. Our AI-driven underwriting solutions are designed to help MGAs like BTIS and other insurers achieve better outcomes by leveraging large, industry specific datasets and advanced machine learning models. We look forward to supporting BTIS as they continue to expand their work comp book of business aggressively and profitably."

In the future, BTIS plans to leverage Gradient AI’s underwriting solution with additional carriers as its business further expands.

About Gradient AI

Gradient AI is a leading provider of proven artificial intelligence (AI) solutions for the insurance industry. Its solutions improve loss ratios and profitability by predicting underwriting and claim risks with greater accuracy, as well as reducing quote turnaround times and claim expenses through intelligent automation. Unlike other solutions that use a limited claims and underwriting dataset, Gradient AI’s software-as-a-service (SaaS) platform leverages a vast industry data lake comprising tens of millions of policies and claims. It also incorporates numerous other features including economic, health, geographic, and demographic information. Customers include some of the most recognized insurance carriers, MGAs, MGUs, TPAs, risk pools, PEOs, and large self-insured employers across all major lines of insurance. By using Gradient AI's solutions, insurers of all types achieve a better return on risk. To learn more about Gradient AI, please visit: https://www.gradientai.com or follow on LinkedIn or X .

Media Contact

Elyse Familant

Results PR

elysef@resultspr.net

Change Management Best Practices for P/C Insurers

Mon, 24 Jun 2024 12:56:01 GMT

June 24, 2024

By Jim Bowers

In the property/casualty insurance industry, AI’s ability to analyze vast datasets to evaluate risk is a game-changer. It enables insurers to price policies more accurately, reduce quote turnaround times, triage claims, and reduce the duration and cost of claims. However, the transition from traditional methods to digital processes isn’t always smooth. In this article, we’ll explore the challenges of implementing AI solutions and propose change management best practices, with a focus on how to overcome resistance to change with a change agent.

Unveiling AI Adoption Challenges

When P/C insurers embark on AI initiatives, they often encounter resistance to AI adoption, citing concerns like, “We’ve always done it this way,” “It will never work,” or “What a waste of money. Just another project that will fail.”

These are a few of the things we’ve heard customers say when they first start to work with AI and machine learning models. While many insurance companies are looking to embrace improved efficiency and profitable growth, they face significant challenges that lead to resistance to adopting AI and automation solutions. Other industries seem to have embraced digital transformation more easily, however, the insurance industry still lags due to various factors. Here are a few reasons why:

Business models : Many insurers delay their digital adoption due to existing business models that require major restructuring to align new goals and flexibility.

Scattered data : Insurers often struggle with assimilating and analyzing vast amounts of unstructured and scattered data. This data management problem can be problematic when attempting automate.

Fear and uncertainty : Often employees and leaders do not understand the business imperative for change and fail to see the personal benefits, leading to fear and resistance. They may worry that AI will either micro-manage them or take their jobs away, further fueling their reluctance to embrace new technologies.

Navigating Change With Artful Strategy

Change management is an art — not a science. How we introduce changes can make or break our efforts. We can’t just send an email to announce the change and hope for the best. We need to be mindful of how people react to change and be intentional with our change management plan.

Before you begin the process, consider completing a change readiness assessment. It’s also important to understand the impacts of the change upstream and downstream as well as identify the key stakeholders and their roles.

A recent Prosci study found that 88 percent of companies with excellent change management programs exceeded expectations, compared to only 13 percent of those with poor change management programs.

The process of driving change through AI and automation in insurance processes requires transparent communication and support. This is why a change management program accompanied by a change liaison or change agent is necessary to achieve desired outcomes.

Empowering Change Agents

Change agents are individuals that promote and support a new way of doing something within an organization. They are critical to driving successful AI and automation initiatives. Change agents take the initial steps for the change process and oversee it to its completion. Here are some of best practices and key responsibilities of change agents.

Integrate change plan activities into the project plan by attending workstream planning sessions, identifying necessary activities and helping everyone stay on schedule.

Identify change impacts on people, processes and technology. For example, they will analyze current and future states and address concerns such as AI training needs and resource requirements.

Plan and support engagement and communication by sending relevant communications to insurance stakeholders at the appropriate times. They will actively demonstrate support for the change outlined in the communication plan.

Identify risks during workstream planning and create mitigation plans to reduce resistance to change. They will address risks such as potential employee turnover and develop plans to mitigate them.

Lead by example , embodying the change they wish to see. They will create a unified message of change, remove obstacles and display an unbiased attitude while welcoming feedback, emphasizing leadership and innovation in the insurance industry.

Modernizing your P/C insurance business is not just about adopting new technologies; it’s about embracing change and innovating for the future. By overcoming resistance through effective change management strategies, including communication, engagement and clear expectations, insurance companies can unlock the full potential of AI and automation.

A version of this article was originally published as a blog item on the Gradient AI website Change Management: AI Adoption in P/C and Work Comp Insurance (gradientai.com)

Jim Bowers, Gradient AI

Jim Bowers is the P/C Senior Solutions Consultant at Gradient AI and brings over 25 years of leadership in the workers compensation and P/C insurance industry. He collaborates across teams to deliver innovative solutions that drive profitable growth and enhance customer experiences.

This article first appeared on Carrier Management.

EY's Survey of Insurance Execs Reveals How They're Prioritizing Generative AI: A Look Behind the Numbers

Wed, 12 Jun 2024 19:14:07 GMT

P&C INSIGHTS BLOG | Apr 29, 2024

The Gradient AI Team

A recent EY study involving 200 senior insurance decision-makers highlights priorities and plans for generative AI in P&C and group health insurance. The study reveals global industry leaders' perspectives on the opportunities and challenges associated with GenAI and how they plan to integrate it in their organizations.

EY authors Isabelle Santenac, Gaston Messineo, and Peter Manchester wrote about the study’s results in an extensive article which you can read here .

We’ve summarized their key findings below.

The authors start off by explaining the difference between “AI” and “Generative AI” this way:

“AI is a broad term for technologies that simulate human intelligence, while GenAI is a subset of AI capable of generating new and original content such as text, images, and video. GenAI in insurance tends to be focused on enhanced underwriting, predictive risk assessment, and personalized product recommendations.”

Key Insights from the EY Study of GenAI in Insurance

1. Insurers view GenAI as an enhancement in an increasingly challenging market:

Survey respondents said they are facing proliferating risks such as climate change, natural catastrophes, cybersecurity threats, rising customer expectations for personalized experiences, and profitability pressures. They see GenAI as a transformative tool to:

Unleash competitive disruption
Create new revenue opportunities
Promote operational excellence

2. The majority of insurers are rapidly adopting GenAI:

42% of insurers said they are already investing in, and 57% said they are planning to invest in GenAI in the next few years. In terms of the use cases they are prioritizing, respondents said this:

69% of all insurers are focusing on transforming specific parts of the value chain, like underwriting, for quick wins
54% of P&C insurers are prioritizing predictive risk assessments
62% of group health providers are focusing on decision support tools

3. GenAI is adding tangible value for insurers: survey respondents are expecting to reap positive benefits by implementing GenAI. They cited these specific benefits:

82% of large insurers cite productivity gains as a primary driver in adopting GenAI
65% of all insurers expect a revenue uplift of more than 10%
52% of respondents anticipate cost savings of 11-20%

4. Insurers consider GenAI adoption a strategic priority:

Some insurers are moving ahead with initial applications, while others are focusing on robust governance models and delivery capabilities before wide-scale deployment. Many respondents said they have dedicated implementation teams with direct links to the C-suite and board. The EY survey results emphasize the value of a two-track approach to implementing generative AI:

1. Encourage bottom-up experimentation by:

Equipping teams with GenAI tools and sandbox environments
Promoting a culture of innovation and quick-win use cases

2. Develop enterprise strategic and technical Infrastructure by:

Crafting a long-term GenAI vision
Mobilizing cross-functional teams and centralize management
Establishing strong data governance practices
Defining measurable business outcomes and KPIs

Conclusion: Generative AI Adoption Trends in Insurance

An overwhelming number of respondents see the benefits of generative AI in their industry and are in the process or have plans to integrate it into their operations. In order to be successful, insurers must balance rapid experimentation with strategic planning to harness generative AI’s full potential.

A two-track approach that promotes immediate action and prepares for long-term success is the favored implementation approach of most of the respondents and will position insurers to unlock significant value from GenAI to maintain that critical competitive edge in the industry.

Learn more about how insurers can leverage both traditional and generative AI to significantly enhance efficiency and effectiveness, improve decision-making, and optimize their workforce utilization in Gradient AI CEO Stan Smith’s recent Dataversity article .
Read the original EY survey and article “How to Revolutionize the Insurance Value Chain with Generative AI” .

Explore the benefits of Gradient AI's P&C or Workers' Comp Solutions.

Schedule a Demo →

Driving Change Management: Strategies for Successful AI Adoption in P/C and Work Comp Insurance Operations

Mon, 10 Jun 2024 14:25:11 GMT

P&C INSIGHTS BLOG | Apr 29, 2024

The Gradient AI Team

In the P&C and workers’ comp insurance industries, AI's ability to analyze vast data sets to evaluate risk is a game-changer. It enables insurers to price policies more accurately, reduce quote turnaround times, triage claims, and reduce the duration and cost of claims. However, the transition from traditional methods to digital processes isn't always smooth. In this blog, we’ll explore the challenges of implementing AI solutions, and propose change management best practices, with a focus on how to overcome resistance to change with a change agent.

Challenges Faced by Insurance Companies Considering AI Adoption

"We've always done it this way."
"It will never work."
"What a waste of money. Just another project that will fail."
"Great, now I'll just have more work to do."

These are a few of the things we’ve heard customers say when they first start to work with AI and machine learning models. While many insurance companies are looking to embrace improved efficiency and growth, they face significant challenges that lead to resistance to adopting AI and automation solutions. Other industries seem to have embraced digital transformation more easily, however, the insurance industry still lags due to various factors. Here are a few reasons why:

Business Models: Many insurers delay their digital adoption due to existing business models that require major restructuring to align new goals and flexibility.
Scattered Data: Insurers often struggle with assimilating and analyzing vast amounts of unstructured and scattered data. This data management problem can be problematic when attempting automate.
Fear and Uncertainty: Often, employees fear that AI will either micromanage them or take their jobs away, leading to resistance to change.

To address these challenges effectively and implement change management efficiently, companies may want to consider partnering with a company like Gradient AI, which specializes in AI solutions for the P&C and work comp insurance industries and has an experienced change management team to facilitate seamless adaptation.

Best Change Management Strategies for Overcoming Resistance

Change Management is an ART - NOT a science. How we introduce changes can make or break our efforts. We can’t just send an email to announce the change and hope for the best. We need to be mindful of how people react to change.

Before you begin the process, consider completing a change readiness assessment to evaluate your organization’s readiness for change. It’s also important to understand the impacts of the change upstream and downstream as well as identify the key stakeholders and their roles.

A recent Prosci study found that 88% of companies with excellent change management programs exceeded expectations, compared to only 13% of those with poor change management programs.

The Role of Change Agents in Insurance: Best Practice for Change Management

Change agents should:

Integrate change plan activities into the project plan by attending workstream planning sessions, identifying necessary activities, and helping everyone stay on schedule.
Identify change impacts on people, processes, and technology. For example, they will analyze current and future states and address concerns such as AI training needs and resource requirements.
Plan and support engagement and communication by sending relevant communications to insurance stakeholders at the appropriate times. They will actively demonstrate support for the change outlined in the communication plan.
Identify risks during workstream planning and create mitigation plans to reduce resistance to change. They will address risks such as potential employee turnover and develop plans to mitigate them.
Lead by example , embodying the change they wish to see. They will create a unified message of change, remove obstacles, and display an unbiased attitude while welcoming feedback, emphasizing leadership and innovation in the insurance industry.

Modernizing your insurance business is not just about adopting new technologies; it's about embracing change and innovating for the future. By overcoming resistance through effective change management strategies, including communication, engagement, and clear expectations, insurance companies can unlock the full potential of AI and automation. Partner with Gradient AI for help with your change management goals as you transition to AI and automation solutions. We want to help you see your AI projects through to success!

Explore the benefits of Gradient AI's P&C or Workers' Comp Solutions.

Schedule a Demo →

Driving Change Management: Strategies for Successful AI Adoption in Group Health Insurance Underwriting

Mon, 10 Jun 2024 14:16:36 GMT

GROUP HEALTH INSIGHTS BLOG | Jun 10, 2024

The Gradient AI Team

In the Group Health insurance industry, AI's ability to analyze vast data sets to evaluate risk is a game-changer. It enables underwriters to price policies more accurately and reduce quote turnaround times. However, the transition from traditional methods to digital processes isn't always smooth. In this blog, we’ll explore the challenges of implementing AI solutions, and propose change management best practices , with a focus on how to overcome resistance to change with a change agent.

Challenges Faced by Insurance Companies Considering AI Adoption

"We've always done it this way."
"It will never work."
"What a waste of money. Just another project that will fail."
"Great, now I'll just have more work to do."

These are a few of the things we’ve heard customers say when they first start to work with AI and machine learning models. While many insurance companies are looking to embrace improved efficiency and growth, they face significant challenges that lead to resistance to adopting AI and automation solutions. Other industries seem to have embraced digital transformation more easily, however, the insurance industry still lags due to various factors. Here are a few reasons why:

Business Models: Many insurers delay their digital adoption due to existing business models that require major restructuring to align new goals and flexibility.
Scattered Data: Insurers often struggle with assimilating and analyzing vast amounts of unstructured and scattered data. This data management problem can be problematic when attempting automate.
Fear and Uncertainty: Often, employees fear that AI will either micromanage them or take their jobs away, leading to resistance to change.

To address these challenges effectively and implement change management efficiently, companies may want to consider partnering with a company like Gradient AI, which specializes in AI solutions for the Group Health insurance industries and has an experienced change management team to facilitate seamless adaptation.

Best Change Management Strategies for Overcoming Resistance

A recent Prosci study found that 88% of companies with excellent change management programs exceeded expectations, compared to only 13% of those with poor change management programs.

The Role of Change Agents in Insurance: Best Practice for Change Management

Change agents should:

Integrate change plan activities into the project plan by attending workstream planning sessions, identifying necessary activities, and helping everyone stay on schedule.
Identify change impacts on people, processes, and technology. For example, they will analyze current and future states and address concerns such as AI training needs and resource requirements.
Plan and support engagement and communication by sending relevant communications to insurance stakeholders at the appropriate times. They will actively demonstrate support for the change outlined in the communication plan.
Identify risks during workstream planning and create mitigation plans to reduce resistance to change. They will address risks such as potential employee turnover and develop plans to mitigate them.
Lead by example , embodying the change they wish to see. They will create a unified message of change, remove obstacles, and display an unbiased attitude while welcoming feedback, emphasizing leadership and innovation in the insurance industry.

Explore the benefits of Gradient AI's Group Health Solutions.

Schedule a Demo →

Medicare Set-Aside and AI: A Powerful Combination

Fri, 24 May 2024 16:21:44 GMT

May 24, 2024

By Kim Wiswell

Efficiently managing Medicare Set-Aside Arrangements (MSAs) is a formidable challenge within workers’ compensation programs. Adjusters struggle with time-consuming manual documentation and complex compliance requirements and the need for accurate forecasting of treatments and cost estimation further compounds the complexity. In this article, we will look at the powerful impact of integrating AI and automation into the MSA process, and how it can effectively address these hurdles and make the MSA process more efficient.

Challenges: Manual Documentation & Complex Regulations

Manual Documentation

Claims assistants or adjusters often spend a significant amount of time on MSA requests, ranging from an average of two to ten hours per referral, depending on factors like claim complexity, age, and the volume of medical records involved. Extended follow-up correspondence for clarification of body parts accepted under the claim or missing records can further extend this time.

Complex Regulatory Landscape

Another significant challenge is the navigation of the complex regulatory landscape of Medicare compliance. To be successful, the MSA application process requires meticulous attention to detail and expertise in interpreting Medicare’s guidelines and regulations. Compliance errors can lead to penalties, delays in MSA approval by CMS, and heightened administrative burdens for all involved stakeholders.

Solution: Harness AI

Automation of Documentation Process

By automating the documentation process, AI significantly reduces the manual effort needed to produce MSA determinations. Advanced algorithms can quickly analyze medical records, treatment histories, and billing information to generate comprehensive MSA documentation accurately. By automating repetitive tasks, AI reduces the likelihood of errors, accelerates processing times, and frees up resources for more strategic activities.

Compliance Management

AI technologies offer sophisticated compliance management capabilities, which allows all parties to navigate Medicare regulations with ease. AI-powered platforms continuously monitor changes in Medicare policies, ensuring MSA documentation remains up-to-date and compliant. Additionally, AI algorithms can identify potential compliance issues proactively, allowing stakeholders to address them promptly and mitigate risks effectively.

Integrating AI technologies into MSA operations can significantly impact MSA workflows. By automating the information and documentation collection and simplifying compliance management, AI streamlines both the MSA referral process, as well as internal operations, improving efficiency, and ensuring adherence to Medicare regulations. As the complexity of modern healthcare continues to evolve, leveraging AI becomes increasingly important for optimizing MSA processes and achieving better outcomes for all involved.

Challenges: MSA Treatment Forecasting and Cost Estimations

Treatment Forecasting Inefficiencies
The current MSA process is laborious. First, a specialized allocator physically goes through the injured worker’s medical records, creating a summary of the injury and the medical treatment. For each treatment, the allocator must determine whether the recommended treatment has been provided to the injured worker. Any unfulfilled recommendations must be included in the MSA. Next, a treatment plan is developed based on physician recommendations for specific treatments such as surgeries, diagnostic testing, hospitalizations, etc., as well as treatment corresponding to CMS’s standardized guidelines. Last, the allocator prices the services and drugs under the treatment plan using state fee schedules and other pricing resources specified by CMS and recommends an MSA amount for the claim.

All of this can take several days to more than a week, which unfortunately leads to delays in claim closure and settlement funds being paid to the injured worker. In addition, the existing approach often leads to unpleasant surprises for claims managers. Unrecognized treatment recommendations often, consequently, increase the overall MSA amount, sometimes making the settlement impossible.

Cost Projection Inaccuracies
It is challenging to accurately estimate future medical costs within the current MSA framework. Most services are priced out based on state workers’ compensation fee schedules or, when unavailable, usual, customary and reasonable (UCR) fees for the jurisdiction. As fee schedules are unique to each state, and fees are updated as often as quarterly, determining the correct pricing can be a complex process. Also, as the claimant’s medical condition and health status change over time, often necessitating new and different treatments, the amount allocated in an MSA may not accurately reflect the future funds needed. Compounding this, CMS requires MSAs associated with settlements to have been done within the past six months. After this point, they are considered stale-dated, leading to additional costs for the claims payer, both for multiple MSA updates and increasing treatment fees over the course of lengthy settlement negotiations and proceedings.

Solution: Harness AI

Improved Identification of Future Medical Treatments

AI solutions can significantly improve MSA operations by addressing inefficiencies in the identification of future medical treatments. By analyzing vast amounts of medical reports, generative AI can detect treatment recommendations within the records, compare them to structured medical payment data for treatment already provided to the injured worker, and flag treatment yet to be provided. These insights allow allocators to readily identify services and drugs for inclusion in the MSA.

For example, by analyzing historical treatment data, AI can determine specific treatment frequencies for evaluations, therapy, and surgical revisions, as well as the injured worker’s current drug regimen. This data helps MSA allocators make better decisions when forecasting treatment and drugs, which leads to improved cost savings and more accurate funding of the MSA.

Enhanced MSA Cost Projections

MSA cost projections can also be more accurate by leveraging AI. Predictive analytics can monitor trends, analyzing information in real time. In cases where settlement hasn’t occurred, it can automatically recommend changes to the MSA to meet the changing medical conditions and related treatment needs. AI also notes important things that can change, such as evolving treatment plans, how medication is used, and how much things cost. It can also identify and flag escalating cost-drivers such as costly surgeries or procedures early on with suggested actions on how to address each cost driver to mitigate costs.

As healthcare evolves, the integration of AI in the MSA process is key to streamlining operations and ensuring the success of workers’ compensation programs. AI offers a promising solution to the challenges of manual documentation, compliance management, treatment forecasting, and cost estimation, ultimately leading to better outcomes for all involved.

About Kimberly Wisell, CMSP-F

With extensive expertise in workers’ compensation, Kimberly Wiswell is proficient in medical bill review, provider networks, data analytics, system design, workflow re-engineering, and operations management. She has held executive roles at Coventry, CLARA Analytics, MEDVAL, Fair Isaac, Health Net, Fremont Compensation Insurance Group, CompPartners, Beech Street, and Medata. Her consultancy clients include the California Workers’ Compensation Institute, the California Division of Workers’ Compensation, Kaiser Permanente, Sutter Health, and Optum. Kimberly served as president of the National Medicare Secondary Payer Network and is a respected speaker on MSP matters, also providing consulting services to the Center for Medicare and Medicaid Services (CMS). Follow Kimberly on LinkedIn

About Gradient AI
Gradient AI is a leading provider of proven artificial intelligence (AI) solutions for the insurance industry. Its solutions improve loss ratios and profitability by predicting underwriting and claim risks with greater accuracy, as well as reducing quote turnaround times and claim expenses through intelligent automation. Unlike other solutions that use a limited claims and underwriting dataset, Gradient AI’s software-as-a-service (SaaS) platform leverages a vast industry data lake comprising tens of millions of policies and claims. It also incorporates numerous other features including economic, health, geographic, and demographic information. Customers include some of the most recognized insurance carriers, MGAs, MGUs, TPAs, risk pools, PEOs, and large self-insured employers across all major lines of insurance. By using Gradient AI’s solutions, insurers of all types achieve a better return on risk. Follow Gradient AI on LinkedIn or X .

By Kimberly Wiswell, Director of Client Services, Embedded AI, Gradient AI

This article first appeared on WorkCompWire.

Revolutionizing Group Health Underwriting with AI: Better Risk Assessment for Carriers

Fri, 24 May 2024 15:53:58 GMT

GROUP HEALTH INSIGHTS BLOG | May 24, 2024

The Gradient AI Team

Are you a carrier looking to enhance your underwriting efficiency and grow your Group Health insurance business more profitably?

In the fiercely competitive realm of group health underwriting, the ability to discern risk with precision is paramount. In such a landscape, where speed, accuracy, and pricing are the linchpins of success, innovation becomes the differentiator. Enter Gradient AI and its cutting-edge SAIL™ Solution for Group Health Underwriting, a game-changer in the health insurance industry.

6 Compelling Reasons why Gradient AI’s Group Health Underwriting Solutions are Reshaping the Carrier Market

1. Access more data, more quickly than ever before:

With Gradient AI’s SAIL™ Solution , access to a vast data lake is at your fingertips. This abundance of data, comprising medical, prescription, and lab data, empowers underwriters with predictive insights previously unattainable.

2. Price business more accurately:

Leveraging machine learning and a comprehensive dataset, Gradient AI enables underwriting managers to combine the value of AI with their teams’ experience and input. Together, they can price policies with unprecedented accuracy. By incorporating predictive insights into pricing models – and utilizing SAIL™ differentiation of AI model with supporting clinical reporting output - underwriters can ensure that premiums align closely with the actual risk.

3. Optimize the underwriting process:

The SAIL™ Solution streamlines the underwriting process, enabling carriers to assess risk efficiently and effectively. By allowing underwriting teams to automate selected aspects of underwriting, carriers can enhance operational efficiency and focus on strategic decision-making.

4. Decrease quote turnaround time:

Speed is of the essence in group health underwriting, and Gradient AI’s SAIL™ Solution delivers. By leveraging advanced analytics and automation, carriers can significantly reduce quote turnaround times, gaining a competitive edge in the market.

5. Enable straight-through processing:

With Gradient AI’s SAIL™ Solution, carriers can embrace straight-through processing with confidence. By automating underwriting decisions based on predictive insights, carriers can expedite the issuance of policies while maintaining rigorous risk management standards.

6. Make informed decisions, faster:

In the dynamic landscape of group health underwriting, informed decision-making is critical. Gradient AI equips carriers with the tools and insights needed to make decisions swiftly and confidently, enabling them to stay ahead of the curve.

By harnessing the power of Gradient AI’s Group Health Underwriting Solutions, carriers can unlock multiple benefits:

Price policies more accurately and aggressively: With predictive insights at their disposal, carriers can fine-tune pricing strategies to capture market opportunities while mitigating risk. Further, access to more data at the time of sale enables more robust analysis by analytical and actuarial teams, allowing pricing models to be more precise.
Capture more business with streamlined underwriting and faster quote turnaround times: By optimizing the underwriting process and reducing quote turnaround times, carriers can attract more clients and enhance their competitive position in the market.
Better manage risk and reduce loss ratios with deeper insights into group risk: Armed with comprehensive insights into group risk, carriers can proactively manage risk and optimize their loss ratios, driving profitability and sustainability.

Conclusion: Unlock New Opportunities for Growth

In conclusion, Gradient AI’s Group Health Underwriting Solutions represent a paradigm shift in the industry, empowering carriers to navigate the complexities of group health underwriting with confidence and agility. By embracing innovation and leveraging advanced analytics, carriers can unlock new opportunities for growth, profitability, and success in an increasingly competitive landscape.

Explore the benefits of Gradient AI's Group Health Solutions.

Schedule a Demo →

How AI Can Improve Treatment & Cost Forecasting Accuracy Within the Medicare Set-Aside Process

Wed, 22 May 2024 13:19:41 GMT

May 22, 2024

By Kim Wiswell

The treatment and drugs included in the cost projection tables within an MSA are drawn from medical records, as well as past medical history. Given the number of medical reports associated with older claims, review of these records can be a lengthy process, often taking several hours or more. In the past, however, this was the only method of ensuring all future treatment recommendations were identified. But now, AI-assisted record review has emerged as a game changing approach.

Challenges in MSA Treatment Forecasting and Cost Estimations

Treatment Forecasting Inefficiencies - The current MSA process is laborious. First, a specialized allocator physically goes through the injured worker’s medical records, creating a summary of the injury and the medical treatment. For each treatment, the allocator must determine whether the recommended treatment has been provided to the injured worker. Any unfulfilled recommendations must be included in the MSA. Next, a treatment plan is developed based on physician recommendations for specific treatments such as surgeries, diagnostic testing, hospitalizations, etc., as well as treatment corresponding to CMS’s standardized guidelines. Last, the allocator prices the services and drugs under the treatment plan using state fee schedules and other pricing resources specified by CMS and recommends an MSA amount for the claim.

Cost Projection Inaccuracies – It is challenging to accurately estimate future medical costs within the current MSA framework. Most services are priced out based on state workers’ compensation fee schedules or, when unavailable, usual, customary and reasonable (UCR) fees for the jurisdiction. As fee schedules are unique to each state, and fees are updated as often as quarterly, determining the correct pricing can be a complex process. Also, as the claimant’s medical condition and health status change over time, often necessitating new and different treatments, the amount allocated in an MSA may not accurately reflect the future funds needed. Compounding this, CMS requires MSAs associated with settlements to have been done within the past six months. After this point, they are considered stale-dated, leading to additional costs for the claims payer, both for multiple MSA updates and increasing treatment fees over the course of lengthy settlement negotiations and proceedings.

AI Solutions Address These Challenges and Enhance MSA Management

As healthcare continues to evolve, AI will become even more critical to streamlining operations to achieve better outcomes and ensuring the long-term success of workers' compensation programs.

By Kimberly Wiswell, Director of Client Services, Embedded AI, Gradient AI

This article first appeared on WorkersCompensation.com.

5 Questions with Gradient AI Senior Recruiter, Kelsey Wagner

Thu, 09 May 2024 13:03:47 GMT

GROUP HEALTH INSIGHTS BLOG | Nov 13, 2023

The Gradient AI Team

Introduction: What Does a Senior Recruiter Do?

Kelsey is a Senior Recruiter at Gradient AI – working to find top notch talent for Gradient AI, as well as supporting current staff. Kelsey says she always knew she wanted a career working with people, and this motivated her to pursue a degree in psychology from Penn State, work in education, and then ultimately find a home in the HR industry.

Kelsey joined Gradient AI in 2022. Prior to that, she spent seven years working for the Pittsburgh-based health care provider and insurer, UPMC. While there, she built the foundation of her recruitment and HR knowledge. She says it’s been exciting to pivot to a very different company and industry and loves being part of the AI tech scene.

Question 1: What is the primary focus of your role at Gradient AI?

Working at a start-up, you’re bound to wear many hats (something I love about this work), but I’d say my primary focus tends to be around talent. On the front end, I’m collaborating with our managers and teammates to find folks who can bring their unique background and perspective to our organization. Internally, I’m working on ways to support our talent and make Gradient AI a top place to work. (Thanks to Kelsey and the rest of our staff, Gradient has been recognized as a Best Place to Work by Built In for 4 years in a row.)

Question 2: What does a typical day at Gradient AI look like for you?

There is so much variety in this space, so every day looks a little different. Most days include managing any open roles I’m working on, which includes collaboration with hiring teams, reviewing applications, phone screening, and connecting with candidates as part of their interviewing experience. Our team also plays a role in onboarding new hires and making sure their experience from day 1 is smooth and informative. To round out a typical day, I’m working on projects that support our other HR initiatives.

Question 3: What do you like most about your job at Gradient AI?

Something I love about my role, and my experience at Gradient AI, is the encouragement to be creative. One of our core values is to “Create Waves, Innovate.” I see this across our organization and am fortunate to have experienced it myself. For example, I get to take on projects that allow me to think outside of the box or take existing systems and processes and improve them so we can set ourselves up for future success.

Question 4: What would you say to candidates considering a job at Gradient?

There are a lot of exciting reasons to join Gradient AI! I could list off so many, but I think one thing I always mention to candidates is the immense amount of support and collaboration they can expect when they join the team. We’re a small but mighty team, and every person here is willing to help, answer questions, and support you in ways to make you successful. I’ve experienced this with people I don’t directly work with, and at every level (our executives included), which can be exceedingly rare.

Question 5: What do you like to do in your free time?

When I’m not at work, you can find me in the outdoors! My husband and I love going on hikes with our dog Bodie, running, exploring New England (I’m new to the area), and when it’s warm I’ll find a body of water to use my stand-up paddleboard. Traveling is also one of my favorite things to do – I’ve been to almost 20 countries and have a long list of destinations on my list to visit. Finally, I wouldn’t call myself a foodie, but I do love going to new restaurants with friends and trying new foods (especially if I’m not the one cooking).

Bonus Question: What’s the most unusual experience you’ve had in your travels?

I traveled to China and southeast Asia for 9 months with a close friend, and between not knowing the languages and being in different cultures, every day was an adventure. We were hiking through Zhangjiajie National Forest Park in China one day when suddenly my friend and I were ambushed by monkeys. We learned quickly that they only wanted our food, but I think it’s safe to say that I’ve never had another hike quite like that!

Thank you, Kelsey! Be sure to check back on our blog as we ask 5 questions to different members of our talented staff across Gradient AI, to learn how they support the success of our clients.

Explore the benefits of Gradient AI's Group Health Solutions.

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From the Floor at NAPEO’s Risk Management Workshop: Industry Panelists Share Insights on AI in Workers’ Comp

Tue, 07 May 2024 21:13:38 GMT

P&C INSIGHTS BLOG | May 06, 2024

The Gradient AI Team

NAPEO's Risk Management Workshop has become one of the largest gatherings of PEO risk management professionals, carriers, brokers, and agents. This year’s programming centered around the ever-changing topic of artificial intelligence and how it impacts the world of PEO risk management.

Sessions included a workshop on the role of AI in insurance underwriting, which was moderated by Gradient AI’s Chase Pettus, Vice President of Sales. Pettus was joined by panelists, Frank Huang, Managing Director and Consulting Actuary, Davies Group North America ; Julie Cirillo, Chief Risk Officer at Engage PEO , and Justin Rowley, Vice President Risk Management, Helpside .

Insights from the Panel: “AI & Risk - The Role of AI in Insurance Underwriting”

The panelists provided a unique view of AI in the Workers’ Comp industry, as well as shared their first-hand experience with the use of AI in their own companies.

The engaging session focused on key discussion points, including:

1. What is driving the use of AI for PEOs and in the Workers’ Comp Market?

2. What challenges are companies trying to solve for with AI?

3. What is the Impact of AI in Workers’ Compensation?

4. What results have you seen since incorporating AI?

5. Why are some companies slow to implement AI as part of their operations?

6. What advice do the panelists have for those considering AI?

Below, we delve deeper into these discussion points.

1. What is driving AI for PEOs and in the Workers’ Comp Market?

There are many reasons for using AI in insurance, including keeping ahead of the competition, improved efficiency, and mitigating a lack of internal data. The panelists agreed that the motivation to consider using AI in underwriting varies from overcoming staff turnover in underwriting and claims, bolstering departments short on staff and experience, to the desire to expand into other territories and markets.

However, while the reasons for using AI may differ, Frank Huang of Davies Group North America, whose experience ranges across P&C, with a particular focus in workers’ compensation, general liability, and employment practices liability, identified one key underlying factor that is common to all: “The overarching theme is profitability. Not only are companies always looking to improve the bottom line, but they want to make sure they are not being adversely selected against. Essentially, incorporating AI helps them avoid being left behind.”

2. What challenges are companies trying to solve with AI?

While Workers’ Comp is still in a soft market, it can be a challenging market for some insurers and risk takers. Decreasing rates and ever-changing claim costs can lead to severe pricing pressure. In addition, it can be difficult to get the clearest picture of risk.

Helpside, a Utah-based PEO providing solutions for payroll, employee benefits, human resources, and work comp solutions, was an early adopter of AI in workers’ comp - incorporating AI about seven years ago. “Initially, we were looking to add more certainty to our underwriting process,” said Helpside’s Justin Rowley during the panel discussion. “We wanted to add another piece of data to the model we already used, especially in states we weren’t familiar with and for companies that didn’t have loss information, such as start-ups. AI provided that additional data.”

Engage PEO is leveraging AI in its underwriting solution to enhance its workers’ comp underwriting process and fuel its business expansion as part of Engage PEO’s broader AI strategy to facilitate operational efficiency and improve results. Julie Cirillo echoed Frank Huang’s sentiments as to why Engage PEO incorporated AI and what challenges they were looking to solve.

“We really wanted to be profitable,” said Cirillo. “And I wanted to find a way, as Engage PEO grew across the country and moved into new territories or industries, to accurately predict the loss for each prospect so we can price accordingly. For example, California can be a challenging state for workers’ compensation, and you had better have a tool that helps you understand the difference between underwriting a prospect in Los Angeles versus one in Sacramento.”

3. What impact has AI had on Workers’ Comp?

From a broad market view, Frank Huang shared that claims operations saw an early impact when simpler claims models came out about twenty years ago, that allowed better claims triage.

As AI Underwriting models evolved, Huang added the important impact on proactive risk management. “As I noted earlier, AI models can help you identify the potential for larger, more lengthy claims and support better claims management, but with the underwriting models available, you are now asking, ‘do we want to allow this business in the door and are we sure have the right price points?”

4. What results have you seen since incorporating AI?

Providing the perspective of a PEO’s experience first-hand, Justin Rowley shared that AI has not only been helpful to Helpside in pricing new clients, but also in their renewal business. “The biggest impact AI has had on us, has been increased certainty in the underwriting process,” said Rowley. “We still use loss runs and an actuarial model when possible. I have built a dashboard that allows me to see the actuarial predicted losses as well as the AI predicted losses and we make decisions from there.”

Rowley also added three key additional benefits of utilizing AI in workers’ comp for Helpside, including:

Greater insights for prospects with gaps in data
Quicker underwriting turnaround times
Increased underwriting efficiency

Julie Cirillo said that Engage needed to create a sustainable underwriting model to support that growth she spoke about earlier in the program. AI has enabled Cirillo to build an underwriting team that can:

Accurately evaluate risk
Onboard and empower less experienced underwriters
Support growth while improving efficiency

“I needed to know that regardless of which underwriter looked at the potential risk of a prospect, we would get consistent results,” said Cirillo.

Not only is AI significantly enhancing Engage PEO’s underwriting process, but it is also being used to support the company’s M&A activities by evaluating segments of risk within a whole book of business. Cirillo added, “This solution has assisted in integrating our acquired companies, allowing for fast and accurate review of both the portfolio and individual clients to ensure profitability is maintained with the transition to our risk-bearing program.”

5. Why are some companies slow to implement AI as part of their operations?

“I think possibly fear or lack of knowledge,” said Frank Huang, who observed the slow adoption practices are not limited to the PEO space - but to the broader insurance and carrier industries too.

Huang did note that there are plenty of smaller firms not using AI that are doing fine. “However, Huang added,” as these smaller firms seek to grow and gain greater efficiency – coupled with the increase in AI usage in workers’ compensation, the potential consequence of not adopting AI does theoretically grow.”

The audience provided feedback on potential barriers as well, expressing concerns around cost and change to underwriting teams experience and habits.

6. What advice do the panelists have for those considering AI?

“It is helpful to know what you are you trying to solve,” said Frank Huang. “For instance, if it is a focus on profitability or overcoming lack of experience than develop a model to support this. If you are looking to save five points of return to justify the investment, map out how you get there and how the tool supports that.”

“We recently had discussions with potential investors and partners and AI was a big part of those conversations,” said Julie Cirillo. “What is the future for your PEO? If you are looking to grow and sell or recapitalize, I think you will find AI is going to be a critical part of that growth.”

Cirillo added that the human element does not go away with AI. “I would never let a model just kick a quote to sales. Our underwriting team is always going to review the price range that comes out of the model. I would say that 90% of the time we price within the range but another 10% of the time we use underwriter discretion and manage approvals accordingly.”

Learn More

Read the press release and discover how Engage PEO utilized Gradient AI’s solution to increase risk appetite, drive profitability, and spur rapid expansion. Read more about Gradient AI’s Workers’ Comp solutions, and solutions for PEOs in Group Health .

About NAPEO

The National Association of Professional Employer Organizations is The Voice of the PEO Industry ^TM . PEOs provide payroll, benefits, regulatory compliance assistance, and other HR services to small and mid-sized companies. NAPEO's mission is to grow, promote, and support the PEO industry. NAPEO advocates for the interests of PEOs at all levels of government, and provides robust member resources, vital networking and referrals, relevant and timely education, and effective public relations and marketing support.

Explore the benefits of Gradient AI's P&C or Workers' Comp Solutions.

Schedule a Demo →

From the Floor at NAPEO’s Risk Management Workshop: Industry Panelists Share Insights on AI for PEOs

Tue, 07 May 2024 20:45:45 GMT

GROUP HEALTH INSIGHTS BLOG | May 07, 2024

The Gradient AI Team

Insights from the Panel: “AI & Risk - The Role of AI in Insurance Underwriting”

The panelists provided a unique view of AI in the Workers’ Comp industry, as well as shared their first-hand experience with the use of AI in their own companies.

The engaging session focused on key discussion points, including:

1. What is driving the use of AI for PEOs and in the Workers’ Comp Market?

2. What challenges are companies trying to solve for with AI?

3. What is the Impact of AI in Workers’ Compensation?

4. What results have you seen since incorporating AI?

5. Why are some companies slow to implement AI as part of their operations?

6. What advice do the panelists have for those considering AI?

Below, we delve deeper into these discussion points.

1. What is driving AI for PEOs and in the Workers’ Comp Market?

2. What challenges are companies trying to solve with AI?

3. What impact has AI had on Workers’ Comp?

From a broad market view, Frank Huang shared that claims operations saw an early impact when simpler claims models came out about twenty years ago, that allowed better claims triage.

4. What results have you seen since incorporating AI?

Rowley also added three key additional benefits of utilizing AI in workers’ comp for Helpside, including:

Greater insights for prospects with gaps in data
Quicker underwriting turnaround times
Increased underwriting efficiency

Accurately evaluate risk
Onboard and empower less experienced underwriters
Support growth while improving efficiency

“I needed to know that regardless of which underwriter looked at the potential risk of a prospect, we would get consistent results,” said Cirillo.

5. Why are some companies slow to implement AI as part of their operations?

The audience provided feedback on potential barriers as well, expressing concerns around cost and change to underwriting teams experience and habits.

6. What advice do the panelists have for those considering AI?

Learn More

About NAPEO

Explore the benefits of Gradient AI's Group Health Solutions.

Schedule a Demo →

How AI can Help Streamline the Medicare Set-Aside Process

Mon, 06 May 2024 12:49:06 GMT

May 6, 2024

By Kim Wiswell

Medicare Set-Aside Arrangements (MSAs) are an important part of workers’ compensation programs and assist in earmarking settlement funds to cover future medical expenses stemming from workplace injury claims. However, providing an easy process for adjusters to refer MSAs, as well as managing operations to create these complex reports for the workers’ comp insurance industry efficiently has long been a challenge, especially when dealing with manual documentation and complex compliance requirements.

Let’s explore how the integration of AI and automation can effectively address these hurdles and make the MSA process more efficient.

Two Significant Challenges to Traditional MSA Processes Manual Documentation

The conventional MSA application process relies heavily on manual documentation. This involves the painstaking tracking of medical records, treatment history, and payment reports. This tedious approach is not only time-consuming, but also prone to inaccuracies, often requiring significant human resources. In addition, manual documentation increases the risk of missing critical information, leading to delays and potential compliance issues. Claims assistants or adjusters often spend a significant amount of time on MSA requests, ranging from an average of two to ten hours per referral, depending on factors like claim complexity, age, and the volume of medical records involved. Extended follow-up correspondence for clarification of body parts accepted under the claim or missing records can further extend this time.

Complex Regulatory Landscape

Another significant challenge is the navigation of the complex regulatory landscape of Medicare compliance. To be successful, the MSA application process requires meticulous attention to detail and expertise in interpreting Medicare's guidelines and regulations. Compliance errors can lead to penalties, delays in MSA approval by CMS, and heightened administrative burdens for all involved stakeholders.

Addressing These Challenges: Harnessing AI for Enhanced MSA Operations

AI-powered solutions can address both of these obstacles. By automating the documentation process, AI significantly reduces the manual effort needed to produce MSA determinations. Advanced algorithms can quickly analyze medical records, treatment histories, and billing information to generate comprehensive MSA documentation accurately. By automating repetitive tasks, AI reduces the likelihood of errors, accelerates processing times, and frees up resources for more strategic activities. In addition, AI technologies offer sophisticated compliance management capabilities, which allows all parties to navigate Medicare regulations with ease. AI-powered platforms continuously monitor changes in Medicare policies, ensuring MSA documentation remains up-to-date and compliant.

Additionally, AI algorithms can identify potential compliance issues proactively, allowing stakeholders to address them promptly and mitigate risks effectively. Integrating AI technologies into MSA operations can significantly impact MSA workflows. By automating the information and documentation collection and simplifying compliance management, AI streamlines both the MSA referral process, as well as internal operations, improving efficiency, and ensuring adherence to Medicare regulations. As the complexity of modern healthcare continues to evolve, leveraging AI becomes increasingly important for optimizing MSA processes and achieving better outcomes for all involved.

By Kimberly Wiswell, Director of Client Services, Embedded AI, Gradient AI

This article first appeared on WorkersCompensation.com.

Survey Says: Top Concerns from NCCI’s Most Recent Workers’ Comp Executive Poll and How AI Addresses Them

Mon, 29 Apr 2024 17:22:00 GMT

P&C INSIGHTS BLOG | Apr 29, 2024

The Gradient AI Team

In the fast-paced world of workers' compensation insurance, staying ahead of the curve is paramount. That's why the findings from the National Council on Compensation Insurance’s (NCCI) latest survey are so crucial. NCCI , renowned for its comprehensive workers’ comp data and industry insights, recently polled insurance executives in its Annual Survey , to uncover the top concerns gripping the workers' comp market today.

Several key concerns came out of the survey, including challenges regarding rate adequacy, medical inflation, and the changing workplace and workforce . Here’s a glimpse into these three key challenges and how innovative AI solutions are reshaping the landscape:

1. Rate Adequacy

The NCCI survey found that insurers are concerned with the relentless decline in rates and loss costs. The looming question: Are traditional analysis methods robust enough to safeguard long-term financial stability? Many fear the uphill battle to maintain profitability in this evolving landscape.

How AI Addresses this Challenge

Workers’ Comp is a challenging market for all types of workers’ comp insurers, including primary and reinsurance carriers, MGAs, captives, and PEOs. Enter AI: a game-changer for underwriting operations. With AI, insurers can enhance their underwriting processes with access to sophisticated computer models and vast industry data lakes. AI supports insurers’ underwriting decisions with precision and profitability, enabling them to recognize risks that might otherwise have been missed, and price accordingly, or decline the business depending on strategic priorities.

Integrating AI into underwriting operations can help:

Price policies more accurately and with deeper insights
Enable straight through processing by identifying low-risk applications
Accelerate expansion into new markets with a deeper understanding of risk

2. Medical Inflation

Escalating medical costs are sending shockwaves through the industry. Insurers are grappling with the uncertainty surrounding claim frequency and severity, exacerbated by inflationary pressures.

How AI Addresses this Challenge

AI to the rescue: By harnessing AI capabilities, underwriters and claims managers can unlock invaluable insights into potential claims, empowering them to make more informed decisions in an inflationary environment.

As an example of AI at work, a Third-Party Administrator (TPA) and Gradient AI customer , wanted to reduce the cost of claims and more quickly identify claims where targeted interventions could provide better outcomes for workers. The TPA determined that it needed a solution with the ability to:

· accurately identify high risk claims early – while there is still opportunity to improve their trajectory, and

· provide staff with an explanation of what is driving claim risk.

The TPA chose to integrate Gradient AI’s Risk Ranking solution into its claims operations, as it predicts which lost time or indemnity claims are most likely to exceed a high-cost threshold. This prediction can be delivered within a few days after a claim file is created and is automatically updated every time new data is added to the claim file. Equipped with this early warning, the TPA’s adjusters have the greatest opportunity to aggressively manage complex claims to reduce their severity and thereby help reduce cost.

3. The Changing Insurance Workplace and Workforce

The NCCI survey indicated insurers are also concerned about an evolving workforce that presents a new set of challenges – including telecommuters, gig workers, aging workers, young and/or less experienced workers, and an overall shortage of talent.

How AI Addresses this Challenge

AI is the bridge: Gradient AI found similar concerns discussed at a webinar we sponsored called How AI is Transforming the Insurance Industry and Bridging the Talent Gap, which presented key industry and labor trends and metrics, and the challenges insurers are facing to recruit and retain the talent needed for continued growth.

The panelists agreed that by harnessing AI, insurers can capture institutional knowledge and empower less experienced staff, ensuring operational efficiency and continuity. Specifically, it allows simpler claims to be sent to less experienced staff and processed quickly through expedited processes, while claims predicted to become more complex are promptly assigned to more seasoned adjusters. This approach frees up internal resources to focus on claims that need more attention at the time when it is needed most.

Conclusion: AI is the Linchpin for Workers’ Comp Insurers Seeking to Thrive in an Ever-Changing Landscape

Whether it's grappling with rate accuracy, claim cost containment, or navigating shifting workforce dynamics, the integration of AI into insurance operations isn't just a strategic move; it's a necessity for looking to future-proof their operations. By embracing AI-driven solutions, workers’ compensation insurers can unlock new insights into their claims and underwriting operations, enhance operational efficiency, and drive sustainable growth regardless of the challenges they face in this dynamic market.

Explore how Gradient’s AI-driven solutions can transform your workers’ compensation operations.

About the NCCI Survey

The 2023 Carrier Executive Survey includes responses from 101 executives representing 98 companies, including the largest multiline, multistate carriers: as well as many smaller, regional, and single-line workers compensation insurers. These survey results inform NCCI’s research and priorities going into 2024 to support the health of the system. To view the full summary of the NCCI Survey click here .

Explore the benefits of Gradient AI's P&C or Workers' Comp Solutions.

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Transforming Group Health Insurance with AI: Overcoming Key Challenges

Wed, 24 Apr 2024 17:32:20 GMT

GROUP HEALTH INSIGHTS BLOG | Apr 24, 2024

The Gradient AI Team

Artificial Intelligence is revolutionizing operations and outcomes in the group health insurance sector, offering a pathway towards enhanced insight, robust risk management, and streamlined underwriting processes.

In our recent white paper, How Artificial Intelligence Addresses Five Key Challenges in Group Health Underwriting , we delve in detail into how AI mitigates and resolves five key challenges encountered by health insurers, empowering them to navigate complexities and foster more sustainable growth in a changing marketplace.

How Artificial Intelligence Addresses Five Key Challenges in Group Health Underwriting

Download the White Paper →

Key Challenges in Group Health Underwriting

Underwriters in the group health insurance industry play a crucial role in assessing and managing risk, ensuring the financial stability of insurance plans, and facilitating the provision of comprehensive coverage to diverse groups of individuals. However, the landscape of group health insurance presents increasing challenges that demand expertise and adaptability.

Here are 5 of the top challenges:

1. Inaccurate Risk Assessment

Traditional underwriting methods may fall short in accurately predicting risk, leading to either overpricing or underpricing of policies. This challenge underscores the critical need for more sophisticated risk assessment techniques that can leverage vast datasets and extract nuanced insights.

2. Underwriting Inefficiency

Timeliness and accuracy are paramount in the underwriting process, particularly during peak periods of policy issuance. The inefficiencies inherent in manual underwriting procedures can impede insurers' ability to meet market demands and capitalize quickly on opportunities.

3. Staff Turnover and Limitations

The aging workforce and the challenge of knowledge transfer pose significant obstacles to maintaining underwriting excellence. As experienced underwriters retire, there is a pressing need to capture and disseminate institutional knowledge to new team members efficiently.

4. Desire to Improve Outdated Methods

Legacy systems and traditional actuarial methods may no longer suffice in a rapidly changing landscape characterized by growing data volumes and shifting consumer preferences.

5. Plans to Expand into New Markets with Limited Data

Venturing into new markets presents inherent challenges, particularly when data availability is limited or nonexistent. Insurers must grapple with the complexities of assessing risk in unfamiliar territories while striving to maintain profitability and competitiveness.

AI’s Role in Addressing These Challenges

Enter artificial intelligence. AI offers invaluable insights, enhancing risk management, and streamlining underwriting operations. Health insurers who have integrated AI into their underwriting and risk management operations have found that AI enhances risk assessment accuracy by analyzing vast data sets and providing nuanced insights. Further, AI enables increased productivity, reduced turnaround times, and more accurate pricing.

In addition, AI can capture institutional knowledge - guiding new underwriters and automating processing where appropriate – enhancing overall efficiency.

“If there are ways to really provide better data, more accurate and quicker, that allow underwriters to become more financial decision makers rather than raters, that’s a huge change in how we are doing things.”

- Jay Sheehy, Chief Underwriting Officer, Gravie

Artificial Intelligence stands as a transformative force within the group health insurance industry, offering a multifaceted solution to top challenges faced by insurers. As more and more insurers embrace AI as a catalyst for change, they position themselves for sustained growth, competitiveness, and relevance in an increasingly dynamic landscape.

Learn More

For an in-depth look into how AI mitigates the five challenges outlined above, download our recent white paper, How Artificial Intelligence Addresses Five Key Challenges in Group Health Underwriting .

Explore the benefits of Gradient AI's Group Health Solutions.

Schedule a Demo →

Gradient AI Signs Up Engage PEO

Tue, 23 Apr 2024 21:59:06 GMT

April 23, 2024

The latest from Gradient AI, who have signed up Engage PEO:

Gradient AI , a leading enterprise software provider of artificial intelligence (AI) solutions in the insurance industry, announced at NAPEO’s Risk Management Workshop that Engage PEO is leveraging its Underwriting Solution to enhance its workers’ comp underwriting process and fuel its business expansion as part of Engage PEO’s broader AI strategy to facilitate operational efficiency and improve results.

Engage PEO, a leading professional employer organization (PEO) offering HR outsourcing solutions to small and mid-sized businesses nationwide, has been experiencing tremendous growth recently. The company’s strong growth during the past several years has included the completion of five mergers and acquisitions, the addition of three operational centers, and a significant increase of clients and worksite employees.

To help meet this demand, Engage PEO implemented Gradient AI’s Underwriting Solution to enhance the accuracy and efficiency of its underwriting process. By leveraging the solution’s AI-powered predictive analytics and a large industry dataset, Engage PEO has enhanced its risk assessment capabilities, allowing for an increased risk appetite and better insights as it expands into new markets and services a broader range of clients.

“Gradient AI’s ability to harness AI to glean insights from its vast data lake, predict future outcomes, and make more informed decisions has contributed significantly to the success of our Work Comp program,” said Julie Cirillo, chief risk officer, Engage PEO. “It has even expanded our risk appetite – we now consider higher risks we previously would have declined quoting. The model enables us to accurately evaluate that risk, we have expanded our market reach and increased profitability. In addition, Gradient AI has significantly streamlined our underwriting process, reducing evaluation times from hours to minutes.”

Engage PEO is also using Gradient AI’s solution to address talent shortages within the industry. By leveraging Gradient AI’s technology to capture and apply institutional knowledge, Engage PEO can onboard and empower less experienced underwriters, who now leverage Gradient AI’s underwriting solution to obtain an accurate risk prediction.

Gradient AI has also had a significant impact on the company’s sales and renewal season. Cirillo commented, “This year, we relied on Gradient AI during our busiest season from October through January, and the results were remarkable. Since the sales and renewals were driven by AI powered data insights, the experience was seamless for both our team and clients. It was the first time in 10 years I got to enjoy Christmas!”

“By leveraging the power of Gradient AI, Engage PEO is positioned to drive even greater success in the PEO industry,” said Stan Smith, founder and CEO, Gradient AI.

Not only is Gradient AI significantly enhancing Engage PEO’s underwriting process, but it is also being used to support the company’s M&A activities by evaluating segments of risk within a whole book of business. Julie Cirillo added, “This solution has assisted in integrating our acquired companies, allowing for fast and accurate review of both the portfolio and individual clients to ensure profitability is maintained with the transition to our risk-bearing program.”

This article first appeared on Insurance Edge .

How AI Can Improve Treatment & Cost Forecasting Accuracy Within the Medicare Set-Aside Process

Mon, 22 Apr 2024 18:02:49 GMT

P&C INSIGHTS BLOG | Feb 26, 2024

The Gradient AI Team

In a previous blog post , we discussed how AI and automation can streamline the Medicare Set-Aside (MSA) process by reducing manual documentation processes and assisting with regulatory compliance. In this blog, we’ll focus on the challenges in treatment and cost estimation within MSAs. We’ll also look at how AI-driven solutions can help improve these processes in workers’ compensation programs.

Two Challenges in MSA Treatment Forecasting and Cost Estimations

1. Treatment Forecasting Inefficiencies

The current MSA process is laborious. First, a specialized allocator physically goes through the injured worker’s medical records, creating a summary of the injury and the medical treatment. For each treatment, the allocator must determine whether the recommended treatment has been provided to the injured worker. Any unfulfilled recommendations must be included in the MSA. Next, a treatment plan is developed based on physician recommendations for specific treatments such as surgeries, diagnostic testing, hospitalizations, etc., as well as treatment corresponding to CMS’s standardized guidelines. Last, the allocator prices the services and drugs under the treatment plan using state fee schedules and other pricing resources specified by CMS and recommends an MSA amount for the claim.

All of this can take from several days to over a week, which unfortunately leads to delays in claim closure and settlement funds being paid to injured workers. In addition, the existing approach often leads to unpleasant surprises for claims managers. Unrecognized treatment recommendations often, consequently, increase the overall MSA amount, sometimes making the settlement impossible.

2. Cost Projection Inaccuracies

It is challenging to accurately estimate future medical costs within the current MSA framework. Most services are priced out based on state workers’ compensation fee schedules or, when unavailable, usual, customary and reasonable (UCR) fees for the jurisdiction. As fee schedules are unique to each state, and fees are updated as often as quarterly, determining the correct pricing can be a complex process. Also, as the claimant’s medical condition and health status change over time, often necessitating new and different treatments, the amount allocated in an MSA may not accurately reflect the future funds needed. Compounding this, CMS requires MSAs associated with settlements to have been done within the past six months. After this point, they are considered stale-dated, leading to additional costs for the claims payer, both for multiple MSA updates and increasing treatment fees over the course of lengthy settlement negotiations and proceedings.

AI Solutions Address These Challenges and Enhance MSA Management in Workers’ Comp Programs

As healthcare continues to evolve, AI will become even more critical to streamlining operations to achieve better outcomes and ensuring the long-term success of workers' compensation programs.

Learn More

Explore how Gradient’s AI-driven solutions can transform your workers’ compensation operations. Contact us today to learn more about solutions that can streamline your process.

Explore the benefits of Gradient AI's P&C or Workers' Comp Solutions.

Schedule a Demo →

Engage PEO Enhances Workers’ Comp Underwriting with Gradient AI

Mon, 22 Apr 2024 13:11:09 GMT

AI-powered Solution Increases Risk Appetite, Drives Profitability, and Spurs Rapid Expansion

Indianapolis, IN-April 22, 2024 - Gradient AI , a leading enterprise software provider of artificial intelligence (AI) solutions in the insurance industry, announced at NAPEO’s Risk Management Workshop that Engage PEO is leveraging its Underwriting Solution to enhance its workers’ comp underwriting process and fuel its business expansion as part of Engage PEO’s broader AI strategy to facilitate operational efficiency and improve results.

“By leveraging the power of Gradient AI, Engage PEO is positioned to drive even greater success in the PEO industry,” said Stan Smith, founder and CEO, Gradient AI.

About Gradient AI

Media Contact

Elyse Familant

Results PR

elysef@resultspr.net

7 Leaders Give Their Advice for Remote Mentorship

Thu, 28 Mar 2024 21:13:47 GMT

March 26, 2024

Gradient AI's Eric Benny among seven mentors from culture-focused tech companies who share their advice on what makes a great mentor

The relationship between managers and the people who report to them is a cornerstone of every organization. One clever way businesses enhance these hierarchies is through mentorship programs, which often provide additional avenues for stronger connection, career development, productivity and company culture.

Mentorship is valuable, regardless of whether the relationship is organic the result of a company initiative. In a recent survey from Oliviet Nazarene University, 76 percent of respondents said that mentors are important, but only 37 percent currently had a mentor.

Additionally, the survey concluded that professionals who had mentors at their current jobs were happier than those who didn’t. Regardless of who an employee’s mentor is — their direct manager, a senior colleague or an external mentor — a mentorship relationship provides resources and support a mentee may not normally have access to in their current role.

Built In had the chance to speak with seven different mentors from culture-focused tech companies to hear their advice on what makes a great mentor. One common piece of advice: Connect early, often and in person when you can with your mentee. Read more advice from the mentors below for some great ideas to make mentorship a rewarding experience for everyone involved.

Eric Benny

Staff Software Engineer • Gradient AI

Gradient AI brings state-of-the-art artificial intelligence and machine learning SaaS solutions to the insurance industry.

How do you ensure your mentee is getting the support they need from a professional standpoint, whether that's through weekly check-ins, collaboration tools, etc.?

Weekly check-ins can be helpful if both people find the structure useful. Keeping an open line of communication is critical. This can be less formal check-ins, or side-sharing questions and new information. Whenever I learn something new or find an interesting article, I try to share that with the people that I think could also benefit from it.

Having good documentation and examples for processes and patterns used within the team allows mentees to learn and familiarize themselves with these topics before jumping into work. It’s also useful for these documents to contain references to provide additional opportunities to gain context and knowledge. This documentation can allow new team members to contribute more quickly and confidently. For software engineers, code reviews are a great way to collaborate on a task and give direct feedback.

How do you look for opportunities to get your mentee involved in the company culture?

Encouraging folks to present their work to others is a good way to gain visibility throughout the company and solicit feedback in a group setting. We hold monthly chapter meetings for the different engineering disciplines which are a great opportunity to learn what others are working on and to present on topics that interest you.

I try to generally encourage people to reach out to me or others whenever they have questions. One thing I really enjoy about Gradient AI is that everyone is willing to help and lend their time regardless of the team or topic. It’s something I’ve benefitted from when learning from others, and I try to make myself as available as possible to anyone that I may be able to help.

Relying too heavily on scheduled meetings will often lead to delays, so feeling comfortable reaching out is something that’s very important in a remote environment and helps facilitate the types of conversations you might expect to have in an office setting.

“At Gradient AI, I really enjoy that everyone is willing to help and lend their time regardless of the team or topic.”

What advice do you have for other mentors who want to strengthen their approach to remote mentorship?

The remote workplace can make mentorship a bit more difficult without the opportunity to see people daily — which is why maintaining and encouraging an open line of communication is key. The scheduled meetings can be useful for more formal check-ins, but I think building a level of trust and comfort so that both people feel comfortable reaching out at any time helps ensure that everyone gets the most out of the relationship.

Michael Smith

Head Of Advertising Intelligence at Sensor Tower • Sensor Tower

Sensor Tower ’s software suite provides innovative mobile performance, trend insights and ad network products that produce enterprise-level data on the global app economy.

How do you ensure your mentee is getting the support they need from a professional standpoint, whether that's through weekly check-ins, collaboration tools, etc.?

If the organization is not healthy, I first work to fix that. Then, I focus on personal connection, career coaching, collaboration tools, our purpose and ownership.

Building personal connections demands recognizing individual needs and aspirations, often through regular in-person or virtual meetings. This helps them align and engage with the work. Career coaching is crucial and requires an understanding of staff roles and the industry. As I guide staff, I draw upon my research and experience and tailor the advice to help them navigate their career path. To collaborate, we have some essential tools: chat, meetings, email, shared docs and wikis. I work with the team to create a clear communication plan for seamless teamwork and coordination with other teams.

Having a strong purpose enhances team motivation and helps us drive towards common objectives, our company vision and mission and team members’ own goals. I encourage ownership among team members to develop their personal investment in their work, leading to higher productivity and personal fulfillment. Ultimately, I want to empower each team member, ensuring they feel valued and capable of contributing to our collective success.

How do you look for opportunities to get your mentee involved in the company culture?

Company culture isn’t just happy hour drinks, holiday parties or swag. It is a combination of shared routines, interactions, experiences and identity that characterize how a staff member connects with their group — whether that be a team, a hybrid group aligned on shared interests or the entire company. It’s like a product, offering teams purpose and social connections without forced promotion, eventually becoming part of their identity.

“Company culture isn’t just happy hour drinks, holiday parties or swag. It is a combination of shared routines, interactions, experiences and identity.”

For managers in companies where culture is still developing, fostering a microculture within the team is key. Personally, as a manager, I start with an assessment of what I think the company culture is and develop a written prototype of what I think the culture should be, adapted for my team, and I write all of this down in a strategy document. Then, I analyze the company values — most companies have them — and define in concrete behavioral terms what it looks like to live those values. I might also introduce essential team values, keeping the list concise to ensure memorability and engagement. Essentially, strong cultures emerge from the systematic cultivation of these team microcultures for overall company culture growth.

What advice do you have for other mentors who want to strengthen their approach to remote mentorship?

To strengthen remote mentorship, I first prioritize getting to know each mentee. I find that personal connections foster trust so that they receive guidance more openly and thoughtfully. Regular in-person meetups are essential, and I include the team in planning to help the events become memorable bonding experiences rather than necessary travel. By navigating through organizational chaos and bridging gaps between resources, technology and teams, I am a trailblazer helping to promote a smoother workflow, and a smoother workflow lowers the barriers to innovation.

Within our team, I cultivate self-organization, a companion to ownership — I don’t think you can have one without the other. Teams that self-organize develop a solidarity and resilience that I’ve never seen otherwise. They’re pleasantly and diligently engaged in delivering positive outcomes, and it’s no surprise that those teams tend to have a lower turnover. And even though every member of our team works remotely in various parts of the world, I mentally feel like we’re meeting in person. I think this is because we have already physically met, and we are conversing so frequently every day.

Shane Mulrooney

Founder & General Counsel • New Era ADR

New Era ADR is a virtual mediation and arbitration platform designed to resolve legal disputes in less than 100 days, save businesses and individuals time and expenses and prevent gamesmanship in litigation.

How do you ensure your mentee is getting the support they need from a professional standpoint, whether that's through weekly check-ins, collaboration tools, etc.?

Ensuring that my mentee receives the necessary support from a professional standpoint involves a combination of strategies. Weekly scheduled check-ins are crucial for maintaining alignment and preventing us from veering off track with tasks, follow-ups and major priorities.

However, in a remote work environment, this alone may not suffice. I make myself readily available for spontaneous communication through platforms like Slack or text messages, adapting to what works best for us. In an office setting, the ability to casually discuss problems or brainstorm solutions by popping into someone's office is invaluable. Replicating this dynamic remotely requires intentional effort. Encouraging my mentee to reach out at any time during working hours without feeling like they're intruding is essential. I actively promote quick messages or impromptu Zoom calls for brief discussions when necessary, fostering a collaborative atmosphere and enhancing productivity.

How do you look for opportunities to get your mentee involved in the company culture?

When it comes to integrating my mentee into our company culture, being part of a small team certainly facilitates the process. We often engage in team activities together, fostering a strong sense of cohesion. However, I take proactive steps to ensure my mentee feels fully immersed in our culture. I facilitate introductions between my mentee and other team members early on, orchestrating conversations to help her quickly integrate and feel connected. Additionally, we have a practice of holding regular cross-functional one-on-one meetings, though not necessarily weekly, which allows everyone to gain exposure to different parts of the business and each other.

Encouraging my mentee to reach out to others on the team has also been important. When she brings up a need or question to me, I often redirect her to someone else who may be better suited to assist, empowering her to initiate these interactions herself. Finding the right balance between respecting others' time and fostering an open culture is crucial. Yet, in a remote setting, I believe it's important to encourage more proactive outreach, given the lack of organic collaboration that naturally occurs in an office environment.

What advice do you have for other mentors who want to strengthen their approach to remote mentorship?

My advice would be to initially lean towards being more involved and connected in your remote mentorship approach. For instance, my mentee and I held twice-weekly regular one-on-one check-ins for the first nine months. I also maintained an open-door policy throughout the rest of the week.

As your mentee gains confidence, independence, and solid working relationships with others on the team, you can gradually scale back these interactions. It's better to err on the side of being overly present initially rather than not being available enough. It's easier to scale back a redundant process than to try to introduce a new one later on.

“It's better to err on the side of being overly present initially rather than not being available enough..”

However, as with any mentorship relationship, it's essential to provide your mentee with enough freedom to experiment, make mistakes, and learn independently. Be cautious that your increased availability doesn't inadvertently lead to micromanaging. Similar to being an effective manager in an office setting, ensuring your mentee never feels isolated is crucial in a remote environment. Offering ample support while allowing room for autonomy is key to fostering growth and success, not just for your mentee but for everyone in a remote workplace.

Alex Dao

Engineering Manager • Valon

Valon is a financial wellness platform oriented for a simplistic, dependable and human mortgage lending experience.

How do you ensure your mentee is getting the support they need from a professional standpoint, whether that's through weekly check-ins, collaboration tools, etc.?

I think this starts with truly understanding your mentee’s personal goals. Everyone is unique — what they care about, what they are motivated by and how they respond to feedback and advice. I try to begin any relationship by getting to know the other person on more than just surface-level stuff. The most important foundation of mentoring is trust, and being intentional about connecting with your mentee will fast-track building that up. Once you have that trust, it’s a lot easier to figure out how to provide the right amount of support and tailor it to that person.

If you’re managing them in an official capacity, then you should at minimum have a weekly one-on-one. For more general mentorships, the cadence can vary. Typically, the more senior the mentee, the less frequent the cadence needs to be because the topics covered are more strategic and happen over longer time periods.

How do you look for opportunities to get your mentee involved in the company culture?

As a mentor, you usually have access and visibility to more of the company than the person you’re mentoring, whether that’s due to your role, relationships or experience. Because of this, you have the ability to highlight or provide opportunities that they otherwise wouldn’t have been exposed to.

One person I’ve been mentoring has been really interested in learning about what goes into growing a team. As an engineering manager at Valon, I frequently conduct hiring manager interviews, so I invited this person to shadow the next one I conduct. We plan to recap afterward with our thoughts on how the interview went and how the candidate matches what we’re looking for at Valon. I’m excited to see how my mentee approaches it, and maybe I can learn a thing or two from a different point of view, as well!

What advice do you have for other mentors who want to strengthen their approach to remote mentorship?

As a remote mentor, it’s important to be disciplined in how you approach mentorship because of the limited amount of organic interaction you’ll have with them. My advice would be to focus on clear communication, set and evolve goals that you can help hold the mentee accountable to, and provide regular feedback on their career development. Doing all of this consistently helps develop a long-term productive and healthy mentee relationship, even if you’re not there in person.

“Focus on clear communication, set and evolve goals that you can help hold the mentee accountable to, and provide regular feedback on their career development.”

Finally, I like to think of mentorship as a two-way street, where both parties involved need to be bought in and invested in it. It’s easier to feel more disconnected when remote, so you might find yourself needing to put in more effort at first to encourage your mentee to make the best use of your time together.

Eric Paarman

Director, Expansion Revenue and Professional Services • Tebra

Tebra provides operations and growth software to independent practices, empowering them with modernized healthcare for patients everywhere.

How do you ensure your mentee is getting the support they need from a professional standpoint, whether that's through weekly check-ins, collaboration tools, etc.?

Whether I'm interacting with a peer, employee or mentee, I find that clear communication and expectation-setting is foundational to success in a remote environment. I have to guard against leaning on the presumption that an individual I'm working with might "know me" enough to fill in the gaps and figure it out. That isn't fair. If I've identified an area of success, I acknowledge it. I there's an opportunity for professional growth and maturation, I give specific feedback.

Build trust and respect for the mentee/mentor relationship with consistent, scheduled check-ins. Make sure you've prepared an intentional agenda, and come ready to review progress, providing feedback to ensure your mentee is feeling supported. When possible, come armed with data and share specific examples. I try to be mindful of my nonverbal communication in a remote setting as well. There are many collaboration tools, ice breakers and styles of leadership that you can employ — none replace the impact of genuine connection and interest.

“There are many collaboration tools, ice breakers and styles of leadership that you can employ — none replace the impact of genuine connection and interest.”

How do you look for opportunities to get your mentee involved in the company culture?

Involvement in company culture can provide such a wonderful opportunity to grow professionally and establish cross-functional relationships. I try to balance providing mentees with an opportunity that will positively impact the business while helping them hone a professional competency that may not be a strength yet.

For example, I mentored an employee who had a deep, technical understanding of our product. They shared that they wanted to pursue a career in leadership but admittedly had limited experience in project management or delegation. We discussed how they could scale their efforts to educate the team by creating an environment that promoted learning and enablement outside of the team meeting directly with them. The mentee partnered with our enablement team and product teams, and they ultimately helped to create a resource library that was leveraged by the entire company. While I'm a big proponent of leveraging existing resources, I encourage my mentees to consider what doesn't exist today and explore new connections.

What advice do you have for other mentors who want to strengthen their approach to remote mentorship?

It's important to listen and evaluate your strategy with each mentee. Ask for their feedback in addition to providing your own. Be intentional with each connection and avoid starting a one-on-one with "What do you have for me?" If you're in the position to mentor, you likely had someone who invested time and energy in your development — pay this forward.

Joy Thomas

VP, Operational Excellence • Arity

Arity is a mobility data and analytics company focused on improving transportation.

How do you ensure your mentee is getting the support they need from a professional standpoint, whether that's through weekly check-ins, collaboration tools, etc.?

I typically meet with my mentees once a month via Zoom video call, but I also encourage them to not wait for our monthly meetings and to connect via Teams or Slack messaging at any time to ask questions, bounce ideas or just chat informally about anything whether it's business or personal. And, of course, I’m still an advocate of live, in-person meetings and try to make those happen too. Arity has several opportunities where employees get together in person like department and team onsite meetings or sales offsites or conferences. If there’s an opportunity to connect live, do it!

“If there’s an opportunity to connect live, do it!”

How do you look for opportunities to get your mentee involved in the company culture?

Throughout the course of my relationship with a mentee, I find out about areas of interest like potential future roles, parts of the company they’d like to learn about or capabilities they wish to develop like public speaking or leadership. I’m happy to connect them with other subject matter experts or employee resource groups to give them insights that I don’t have as well as access to resources that can help them.

For example, one of my mentees was very interested in improving their public speaking capabilities so I introduced them to Toastmasters, one of our employee groups. Another mentee was very interested in developing their leadership skills and helping drive Arity’s culture, so when an opening on our Diversity, Equity, Inclusion and Belonging committee came up, we agreed that it was a great opportunity for them to take on.

What advice do you have for other mentors who want to strengthen their approach to remote mentorship?

Like any mentor-mentee relationship, I believe it’s important to establish a communication cadence and how often and how we meet, determine what the mentee wants and expects to get out of the relationship and discuss those objectives each time we meet to make sure we’re making progress. I also find it helpful to have an agenda or topic we want to cover in each meeting which often requires the mentee to prepare or do some pre-work. Finally, it’s important to me to build a trusting relationship by being available and meeting the mentee where and when they need — even on an ad hoc basis — which tools like Teams/Slack easily enable.

Max Johnson

Staff Software Engineer, Platform • Sprout Social

Sprout Social provides a social media management and analytics platform with comprehensive publishing and engagement functionality, customer care, influencer marketing, advocacy and AI-powered business intelligence.

How do you ensure your mentee is getting the support they need from a professional standpoint, whether that's through weekly check-ins, collaboration tools, etc.?

While the shift to remote and hybrid work has presented numerous advantages, a significant challenge is the absence of spontaneous conversations that naturally unfold in an open office setting, facilitating a passive exchange of team updates and individual statuses. In a remote work environment, it’s crucial to actively cultivate these interactions.

For engineering mentors, be sure to dedicate time in your day to review pull requests, pair on a tricky bug investigation or bounce ideas off of each other while collaborating on a technical design. Creating purposeful and informal video catch-ups is a great way to connect and share ideas, reminiscent of the “gather around a whiteboard” atmosphere that many engineers enjoy. Moreover, these intentional conversations play a vital role in better gauging their current workload and well-being. Are they feeling burnt out and stuck on a challenge, or are they eager to take on new and rewarding growth opportunities?

How do you look for opportunities to get your mentee involved in the company culture?

At Sprout, the onboarding process introduces new employees to a variety of community resource groups and opportunities to immerse themselves in our company culture. Most of the individuals I’ve mentored have been heavily involved in these areas from early on and have often been the ones to introduce me to new opportunities and ways to get involved. One of my favorites is the #talk-donut Slack channel, which pairs individuals across the organization for virtual “coffee and donut” chats. It’s a great way to connect with others to learn about different aspects of our business, or even discover a shared hobby!

When it comes to connecting with engineers, one of the challenges I often faced in my career was not knowing where to reach out for a question or project outside of our team. Over the years, I am now able to share my network to help make these introductions, while encouraging mentees to build their own network of go-to contacts across the organization. This can also be a great opportunity to help mentees build domain expertise when another team asks “Who can I reach out to for X?"

What advice do you have for other mentors who want to strengthen their approach to remote mentorship?

Be present and make yourself accessible. It’s easy to get focused on a project and seemingly disappear in remote work, but this can often be a hurdle to those who feel like they might be interrupting you when they need guidance. Be active in Slack and meetings, and set aside time in your day to help unblock others. The time spent sharing knowledge and building the skills of your team pays off exponentially when you see others confidently take on new challenges and become an expert in their own domain.

“The time spent sharing knowledge and building the skills of your team pays off exponentially when you see others confidently take on new challenges and become an expert in their own domain.”

When your time inevitably becomes more scarce, be sure to make your knowledge just as accessible. Sharing knowledge on a remote team can be as much about writing documentation as it is about writing code. In software development, there is so much context beyond the code that it can take years to fully understand the way things work and how they became that way. Try to leave a virtual paper trail of decisions, discussions on trade-offs, detailed troubleshooting guides, and feature documentation to make this journey a little bit easier for each person who follows. Your mentees — and your future self — will thank you!

Taryn Cramer

Consulting Manager • Mission Cloud

Mission Cloud Services is a leading AWS Premier Tier Services Partner and Cloud Managed Services Provider, delivering a comprehensive suite of cloud services designed to help businesses migrate, manage, modernize and optimize their AWS cloud environments.

How do you ensure your mentee is getting the support they need from a professional standpoint, whether that's through weekly check-ins, collaboration tools, etc.?

I see mentoring relationships as an act of community. It is a continual opportunity to give, receive and learn together through varied circumstances. I find that relationships are bolstered when mentors are both proactive and reactive. I schedule regular sessions with mentees, so I am ever present in their daily lives. I also do my best to identify opportunities in real time for praise and feedback through Mission’s project ecosystem.

“I see mentoring relationships as an act of community. It is a continual opportunity to give, receive and learn together through varied circumstances.”

When I embark on a mentoring journey, I also start with sessions governed by vulnerability. We often try to segment our personal and our career selves as if two separate entities, but I find that we most often bring our whole selves to every moment. In these sessions, we talk about each other’s personal history. You may find that someone you are supporting had a parent who expected perfection, an individual who lost a sibling or a person who had a difficult childhood. I then typically transition into asking thoughtful questions because I find that most of us deep down know the personal work that we need to do, and I am merely a facilitator, confidant and friend.

How do you look for opportunities to get your mentee involved in the company culture?

I leverage an analogy in mentoring of “tending a garden” that I’ve found helpful. We work hard to amend, till and make our soil fertile, and in the end, we are merely planting seeds. I find this analogy helps mentees to refocus their energies. We are not willing or forcing certain things to happen nor expecting progression to look a certain way. We are merely creating an environment colored by serendipity and opportunities for personal growth and advancement.

Getting involved in company culture and resource groups are great examples of ways to cast seeds. I talk to mentees about Mission’s ERGs. Participation in ERGs provides mentees opportunities to give back, to make themselves known, and to gain insights and skills that will serve them throughout their careers.

What advice do you have for other mentors who want to strengthen their approach to remote mentorship?

I think it is important for mentors to internalize that we cannot and should not have all the answers. There is no golden equation that renders 100% certainty in a mentee’s desired outcomes. I also suspect that if we look at our own journeys, we will find ample examples where our paths made unexpected shifts and turns that brought us to where we were meant to be. As mentors, our job is to help people see their potential, to explore a world of infinite possibilities, and to help them create an environment or ethos that lends itself to career progression and personal growth. And it is my opinion that this starts fundamentally with active listening, a lot of laughter, and a pervasively kind heart.

This article first appeared on BuiltIn .

Streamlining Medicare Set-Aside Operations with AI

Thu, 28 Mar 2024 20:39:00 GMT

P&C INSIGHTS BLOG | Feb 26, 2024

The Gradient AI Team

Medicare Set-Aside Arrangements (MSAs) are an important part of workers’ compensation programs and assist in earmarking settlement funds to cover future medical expenses stemming from workplace injury claims . However, providing an easy process for adjusters to refer MSAs, as well as managing operations to create these complex reports for the workers’ comp insurance industry efficiently has long been a challenge, especially when dealing with manual documentation and complex compliance requirements.

In this blog, we'll explore how the integration of AI and automation can effectively address these hurdles and make the MSA process more efficient.

Two Significant Challenges to Traditional MSA Processes

1. Manual Documentation

2. Complex Regulatory Landscape

Another significant challenge is the navigation of the complex regulatory landscape of Medicare compliance. To be successful, the MSA application process requires meticulous attention to detail and expertise in interpreting Medicare's guidelines and regulations. Compliance errors can lead to penalties, delays in MSA approval by CMS, and heightened administrative burdens for all involved stakeholders.

Addressing These Challenges: Harnessing AI for Enhanced MSA Operations

In addition, AI technologies offer sophisticated compliance management capabilities, which allows all parties to navigate Medicare regulations with ease. AI-powered platforms continuously monitor changes in Medicare policies, ensuring MSA documentation remains up-to-date and compliant. Additionally, AI algorithms can identify potential compliance issues proactively, allowing stakeholders to address them promptly and mitigate risks effectively.

Explore how Gradient’s AI-driven solutions can transform your workers’ compensation operations. Contact us today to learn more about solutions that can streamline your process!

Explore the benefits of Gradient AI's P&C or Workers' Comp Solutions.

Schedule a Demo →

Gradient AI's SAIL Delivers Enhanced Group Health Risk Assessment and Underwriting Intelligence to PrestigePEO

Tue, 19 Mar 2024 13:28:14 GMT

PrestigePEO Leverages AI-driven Insights for Informed Decision-Making and Optimal Pricing Strategies

Boston, MA March 19, 2024 - Gradient AI , a leading enterprise software provider of artificial intelligence (AI) solutions in the insurance industry, today announced successful results of the implementation of its SAIL™ AI Solution by PrestigePEO , one of the nation’s largest professional employer organizations (PEO).

SAIL provides comprehensive insights into group health risks, enabling data-driven decision-making, smarter underwriting, and more accurate pricing strategies. In PrestigePEO’s case, which offers large-company employee benefits and HR services to small and medium-sized businesses (SMBs), SAIL enables the company to identify both good risk and bad risk among SMB populations covered under its umbrella of services and streamline group insurance selection processes while supporting its growth strategy.

Before adopting SAIL, PrestigePEO faced challenges in acquiring comprehensive information about the risk profiles of various groups, hindering its ability to make informed underwriting decisions for group health insurance for the SMBs it serves.

Gradient AI's SAIL Solution has significantly enhanced PrestigePEO's underwriting process. By harnessing SAIL's advanced machine learning capabilities and vast de-identified data sources, PrestigePEO now possesses a comprehensive understanding of group risk profiles. This knowledge empowers its underwriters to make data-driven decisions, leading to more accurate pricing and improved risk management strategies.

Andrew Lubash, PrestigePEO founder and CEO, said, "The knowledge derived from SAIL enables PrestigePEO to strategically determine pricing for plans tailored for SMBs. This AI solution recognizes different risk factors, providing guidance on adopting a more assertive pricing approach during low-risk scenarios and when to reconsider or adjust pricing to accommodate higher risks. This aids in gaining a deeper understanding of the marketplace dynamics and the businesses we engage with, ultimately enhancing our decision-making process."

"SAIL has significantly improved PrestigePEO's underwriting process," said Stan Smith, founder and CEO of Gradient AI. "By providing actionable insights into group risk profiles, SAIL enables PrestigePEO to make better and more informed decisions, ultimately leading to improved loss ratios and enhanced profitability.”

About PrestigePEO

Melville, N.Y.-based PrestigePEO , also known as Prestige Employee Administrators LLC, provides integrated, end-to-end outsourced solutions to small and medium-sized businesses (SMBs) for managing all integral benefits and human resources operations. This includes support for employee benefits management, payroll administration, workers’ compensation, compliance, and HR guidance, among other areas. PrestigePEO’s full spectrum of solutions helps SMBs lower employee benefit costs, reduce administrative workloads, and manage critical HR functions.

PrestigePEO is among only 1% of PEOs accredited by the Employer Services Assurance Corporation (ESAC), certified (CI) through Workers Comp Risk Management and classified as a Certified Professional Employer Organization (CPEO) by the IRS – demonstrating financial stability and strict adherence with a diverse range of regulatory and security standards.

To learn more, visit www.prestigepeo.com and follow PrestigePEO on Facebook , Twitter , LinkedIn and YouTube .

About Gradient AI

Gradient AI is a leading provider of proven artificial intelligence (AI) solutions for the insurance industry. Its solutions improve loss ratios and profitability by predicting underwriting and claim risks with greater accuracy, as well as reducing quote turnaround times and claim expenses through intelligent automation. Unlike other solutions that use a limited claims and underwriting dataset, Gradient AI’s software-as-a-service (SaaS) platform leverages a vast industry data lake comprising tens of millions of policies and claims. It also incorporates numerous other features, including economic, health, geographic, and demographic information. Customers include some of the most recognized insurance carriers, MGAs, MGUs, TPAs, risk pools, PEOs, and large self-insured employers across all major lines of insurance. By using Gradient AI's solutions, insurers of all types achieve a better return on risk. To learn more about Gradient AI, please visit https://www.gradientai.com .

Media Contact

Elyse Familant

Results PR

elysef@resultspr.net

The Rise of Generative AI in Insurance

Wed, 13 Mar 2024 19:06:11 GMT

March 13, 2024

By Stan Smith

The global market for artificial intelligence (AI) in insurance is predicted to reach nearly $80 billion by 2032 , according to Precedence Research. This growth is being driven by the increased adoption of AI within insurance companies, enhancing their operational efficiency, risk management, and customer engagement.

Despite widespread integration of AI in the industry today, its full potential is in its infancy. While AI has proven its capabilities in automating and excelling at insurance-specific tasks such as assessing risk, expediting claims and performing complex analysis, many insurers have yet to harness these efficiencies despite the challenges they face in workforce shortages and intensifying competition.

In this article, I’ll explore the potential of combining generative AI (GenAI) with traditional AI as a catalyst for achieving more profound and impactful transformations.

Enhancing Efficiency and Effectiveness

Efficiency and effectiveness drive the insurance industry. Efficiency involves achieving more with fewer resources and less time, at a lower expense. Effectiveness entails making better decisions and driving better outcomes. The integration of generative AI and traditional AI enhances both efficiency and effectiveness.

Traditional AI excels at analyzing vast datasets, including historical policy and claims data, offering a comprehensive evaluation of underwriting and claims risks. Meanwhile, using large language models , GenAI can be trained to perform tasks like summarizing notes, writing emails and providing insightful guidance.

This combination streamlines insurance underwriting and claims processes, enabling insurers to make better decisions about risk, increasing policy pricing accuracy and enhancing claims outcomes.

Empowering the Next Best Action with Generative AI

Generative AI has the potential to significantly reduce insurance claim costs and duration by performing time-consuming tasks and guiding adjusters toward optimal actions. It can analyze a vast amount of data to provide actionable recommendations.

Imagine an insurer handling a worker’s compensation claim for an injured employee. Traditionally, the process would involve reviewing medical records, consulting healthcare providers and manually assessing the worker’s condition to determine the appropriate course of action. This can lead to delays, prolonged worker absence, and higher claims costs.

Leveraging traditional and generative AI, the adjuster inputs data such as medical reports, diagnostic test results, adjusters’ notes and job requirements. Traditional AI algorithms can leverage available data, analyzing past similar cases and monitoring the recovery of an injured worker continuously. Based on this analysis, GenAI can provide appropriate recommendations for the best next actions, like:

Optimized next steps: Suggest an independent medical exam or other steps already demonstrated effective for similar claims.
Modified work assignments: Recommend appropriate modified duty assignments, ensuring a smoother transition back to work.
Rehab plans: Suggest tailored rehabilitation or physical therapy programs, expediting recovery and potentially reducing claim duration.
Cost-cutting strategies: Propose cost-effective measures, such as utilizing in-network medical providers.
Communication recommendations: Offer guidance on the best communication practices between the injured worker and healthcare providers, ensuring a smooth flow of information.

Improving Confidence Through Explainability

A key concern in AI adoption is the concept of “ explainability ” or the system’s ability to explain how it makes decisions. Traditional AI models can seem like “black boxes,” leaving professionals perplexed. GenAI addresses this by providing interactive decision support, explaining results in plain language, and even engaging in conversations. For example, if a medical term appears in a claim, insurers can ask open-ended questions and engage with GenAI to obtain a clear, easy-to-understand explanation, enhancing their confidence by clarifying the factors impacting risk predictions. GenAI helps users comprehend the reasoning behind the model’s conclusions, playing an important role in establishing trust and accountability, essential in the insurance industry.

Challenges Adopting AI in Insurance

The collaboration of traditional and GenAI holds immense promise for insurers. However, there are challenges that must be addressed.

Data Quality: Assuming availability in a usable format, real-world data often contains inaccuracies and inconsistencies, posing a significant challenge in maintaining clean, reliable data for AI models. Even when collaborating with an external vendor, compatibility and preprocessing of data for model consumption remain crucial. Identifying and rectifying issues like missing or inconsistent data is essential.

Addressing Biases in Machine Learning: AI systems learn from data, which, if not representative of the entire population of a covered group, can cause AI models to perpetuate or even amplify particular biases. Imbalanced training data can lead to inaccurate or discriminatory predictions. To mitigate biases, the training data must be diverse and representative of the population. Including a broad range of demographics, backgrounds, and experiences helps prevent the model from learning and perpetuating biases.

Data Quantity and Variability: Insufficient data access is also a challenge. Successful machine learning relies on substantial datasets that capture diverse inputs and outcomes within specific business contexts. For instance, when training an AI model to predict workers’ compensation claims, factors like injury types, treatments, and locations significantly impact outcomes. Relying on a narrow dataset might lead to inaccurate predictions across regions, injury types, or severities.

Cultural Acceptance: Building trust in AI is essential for successful integration and collaboration between employees and AI systems. This includes offering training to familiarize employees with AI – what it is, how it works, how it complements their roles, its benefits, and addressing any misconceptions or fears. Model explainability helps, as does emphasizing how AI is simply another tool to amplify their work, rather than to replace human capabilities. Professionals can leave routine, repetitive tasks to the AI systems, and increasingly focus on more strategic, value-added tasks.

The Future of Generative AI in Insurance

As we look to the future of the insurance industry, it’s clear that integrating generative AI and traditional AI is key to unlocking AI’s full potential.

The combination promises increased efficiency and effectiveness, enhanced decision-making, and optimal utilization of the existing workforce. Traditional AI excels at predicting risk, policy pricing, and projecting claim reserve requirements. GenAI’s natural language capabilities will accelerate insurers’ ability to make sense of large amounts of unstructured data, enabling them to make more informed decisions quickly and accurately.

The synergy between these two AI approaches will help insurers achieve a better return on risk, stay competitive in an increasingly challenging market, and deliver excellent client service.

Stan Smith, founder and CEO of Gradient AI, has been working with AI and technology companies for nearly 30 years. By applying the latest artificial intelligence and machine learning advancements to the data-intensive insurance industry, Stan founded a company that enables insurers to significantly improve their underwriting and claims operations, resulting in enhanced loss ratios and profitability.

This article first appeared on Dataversity .

5 Questions with Sumit Rane, Gradient AI Data Scientist, Group Health

Fri, 08 Mar 2024 23:02:16 GMT

GROUP HEALTH INSIGHTS BLOG | Nov 13, 2023

The Gradient AI Team

Sumit Rane has been a Staff Data Scientist for Gradient AI’s SAIL™ Group Health underwriting solutions since 2021. He has leveraged his extensive experience and subject matter expertise in data science, machine learning (ML), and analytics to develop cutting-edge AI applications which solve unique problems in the healthcare insurance industry.

Sumit has a BS in Electrical Engineering from University of Pune, and a Master’s in Business Analytics (with a specialty in Statistics) from Drexel University, and recently completed a Leadership and Management Certificate from The Wharton School of Executive Education. He lives in Boston.

What is the primary focus of your role as Staff Data Scientist?
My primary responsibility revolves around the development and maintenance of machine learning models tailored for our Group Health Underwriting solution, SAIL™. Additionally, my job involves actively exploring unique methodologies that not only help refine our models but also exhibit potential for broader application across other business lines at Gradient AI , thereby providing technical leadership and strategic guidance in our data science endeavors.
What advice would you give new customers considering optimizing the use of artificial intelligence in their underwriting processes?
Traditionally, most Group Health underwriters have used rule-based methods to evaluate risk, which seems intuitive for decision-making, but can often become unwieldy when there are complexities. These challenges can be overcome by harnessing the power of the AI and machine learning models that we are able to build today.

I think the insurance industry will likely never get to a state of zero uncertainty in the underwriting process, but machine learning can help us get a bit closer. In my experience, pairing ML with an underwriter’s expertise is a definite way to enhance returns on the use of AI.

Think of AI/ML solutions as a belaying system for rock climbers, where the underwriter is the person scaling the mountain, and the AI solution is the belayer holding the rope so that the rock climber can continue climbing while being safeguarded.

I would encourage our new customers to actively engage and establish a feedback loop with our client execs , where they continue to own the philosophy of risk selection, while our AI solutions help them refine the risk selection, increase their confidence in underwriting, and ultimately contribute to improving predictive capabilities.
What does a typical day at Gradient AI look like for you?
Given Gradient’s commitment to the ongoing enhancement of our Group Health underwriting solutions, engaging closely with my fellow Data Scientists, and fostering cross-collaboration with the Engineering and Product Teams on a daily basis is integral to my role.

Typically, the better part of my day encompasses technical tasks aimed at addressing intricate data science challenges; for example, defining problem statements, formalizing experiment design, generating hypotheses, extracting, and analyzing vast amounts of data, and going through rigorous iterative cycles of ML model experimentation to drive optimal performance.

I also dedicate time each day to strategic planning of deliverables, providing recommendations from an analytical perspective, and communicating technical decisions to stakeholders that ensure successful project completion.
What do you like most about your job at Gradient AI?
I highly value the supportive atmosphere at Gradient AI, which fosters a culture of idea-sharing. Moreover, I have consistently found fulfillment in working within small teams here which nurture collaboration and growth.

I also enjoy addressing the questions and challenges that we get from our Group Health customers concerning edge cases - and then providing them with a thorough analysis to explain the rationale behind our models’ predictions. At that time, I also try to identify discernible patterns within those investigations to facilitate necessary enhancements for future iterations of our model.
What do you like to do in your free time?
On weekdays, you'll catch me jamming out on my guitar or keyboard, trying to nail down some cover songs. I’m a lifelong student of music, so I continuously try to expand my repertoire. On weekends, I enjoy watching sports, going out, and doing outdoorsy stuff with my friends, or exploring the city. I also like to kick back and listen to podcasts about all sorts of things, like philosophy and cosmology.

Bonus Question: What’s your favorite musical artist and/or favorite song?
I'm into a variety of music genres, so I've got quite a few favorites. But I'm a huge fan of the artist Steve Wilson and his song 'Arriving Somewhere' -- it's definitely up there on my list!

Thank you, Sumit! Be sure to check back on our blog as we ask 5 questions to different members of our talented staff across Gradient AI, to learn how they support the success of our clients.

Explore the benefits of Gradient AI's Group Health Solutions.

Schedule a Demo →

2024 AI Trends Reshaping Insurance Operations

Mon, 19 Feb 2024 15:49:15 GMT

February 19, 2024

The industry’s strategic focus on AI adoption, coupled with nuanced considerations for data quality and talent retention, is poised to usher in a new era of innovation and operational excellence to the insurance sector in 2024.

By Stan Smith

2024 promises to be an exciting year for artificial intelligence (AI) within the insurance sector, offering new avenues for insurers to implement and capitalize on technological advancements. Last year saw a notable upswing in AI adoption within the insurance industry, and throughout 2023, a growing number of companies integrated AI into their daily operations to enhance efficiency.

Looking ahead, expect a continual focus on AI, with insurers dedicating time, money, and resources to develop and execute strategic initiatives involving AI. The shift in 2024 will include a proactive approach, as insurers leverage AI in building streamlined processes, reducing time constraints, and enhancing overall efficacy. The strategic use of AI is expected to address and overcome various common challenges faced by insurers.

For 2024, several emerging AI trends are shaping the future of the insurance industry, including:

AI Adoption and Investment Accelerates
Across the insurance industry, companies are embracing AI as a strategic priority and increasing their AI investments. AI algorithms can provide accurate and consistent insights, enabling insurers to make better and faster decisions and reduce risks. However, AI is not a replacement for human expertise. Instead, it serves as a virtual digital assistant that can give insurers more confidence in making decisions. By combining AI and human judgment, insurers can achieve a more balanced and nuanced approach to decision making and risk assessment, increasing their trust in the outcomes.

In 2024, there will be an increased focus on AI for underwriting and claims management, where AI can help optimize policy pricing, reduce quote turnaround times, triage claims, and reduce the duration and cost of claims. There will also be an increase in exploration of Generative AI, which can create new and original content or data based on patterns and information it has learned from previous examples. This will allow insurers to automate tasks such as summarizing medical notes, generating routine correspondence, and processing simple applications and claims.
Prioritization of Cloud and Big Data
Cloud and big data are the top priorities for insurers. Companies are starting to recognize the essential role these technologies play in enabling data-driven insights and building resilient operational infrastructures. In 2024, there will be more insurers leveraging cloud and big data to drive innovation and operational efficiency in the insurance industry. As this unfolds, it will be critical to ensure data quality because it directly impacts the accuracy and reliability of AI models.
Accurate and Competitive Policy Pricing
AI’s impact on policy pricing is becoming imperative for enhancing accuracy and customer satisfaction. AI’s ability to analyze vast data sets and external sources to evaluate risk is a game-changer for insurers. It allows them to price policies more accurately based on multiple key risk factors. This capability not only enhances accuracy but also ensures competitiveness and customer-centric pricing structures.
Addressing the Talent Gap
As aging workers retire, and insurers struggle to find new talent to replace them, AI is emerging as a solution capable of capturing and preserving institutional knowledge. By harnessing AI’s capacity for knowledge preservation, companies are making key shifts in their talent management strategies. AI is not only bridging the talent gap, but also redefining how institutional knowledge is used within the insurance sector.

AI’s growing prominence, not only as a technological enabler but as a critical catalytic force, is reshaping the insurance industry. It is becoming an indispensable tool for insurers to enhance their effectiveness and efficiency. The industry’s strategic focus on AI adoption, coupled with nuanced considerations for data quality and talent retention, is poised to usher in a new era of innovation and operational excellence to the insurance sector in 2024.

Stan Smith, Founder and CEO founded Gradient AI to focus on the risk management and insurance industry’s most challenging business problems. He has nearly 30 years of experience growing AI and technology organizations and has held founding or executive-level roles for multiple startup companies. This includes serving as Vice President at MatrixOne, Executive Vice President and General Manager at Agile Software, and CEO and Founder at OpenRatings. Smith also led development of patented technologies including: technology that predicts bankruptcies for small, privately held suppliers; a global database to improve supplier performance for more than 80 million companies; and, technology that combines assessments with performance data to identify opportunities for reducing inefficiencies through lean initiatives.

This article first appeared on Insurance Innovation Reporter .

AI: 4 trends for insurers in 2024

Wed, 07 Feb 2024 19:20:56 GMT

February 7, 2024

By Stan Smith

Artificial intelligence continues to advance in both efficiency and accuracy, with its integration accelerating across various industries. In recent years, the insurance industry has accelerated its use of AI, recognizing its potential to enhance effectiveness and streamline operations. AI provides insurers with accurate and consistent insights, facilitating quicker and more informed decision-making, while also reducing risks. As AI adoption continues to surge, 2024 is poised to see significant strategic shifts in how insurance companies leverage this technology.

To better understand the artificial intelligence landscape for insurance companies in 2024, Gradient AI conducted a survey among more than 100 customers across diverse insurance companies, revealing four AI trends:

1. Increased investments in AI

A staggering 90% of insurers surveyed plan to increase their AI investments, underscoring AI’s role in enhancing operational effectiveness and efficiency. The primary AI focus areas, according to 75% of respondents, are underwriting and claims management, policy pricing optimizations, expedited quote turnaround times, triaging claims, and minimizing both the duration and cost of claims.

An emerging trend in 2024 is the widespread adoption of generative artificial intelligence, which can generate new content or data by using patterns and information acquired from past examples. This is empowering insurers to automate repetitive tasks, including generating medical notes and routine correspondence, as well as processing simple and straightforward applications and claims.

2. Prioritization of cloud and big data

About 40% of insurers identified cloud and big data as their primary focus, aiming to drive innovation and improve operational efficiency. This focus underscores the role of ensuring data quality, given its direct impact on AI model reliability. The management of large volumes of data in cloud environments can be susceptible to errors, necessitating vigilant monitoring and management practices to ensure accuracy and reliability.

3. Enhanced competitive policy pricing

Almost 50% of respondents pointed to the importance of using AI for policy pricing. AI’s adeptness at analyzing big datasets for risk assessments is fundamentally reshaping traditional practices in the industry. Policy pricing is essential in improving accuracy and customer satisfaction for insurance companies. By leveraging AI, insurers are achieving more precise policy pricing by considering multiple key risk factors. These capabilities not only elevate accuracy but also ensure competitiveness, paving the way for the establishment of customer-centric pricing structures.

4. Closing the talent gap

AI’s ability to preserve institutional knowledge is expected to play a key role in bridging the talent gap amid ongoing workforce transitions in the insurance industry. In 2024, this recognition is expected to be catalyst for insurers to embrace AI, with 62% of respondents recognizing AI’s abilities in talent management. As a result, expect to see AI adoption to be included in future strategies for retaining talent.

The industry's commitment to adopting artificial intelligence, along with considerations for data quality and talent retention, will bring increased innovation and efficiency to the insurance landscape in 2024. In the coming year, expect to witness a shift in the role of artificial intelligence as it evolves from solely a technological enabler to a transformative force that reshapes the insurance landscape.

Stan Smith is CEO of Gradient AI. Contact him at stan.smith@innfeedback.com .

This article first appeared on INSURANCE NEWSNET.

Survey Shows that More than 90% of Insurers Plan to Increase AI Investment – Top 4 Trends for Insurers in 2024

Sat, 03 Feb 2024 22:02:48 GMT

February 3, 2024

To glean insights, Gradient AI conducted a survey among 100+ customers across diverse insurance companies, revealing four noteworthy AI trends influencing the future landscape of the insurance sector.

Trend #1- AI Adoption Accelerates

The survey shows that insurers are embracing AI as a strategic priority, with 90% of respondents planning to increase their AI investments. The majority of them (75%) are focusing on AI applications for underwriting and claims management, where AI can help optimize policy pricing, reduce quote turnaround times, triage claims, and reduce the duration and cost of claims.

In addition, about 25% of respondents are exploring the potential of Generative AI, which can create new and original content or data based on patterns and information it has learned from previous examples. This will allow insurers to automate tasks such as summarizing medical notes, generating routine correspondence, and processing simple applications and claims.

Gradient AI’s take: The survey confirms that AI is becoming an essential tool for insurers to enhance their effectiveness and efficiency. AI algorithms can provide accurate and consistent insights, enabling insurers to make better and faster decisions and reduce risks. However, AI is not a replacement for human expertise. Instead, it serves as a virtual digital assistant that can give insurers more confidence in making decisions. By combining AI and human judgment, insurers can achieve a more balanced and nuanced approach to decision making and risk assessment, increasing their confidence and trust in the outcomes.

Trend #2- Cloud and Big Data is the Foundation of Data-Driven Insights

Cloud and big data are the top priorities for nearly 40% of insurers surveyed, who recognize the essential role these technologies play in enabling data-driven insights and building resilient operational infrastructures.

Gradient AI’s take: We share our customers’ vision of leveraging cloud and big data to drive innovation and operational efficiency in the insurance industry. However, we also understand the challenges of ensuring data quality and reliability, which are crucial for the success and effectiveness of AI models.

Trend #3- AI is the Key to Accurate and Competitive Policy Pricing

AI’s impact on policy pricing is recognized by almost 50% of insurers surveyed, who see it as a strategic imperative for enhancing accuracy and customer satisfaction.

Gradient AI’s take: AI’s ability to analyze vast data sets and external sources to evaluate risk is a game-changer for insurers. It allows them to price policies more accurately based on multiple key risk factors. This capability not only enhances accuracy but also ensures competitiveness and customer-centric pricing structures.

Trend #4- The Solution to the Talent Gap: Knowledge Preservation

An overwhelming 62% of respondents strongly or somewhat agree that AI captures institutional knowledge and helps bridge the talent gap as aging workers retire and insurers struggle to find new talent to replace them.

Gradient AI’s Take: The acknowledgment of AI’s role in knowledge preservation marks a key shift in talent management, positioning AI as a vital tool in preserving institutional knowledge and shaping future talent retention strategies within the insurance sector.

The results of this year’s Gradient AI customer survey highlight AI’s growing prominence, not only as a technological enabler but as a critical catalytic force reshaping the insurance industry. The industry’s strategic focus on AI adoption, coupled with nuanced considerations for data quality and talent retention, will bring increased innovation and efficiency to the insurance landscape in 2024.

This article first appeared on insideBIGDATA.

5 Questions with Senior Product Manager, Gradient AI Group Health, Wes Jacobs

Wed, 31 Jan 2024 14:45:01 GMT

GROUP HEALTH INSIGHTS BLOG | Nov 13, 2023

The Gradient AI Team

What does a Senior Product Manager do? And why is Wes so important to Gradient AI’s Group Health underwriting solutions?

Wes Jacobs is the Senior Product Manager for Gradient AI’s SAIL™ Group Health underwriting solution for new business, and is keenly focused on pushing the limits of big data and AI models to help group health insurers, stop loss carriers, MGU’s, and PEO’s provide more accurate underwriting assessments, reduce claims costs, and improve operational efficiencies.

Wes has extensive experience in the industry - leading clients to greater growth and profitability as a principal, business development executive, and sales director over the last 25 years. Wes has a bachelor’s degree in statistics, and an MBA from Brigham Young University. He lives in Salt Lake City.

What is the primary focus of your role as Senior Product Manager in Group Health?
I am primarily responsible for the vision, roadmap, and development of the SAIL™ underwriting solution . I meet with clients, prospects, partners, and industry leaders regularly to define our roadmap and ensure Gradient continually leads the industry; pushing innovation, advanced AI modeling, and reporting features to best address our clients’ underwriting and risk assessment needs.

Most recently, our product team has strengthened our commitment to superior execution in the development of our SAIL™ underwriting solution. As Frank Slootman says in his book, Amp It Up , “the best strategy in the world cannot overcome poor execution”.
What are common misconceptions customers or prospects may have about AI and machine learning?
One of the most common misconceptions is that a machine learning model continually learns and teaches itself. In reality, an ML model only knows what it has seen or been trained for – and that’s where Gradient’s expert engineers and data scientists come in.

How do we teach ML models at Gradient AI? At Gradient, we’ve implemented a systematic clinical hybrid update approach to teach the SAIL™ model to augment predictions and account for new drugs, treatments, conditions, and use patterns.

A great example of this is our recently implemented hybrid update in SAIL™ - an adjustment to account for the recent explosion in the use of GLP-1 receptor agonists such as Ozempic, Wegovy, and Manjouro for weight loss. Without the systematic clinical hybrid updates that we provide, the ML model is out-of-date the minute you release it.
What does a typical day at Gradient look like for you?
I work closely with our engineers and data scientists to communicate objectives, define requirements, coordinate assignments, refine processes, and ensure sufficient resources are available to execute at a high level. Generally, more than half of my day is spent with client support, sales support, product development, and training. Additionally, I work with partners, vendors, and data providers to facilitate product and business development.

I also carve out time for personal development. I am a firm believer that in any job – and particularly in the tech / AI industry – you need to continue to always improve your knowledge, skillset, and general awareness.
What do you like most about your job?
I enjoy interacting with people. I enjoy working together as a team with common goals to deliver a quality solution that makes life better for our clients and allows them to differentiate themselves in the market.

And of course, building client relationships – and understanding their challenges and frustrations – is critical to helping them achieve their goals and objectives.
What do you like to do in your free time?
I have a large family that is growing up and expanding, so I’m always looking for ways to spend time with them, particularly in the outdoors. We love houseboating and wake surfing at Lake Powell, snorkeling and scuba diving in Hawaii, skiing and snowboarding in the mountains, playing disc golf at the park, or just watching a movie together.

Bonus Question: Where is your favorite place to ski and why?
As a 30+ year resident of Utah, we have an annual pass at the “locals focused” Brighton Resort up Big Cottonwood Canyon. 900 inches of snow last year!

Thank you, Wes! And be sure to check back on our blog as we ask 5 questions to different members of our talented staff across Gradient AI, to learn how they support the success of our clients.

Explore the benefits of Gradient AI's Group Health Solutions.

Schedule a Demo →

Traditional AI and Generative AI: Transforming the Insurance Industry

Mon, 29 Jan 2024 14:15:00 GMT

P&C INSIGHTS BLOG | Dec 20, 2023

The Gradient AI Team

Fueled by the power of artificial intelligence that now includes generative AI combined with traditional machine learning, the insurance industry is undergoing a profound shift. This shift goes beyond basic AI functionalities, diving deep into the realm of intelligent guided decision-making.

As more applications leverage traditional and generative AI, these technologies will turbocharge their impact on society and business. The idea of computers mimicking human reasoning and thinking used to seem distant, but today we’re witnessing it become a reality. And this is just the start for AI, akin to the early 1980s when personal computers were first being adopted. Even more transformative and profound changes are ahead.

The Benefits of Generative AI in the Insurance Industry

In the insurance industry, not only can insurers underwrite risk more accurately, provide better customer service, and automate tasks, but with the help of generative AI, they can virtually have a conversation with their organizations’ vast, previously inaccessible knowledge bases. They can now consult decades of accumulated claims data, medical notes, and outcomes to improve decision-making in underwriting and claims management.

Consider an insurer with 20, 30, or 40 years of accumulated claims data having detailed medical and adjuster’s notes, claim outcomes and associated costs. Leveraging traditional and generative AI applications, adjusters can engage with the institution’s entire knowledge base, allowing them to better understand the direction a claim might take and to determine the next best actions to drive better outcomes. Likewise, this combination empowers underwriters to make more informed decisions, assessing the predicted risk of writing a specific policy, and gaining deeper insight into the specific factors driving that risk up or down.

Industry Data Lakes Are Key to AI in Insurance

The effectiveness of AI in insurance hinges on access to extensive, high-quality data. In general, the more data an AI model is exposed to, the more accurate and effective it becomes. Industry data lakes , or databases, enriched with diverse data sets, are crucial for training such models.

Gradient AI maintains an industry data lake with tens of millions of policies and claims observations, enriched with third-party features including economic, health, geographic, and demographic data. Both generative AI large language models and traditional AI machine learning models benefit, gaining extensive knowledge that improves their effectiveness across numerous claims and policies.

AI in Insurance Delivers Real Results

decreased its loss ratio by 13%
reduced quote turnaround times by 80%, and
increased direct written premium per employee by 86%.

On the claims side, one of Gradient AI’s customers reduced workers’ compensation claims costs by about 10%, which translated to more than $300 million in savings for the insurer’s clients.

The AI transformation in insurance represents a fundamental operational shift, moving into an era that goes beyond automation to one where decisions are profoundly informed. As insurers continue to leverage AI’s cognitive advances, the future holds the promise of an industry that operates with unprecedented intelligence, effectiveness, and efficiency, benefiting both insurers and customers alike.

Learn More Today

Read more about generative AI in insurance in this Focus Outlook article , written by Gradient’s CMO, Paul Cataldo.

To learn more about applications for traditional and generative AI in insurance, schedule a 30-minute conversation with us today.

Contributory Data Lakes: A Key Consideration for Insurers in Their AI Initiatives

Tue, 23 Jan 2024 15:39:33 GMT

P&C INSIGHTS BLOG | Dec 20, 2023

The Gradient AI Team

It has been said that data is the new oil. If this is true, the insurance industry is very wealthy indeed! At its core, insurance is a data-driven business. Underwriters and adjusters depend on data from various sources to assess risk and price policies, and to resolve claims.

Artificial intelligence is increasingly integrated into insurance operations with data as the fuel that drives AI-based solutions in underwriting and claims . Models are only as good as the data they are trained on. Higher quality data translates directly into more accurate models that provide better predictions and results.

For example, if you train AI models only on your in-house data, they can make predictions based only on the outcomes your company has seen. On the other hand, when you combine your in-house data with high-quality data from other carriers, AI models become more generalized because they’ve seen a wider range of policies and claims outcomes. They can enable you to operate more effectively across a wider range of opportunities and circumstances.

Why Contributory Data Lakes are Vital to Insurance in the AI Era: a Real-World Example

Insurers have vast underwriting and claims data assets. However, this information is limited to the industries and geographies in which they operate, and more specifically, to their own clients only.

For example, suppose you’re a Workers’ Compensation (WC) carrier who only writes business for construction companies in Arizona. You have extensive data on construction practices, weather, traffic, accidents, and numerous other factors related to the construction industry in Phoenix.

Now imagine you want to expand into Minnesota and provide WC coverage for construction companies there. Because you don’t have any experience providing construction companies with coverage in Minnesota, you will find it difficult to accurately assess risk in this new geography. This challenge is further compounded if you want to expand into other industries, such as providing WC coverage for janitorial companies. You lack the data to understand the risks associated with this business, so similarly it will be difficult to accurately assess and price risk accordingly.

This is where the benefits of a contributory industry data lake or database come into play. Contributory data lakes leverage numerous insurers’ data, creating a collaborative and dynamic repository of information. These contributory platforms rely on the collective input and contributions of multiple users providing a far more robust data set than any individual insurer would otherwise have. Industry data lakes of this magnitude not only help insurance companies assess risk more accurately in the markets in which they already operate, but also enable them to move into new markets, add new products, and benchmark themselves against their peers. These industry data lakes help refine AI models to improve their accuracy.

The Benefits of Gradient AI’s Data Lake

Over the years, Gradient AI has invested significant resources into our industry data lake. Currently, it houses tens of millions of structured and unstructured underwriting and claims records. All of the data is de-identified to ensure anonymity. Beyond the contributory sources of the data lake, we’ve further enhanced it by adding almost 100 third-party data sources, such as:

Medical, prescription, and laboratory data
Weather, crime, distance to EMS, and numerous other demographic data points
Psychographic data

…and much more

We added these additional features that go beyond basic data points because they enable AI models to be more accurate predictors. Of course, ensuring the security of data is paramount and Gradient AI is SOC2 compliant and HITRUST certified.

AI Models are Only as Good as the Data They are Trained On

As carriers look to drive efficiencies in underwriting and claims, AI-powered solutions have become more prevalent to boost underwriting and claims productivity.

Yet not all data is created equally. When evaluating solution providers, it is critical to remember that a model is only as good as the data it was trained on. A vendor can have sophisticated models, but data is the “fuel” that powers these models, and if they don’t have an extensive training dataset, their AI models may not be as accurate as models trained on more robust data sets.

To learn more about Gradient AI’s contributory industry data lake, schedule a 30-minute conversation with us today.

Traditional AI and GenAI: Transforming the Insurance Industry

Tue, 09 Jan 2024 16:01:21 GMT

January 9, 2024

By Paul Cataldo

Fueled by the power of artificial intelligence (AI) that now includes generative AI combined with traditional machine learning, the insurance industry is undergoing a profound shift. This shift goes beyond basic AI functionalities, diving deep into the realm of intelligent guided decision-making.

IBM once described cognitive computing as an advanced system capable of learning at scale, reasoning with purpose and engaging with humans in a natural and meaningful manner. This concept integrates various AI disciplines such as neural networks, machine learning, natural language processing, and contextual awareness, operating in a way that resembles human problem-solving. When this term was first coined, the idea of computers mimicking human reasoning and thinking seemed distant, but today we’re witnessing this become a reality.

As more applications leverage traditional and generative AI, these technologies will turbocharge their impact on society and business. The latest developments are truly astounding; we are witnessing AI advancements that resemble human abilities to read, understand and reason. What is even more exciting is that this is just the start. This moment in AI development is akin to the early 1980s when personal computers were just being adopted, foreshadowing even more transformative and profound changes ahead.

What does the power of generative AI look like? Consider Anthropic’s Claude , a next-generation AI assistant that can read and process the average novel of about 75,000 words in about one minute. With AI systems already delivering this kind of processing power, it isn’t surprising that a recent McKinsey report estimated that the combined impact of generative and traditional AI could unlock and add $17 to $25 trillion of value to the global economy.

In the insurance industry, this AI combination empowers insurers in ways never before possible. Insurers can not only underwrite risk more accurately, provide better customer service, and automate tasks, but now, with the help of generative AI, they can virtually have a conversation with their organizations’ vast, previously inaccessible knowledge bases. They can now consult decades of accumulated claims data, medical notes, and outcomes to improve decision-making in their underwriting and claims management operations.

Consider an insurer with 20, 30, or 40 years of accumulated claims data having detailed medical and adjuster’s notes, claim outcomes and associated costs. By leveraging traditional and generative AI applications, adjusters can engage with the institution’s entire knowledge base. This interaction allows them to better understand the direction a claim might take and to determine the next best actions to drive better outcomes. Likewise, this combination also empowers underwriters to make more informed decisions. Not only can they assess the predicted risk of writing a specific policy, but also gain deeper insight into the specific factors that are driving that policy’s risk up or down.

However, the effectiveness of AI in insurance hinges on access to extensive, high-quality data. In general, the more data an AI model is exposed to, the more accurate and effective it becomes. Industry data lakes, enriched with diverse data sets, are crucial for training AI models comprehensively.

At my company, we maintain an industry data lake with tens of millions of policies and claims observations. We have enriched this information with third-party data features including economic, health, geographic, and demographic information. Both generative AI large language models and traditional AI machine learning models can benefit from these vast data assets. They can learn from numerous instances, gaining extensive knowledge that improves their effectiveness across many different types of claims and policies.

Insurers that embrace these technologies today are poised to gain significant competitive advantages in the years to come. In underwriting, AI has had a demonstrative impact on key operating metrics and profitability. For example, an insurer leveraging AI decreased its loss ratio by 13%, reduced quote turnaround times by 80%, and experienced an 86% increase in direct written premium per employee.

On the claims side of the business, one of our customers reduced workers’ compensation claims costs by about 10%, which translated to more than $300 million in savings for the insurer’s clients.

The AI transformation in insurance isn’t just a technological evolution; it’s a paradigm shift in how the industry operates, and a precursor of what’s to come in 2024 and beyond. The industry is moving into an era that goes beyond automation to one where decisions are profoundly informed. As insurers continue to leverage AI’s cognitive advances, the future holds the promise of an insurance industry that operates with unprecedented intelligence, effectiveness, and efficiency, benefiting both insurers and customers alike.

Paul Cataldo

CMO, Gradient AI

Paul is a senior technology executive with over 20 years of experience, with a focus on leveraging big data to improve business processes and outcomes. His early work transforming a data science services company, DataSage, into a product company leveraging machine learning to build a customer analytics solution for Walmart, Amazon, Outpost, and other leading retailers, resulted in one of the first AI unicorns (exit value over $1 billion).

Paul also led the product marketing and management teams at IntelliVid, a video analytics company (acquired by Tyco) which developed a groundbreaking solution for loss prevention. He later served as vice president of marketing for OATSystems, an MIT spinout that pioneered IoT solutions for retail and manufacturing applications, prior to its acquisition by Checkpoint Systems. His recent work includes developing the AI business strategy for a major insurance carrier, leveraging machine learning to improve IT operations and the end user experience.

Paul holds a BA degree (cum laude) from the University of Massachusetts, and recently completed MIT Sloan School’s Artificial Intelligence: Implications for Business Strategy and Stanford University’s Machine Learning programs.

This article first appeared on Focus Outlook .

Gradient AI Recognized by Built In for Three 2024 Best Workplace Awards

Tue, 09 Jan 2024 15:19:01 GMT

Best Places to Work, Best Midsize Places to Work, & Remote Best Midsize Places to Work

Boston, MA, January 9, 2024 - Gradient AI , a leading enterprise software provider of artificial intelligence (AI) solutions in the insurance industry, today announced it was recognized by Built In's 2024 Best Places to Work Awards for three categories:

This is the fourth year in a row Gradient AI has been awarded by Built In's program, which honors companies whose outstanding cultures show a commitment to their employees. The annual awards include companies of all sizes and are either remote-first employers or companies in the eight largest tech markets across the United States.

“Being acknowledged as a top workplace across three categories speaks volumes about our exceptional team and the culture we've cultivated," said Stan Smith, founder and CEO of Gradient AI. "Our aim is to provide a workplace that nurtures success for our employees. They are the core of our company, and we take pride in fostering a culture where everyone thrives and excels professionally."

Doing the Right Thing, Teamwork, and Creating Waves are Gradient AI’s three core values and are a cornerstone for building the company’s growing market leadership. They encourage employees to always act with integrity, respect colleagues, and to innovate, always challenging the status quo. Gradient AI offers a flexible workplace in which employees have a number of different working models including remote, on-site, and hybrid. The company offers unlimited vacation and eleven paid holidays so employees can relax and recharge. Gradient AI is dedicated to mental and physical wellness, employee engagement, diversity, inclusion, and community outreach.

Built In determines the winners of Best Places to Work based on an algorithm, using company data about compensation, benefits and companywide programming. To reflect the benefits candidates are searching for more frequently on Built In, the program also weighs criteria like remote and flexible work opportunities, programs for DEI and other people-first cultural offerings.

“I’d like to extend our heartfelt congratulations to the 2024 Best Places to Work winners,” said Maria Christopoulos Katris, founder and CEO, Built In.

About Built In

Built In is creating the largest candidate generation platform for technology professionals globally. Monthly, millions of the industry’s most in-demand professionals visit the site from across the world. They rely on our platform to stay ahead of tech trends and news, learn skills to accelerate their careers and find opportunities at companies whose values they share. Built In also serves thousands of innovative companies from startups to the Fortune 500. By putting their stories in front of our uniquely engaged audience, we help them reach otherwise hard-to-hire tech professionals. www.builtin.com.

About Built In’s Best Places to Work

Built In’s annual Best Places to Work program honors companies with the best total rewards packages across the U.S. and in the following tech hubs: Atlanta, Austin, Boston, Chicago, Colorado, Dallas, Houston, Los Angeles, Miami, New York, San Diego, San Francisco, Seattle and Washington DC. Best Places to Work is distinct because its algorithm selects tech companies that build their offerings specifically around what tech professionals value in a workplace. Winners are announced in early January 2024. https://employers.builtin.com/best-places-to-work/

About Gradient AI

Gradient AI is a leading provider of proven artificial intelligence (AI) solutions for the insurance industry. Its solutions improve loss ratios and profitability by predicting underwriting and claim risks with greater accuracy, as well as reducing quote turnaround times and claim expenses through intelligent automation. Unlike other solutions that use a limited claims and underwriting dataset, Gradient's software-as-a-service (SaaS) platform leverages a vast industry data lake comprising tens of millions of policies and claims. It also incorporates numerous other features including economic, health, geographic, and demographic information. Customers include some of the most recognized insurance carriers, MGAs, MGUs, TPAs, risk pools, PEOs, and large self-insured employers across all major lines of insurance. By using Gradient AI's solutions, insurers of all types achieve a better return on risk. To learn more about Gradient AI, please visit: https://www.gradientai.com .

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How to Enhance Workers' Comp Outcomes

Wed, 03 Jan 2024 16:19:16 GMT

January 3, 2024

AI can help in a big way but is not the sole solution. The key lies in integrating people-focused strategies with AI advancements.

By Gia Sawko

KEY TAKEAWAY:

From the insurer’s perspective, AI technology can be used to quickly score claims and provide a detailed explanation of a claim’s severity from its initial report. AI can assign that claim to an adjuster with the appropriate level of experience. It can also triage the case to professionals who have worked with these types of claimants and patients before.
For risk managers, AI provides a secondary, 24-hour set of eyes on claims constantly looking for patterns enabling managers to put resources and ancillary services to work where they can make the most difference. In many cases, these AI tools inform you if, when and how you need to prepare for litigation. Additionally, you can use AI for reserving to help predict the cost of certain claims and determine how that cost affects employer deductibles and deductible programs. These AI risk management capabilities help risk managers curb costs, reduce liability and shorten the claim duration on the employer side.

Workers' compensation claims pose challenges for all involved, especially risk managers who handle multiple claims simultaneously, set expectations and serve as the crucial point of contact among all parties. While they juggle the legal details, state-specific regulations and other data-driven aspects of the workers’ comp lifecycle, it becomes increasingly challenging for risk managers to effectively manage the people side of the equation. This can lead to claimants feeling underserved and underappreciated and more likely to sue the organization.

Fortunately, emerging technologies such as artificial intelligence (AI) are beginning to address this risk management challenge, making it possible to optimize claim processing and triage at scale and in real time. AI can improve claims management while alleviating some of the burdens faced by risk managers. However, it’s essential to recognize that AI is not the sole solution. The key lies in integrating people-focused strategies with AI advancements.

By leveraging AI to streamline certain processes, risk managers can free up valuable time and resources. This newfound bandwidth allows them to prioritize an important aspect of their role: developing a comprehensive return-to-work program. This program, when combined with thoughtful consideration of the injured worker's needs and the requirements of employers, ensures both efficiency and empathy in managing workers' compensation claims.

Challenges Faced by Employers and Risk Managers

In my career, I’ve developed multiple perspectives on workers’ compensation by working as a claims adjuster, supervisor and risk manager. I was lucky to have supportive supervisors and mentors along the way who guided me from the most simplistic claims in the very beginning of my career to tackling the very complex as the years went on. I later shifted to the role of risk manager, which presented me with many new challenges:

Limited File-Level Control: Risk managers often have little to no control over claimant files and how they are adjusted, yet they are the stand-between and the main communicator for both the adjuster and the company.
Regulatory Compliance Requirements: Maintaining strict adherence to rules and regulations is essential to avoid unnecessary claims. If companies have frequent or large claims, their workers’ compensation insurance premiums can increase.
Stakeholder Management: Risk managers must constantly keep up with, listen to and deal with the demands of all important stakeholders, including finance, operations, legal counsel, state workers’ comp boards, employee advocates and unions.
Data Challenges: Handling a large volume of claims can be daunting. A risk manager might request a file review of around 200 files and give the adjuster 30 days to prepare for that task. However, as the process unfolds, these files may change significantly, for better or worse. It’s difficult to manage a large number of claims without accurate, real-time data.

While working through these challenges, I found myself in a tug of war with the multiple demands of my stakeholders, third-party administrator (TPA) and injured workers. Although the job was demanding, it was important to make room for the personal touch, which can make a significant difference when working with injured workers.

The Risk Manager’s Role in Return to Work and Employee Experience

It’s stressful to be an injured worker, especially if your job is changed or limited during your claim. While risk managers can't totally eliminate that stress and uncertainty, there are certain steps they can take and tools they can use to clarify return-to-work expectations, keep claimants engaged and feeling part of the team and build a healthier workers’ comp culture.

For improved return-to-work results, fewer legal issues and a happier workforce, risk managers should follow these three key steps:

Ensure accessible care is available to all claimants. Provide direct care options when your state allows it, but if that’s not possible, diligently provide claimants with as much care information as possible. Advocate for more doctors and clinics in your workers’ residential areas so they have accessible care options.
Develop a triage or advocacy program. Implement programs or employee applications that set clear expectations for claimants and show empathy for their situation in the earliest days of their claim. Armed from the beginning with more information about their particular situation and how it affects their employment, claimants are more likely to feel cared for and remain with the company.
Circle back post-claim. After claims are processed and employees return to work, follow up with them to get their feedback through surveys or apps that provide informal touch points. The focus of this survey is limited to compensable claims. A claim that was denied would not be eligible for the survey. If they are unhappy with the outcome of their case, you’ll be able to identify and address any issues quickly to avoid litigation.

Leveraging AI Technology to Optimize Workers’ Comp Workflows

Most risk managers do not have much time or the tools they need. However, a number of new AI solutions and risk management platforms are filling the gap, enabling risk managers to maneuver the workers’ comp workflow more intelligently.

From the insurer’s perspective, AI technology can be used to support better claim identification, classification, predictive analysis and automated claim assignments. For example, several AI tools now support a first report of injury model, quickly scoring claims and providing a detailed explanation of a claim’s severity from its initial report. From there, AI can segment that claim and assign it to an adjuster with the appropriate level of experience. It can also triage the case to professionals who have worked with these types of claimants and patients before. Using AI gives the insurance company or TPA more data and real-time knowledge from the outset, enabling it to process claims more quickly, with more context and at greater scale.

For risk managers, AI provides a secondary, 24-hour set of eyes on claims constantly looking for patterns enabling managers to put resources and ancillary services to work where they can make the most difference. In many cases, these AI tools inform you if, when and how you need to prepare for litigation , indicating which cases have certain levels of risk or fit patterns from past legal cases. Additionally, you can use AI for reserving to help predict the cost of certain claims and determine how that cost affects employer deductibles and deductible programs. These AI risk management capabilities help risk managers curb costs, reduce liability and shorten the claim duration on the employer side.

Balancing AI Advancements With a Personal Touch

As the insurance industry grows increasingly complex, the demands for better and more accurate claims management have increased. Traditionally, risk managers have not had the resources or time to support the insurer in their work, but with recent AI-powered claims management solutions and the detailed, real-time information they provide, risk managers can reduce the need for frequent file reviews while automating and streamlining data and compliance management responsibilities.

More importantly, AI allows risk managers to devote more time to the human aspect of workers’ compensation. By prioritizing employee care and experience, which is really the most important piece of the risk management puzzle, organizations can mitigate the likelihood of legal action and employee turnover. When employees feel genuinely supported and valued, they aren’t likely to sue their employers or leave the company.

Gia Sawko

Gia Sawko is Gradient AI’s senior product manager of claims.

This article first appeared on Insurance Thought Leadership .

Top 4 Trends for P&C Insurers in 2024: Survey Shows that More than 90% of Insurers Plan to Increase AI Investment

Wed, 20 Dec 2023 17:34:44 GMT

P&C INSIGHTS BLOG | Dec 20, 2023

The Gradient AI Team

What are the key goals and challenges for insurers in 2024? How are they planning to leverage AI to achieve and overcome them?

To find out, Gradient AI conducted a survey among our 100+ customers, who represent a diverse range of insurance companies. The results reveal four emerging AI trends that are shaping the future of the insurance industry.

Trend #1: AI Adoption Accelerates

In addition, about 25% of respondents are exploring the potential of Generative AI , which can create new and original content or data based on patterns and information it has learned from previous examples. This will allow insurers to automate tasks such as summarizing medical notes, generating routine correspondence, and processing simple applications and claims.

Trend #2: Cloud and Big Data is the Foundation of Data-Driven Insights

Trend #3: AI is the Key to Accurate and Competitive Policy Pricing

AI’s impact on policy pricing is recognized by almost 50% of insurers surveyed, who see it as a strategic imperative for enhancing accuracy and customer satisfaction.

Gradient AI’s take: AI's ability to analyze vast data sets and external sources to evaluate risk is a game-changer for insurers. It allows them to price policies more accurately based on multiple key risk factors. This capability not only enhances accuracy but also ensures competitiveness and customer-centric pricing structures.

Trend #4: The Solution to the Insurance Talent Gap: Knowledge Preservation

Gradient AI’s Take: The acknowledgment of AI's role in knowledge preservation marks a key shift in talent management, positioning AI as a vital tool in preserving institutional knowledge and shaping future talent retention strategies within the insurance sector.

Explore the benefits of Gradient AI's P&C or Workers' Comp Solutions.

Schedule a Demo →

Top 4 Trends for Health Insurers in 2024: Survey Shows that More than 90% of Insurers Plan to Increase AI Investment

Wed, 20 Dec 2023 16:01:55 GMT

GROUP HEALTH INSIGHTS BLOG | Dec 20, 2023

The Gradient AI Team

What are the key goals and challenges for insurers in 2024? How are they planning to leverage AI to achieve and overcome them?

Trend #1: AI Adoption Accelerates

In addition, about 25% of respondents are exploring the potential of Generative AI , which can create new and original content or data based on patterns and information it has learned from previous examples. This will allow insurers to automate tasks such as summarizing medical notes, generating routine correspondence, and processing simple applications and claims.

Trend #2: Cloud and Big Data is the Foundation of Data-Driven Insights

Trend #3: AI is the Key to Accurate and Competitive Policy Pricing

AI’s impact on policy pricing is recognized by almost 50% of insurers surveyed, who see it as a strategic imperative for enhancing accuracy and customer satisfaction.

Trend #4: The Solution to the Insurance Talent Gap: Knowledge Preservation

Explore the benefits of Gradient AI's Group Health Solutions.

Schedule a Demo →

Interview: Gia Sawko, Director of Claims, on How AI Helps Make Claims More Efficient and Effective

Wed, 13 Dec 2023 15:27:31 GMT

P&C INSIGHTS BLOG | Nov 16, 2023

The Gradient AI Team

Gradient AI’s Marketing team recently sat down with Gia Sawko, Director of Claims, to discuss how AI is being used by insurers in their P&C claims operations, and the impact it is having. In just a short conversation, Gia shed a lot of light on artificial intelligence and its expanding role in P&C claims management.

QUESTION:

At a high level, can you describe how insurance claims professionals benefit from the use of AI?

In the most recent conversations, when I've been asked the question of the value to the insurer in how claims and AI work together , or where it's going to go, I think about a lot of the opportunities to change the workflow.

Had I been an adjuster 30 years ago, looking at what people are doing today in claims, it doesn't seem all that different a process. Insurance claims themselves have become more sophisticated. And so, for carriers, the job needs to meet the complexity of the space where we're in today. That's really what AI and machine learning can do.

My favorite part about it is that now there's a way to use data for adjusters and supervisors that they didn't have before. If they were taught to do everything by instinct, they learned by repetition. Times have changed, and now, when they interact with the model and the data, especially Gradient AI’s data -- because it's such a deep data lake – the adjuster for the first time has this visual, this tool that they can interact with, to either validate their reserves and their position, or to reconsider not only what a claim is going to cost, but what actions they need to take. It also increases the accuracy of what they're predicting.

AI is using the same best practices as seasoned adjusters. It's built on the same Law of Large Numbers as when an adjuster was trained 30 years ago. We talk a lot about this in claims: ‘if it walks like a duck, swims like a duck, it's probably a duck.’ And that's where that instinct had come in, because you could trust by just the sheer number or volume of claims that you knew how it would end or its outcome. For example, this claim that was unwitnessed on a different location after the person was written up, and they've been at work for 6 weeks. Those are all red flags. That claim was going to cost a lot of money. It had a high probability of litigation, and perhaps a lot of intent to sue or to retain counsel.

These same patterns exist in claims, and the AI model can learn that. It really is like a parallel universe between the traditional approach and AI, but because AI is faster, it just gives the adjuster or their supervisor information more quickly. I don't think it's better, I think it's faster. A very good adjuster is a very good adjuster, because they've learned how to interact with a claimant based on what they see. Even a good adjuster can benefit from using the model, and a new adjuster can definitely benefit from using the model. They just don’t have that kind of depth of experience, and they're not given the same length of time to be exposed to so many claims. So, it's the perfect time for the industry to embrace machine learning and AI, as the next generation of adjusters comes in.

QUESTION:

In terms of the talent gap in the insurance industry, the labor shortage, and all of those challenges, can you just talk a little bit more about that and the potential benefits for new adjusters coming in?

You know, this talent gap has come up in conferences throughout this past year. Everyone is wondering, “What will we do with the insurance industry given the labor challenges?” In the last 6 weeks, I’ve evolved my vision on it.

Adjusters that have been doing claims for a really long time probably have the slowest adoption of using the models just because it's such an innate nature for them. Back to that instinct, they're really good at what they do. Because of that, they feel like AI doesn’t necessarily help so much. It's kind of like telling them something that I already know.

But as these adjusters are starting to retire, then I think about that next generation. I think about attorneys that are in their mid to late twenties or people coming out of school. They're used to being with data. They're used to interacting with that kind of technology. And I feel like the old way of doing it is not an attractive job anymore, because it is so archaic. Now, as the industry evolves, it becomes “Hey! Come, join us! We're the fastest growing tech industry to be a part of!” I think now, we're going to actually flood the market with people who want to come here, use AI, and do all the noble things that adjusters and supervisors have been known for.

QUESTION:

And what about like the benefits of the guardrails or guide posts that AI provides?

Yes, that that's even more of a reason to use AI, because before it was the supervisor that would read your diary and tap you on the shoulder and say, “Hey, when you see this pattern, you already know that something has a high probability of happening.” Now with AI, the neural networks do that for you, they recognize the pattern. AI gives this single thread of a best practice.

And the other thing I always find interesting is that a lot of people could be good at claims, but not everyone can train someone else how to handle claims. They know how they do it, but either they're not articulate, or they just don't have the experience teaching someone else. So, these guardrails that AI provides kind of become that gentle teacher, because it's stopping you and saying, “Hey, consider this file for an IME, or consider this file as costly, so you need to be more in touch with that injured worker.” In this way, it's like really a tool to help guide newer adjusters.

QUESTION:

Do you think we're moving to a time when claims adjusters won't have to touch every claims file every day?

I hope so. That is my dream, you know. I tell this story a lot that as an adjuster, if I had 14 claims on diary, I took them as 1, 2, 3, 4, 5, 6, 7, all the way to 14, and if that fourteenth claim was the worst claim, hopefully, I got to it because chances are I was interrupted by the phone ringing, or some other distraction came up. Now with AI, you can scramble that deck, look at those priority claims and take action. It doesn't mean you'd ignore the claims that are on track, but maybe you wouldn't need to look at them at the same frequency. I sure hope we’re moving towards that.

QUESTION:

When it comes to AI, what’s the value of the data, and what does it mean to have access to a vast data lake? How does that impact insurers?

I'll use Whole Foods as my example. When I was a Managing Director there, I knew my claims and my transactions. How many reserve changes, how many new claims, how many claims that we closed. I knew everything there was to know about Whole Foods’ claims, and I knew everything there was to know about best practices, because I've been doing it for a long time.

But what I didn't know was how well Whole Foods did against other companies. And my claims were very specific to retail and food and hospitality. Where would I go to find any other claims to benchmark? I could call my friends, you know, at my competitors, but that would be weird. I could call other retailers, but they weren't the same business. So I really could never leverage anyone's data but my own. And so now, having a deep data lake, and what I love about the data lake here is that it's anonymized, it's de-identified, the data is completely private and secure when it goes into the data lake, it's stripped of all of identifiable information. We have no idea what claim it is from. Any single claim in the data lake is just one of our claims. So now, you have access to this deep data lake, and you have the opportunity for those comparisons to learn more about how your claims compare.

Also, you can learn about the behavior of claims in other states or regions, or across the country, and in terms of moving into a new market or expanding geographically one of the great things about having a contributory set is if you’re a company with a national footprint, or you’re a carrier that’s writing some policies in New Mexico, and your adjusters are handling multiple claims but they don’t have that many in one particular geography to compare a given claim to, or they want to expand into a new industry – to serve retail clients or restaurants, or construction, or office workers. For me as an adjuster, those meant very different injuries. And I think if a carrier was going to enter into a new market, the ability to see that data or learn about that market would be really helpful.

Gia Sawko has over 30 years of experience in the Property Casualty Claims Industry. As Gradient AI’s Director of Claims, Gia leads product development for its claims management solution leveraging AI and machine learning to drive better outcomes for claim operations. Her passion and focus are empowering adjusters with the benefits of machine learning to reduce claim duration, and cost and improve operational efficiency. Gia’s career started in Personal Auto but also includes Commercial Auto, Commercial Liability, and Workers' Compensation as an adjuster and supervisor. She has also served as Whole Foods Market’s Managing Director of Safety, Claims, and IBNR. Following that, Gia spearheaded solution development as Vice President of Business Impact at Gallagher Bassett leveraging her unique skills to drive business value from technology and artificial intelligence.

Explore the benefits of Gradient AI's P&C or Workers' Comp Solutions.

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Why Use AI in Group Health Underwriting?

Wed, 13 Dec 2023 15:10:16 GMT

GROUP HEALTH INSIGHTS BLOG | Nov 13, 2023

By Jason Oswald

Predictive modeling has always been a part of Group Health underwriting . You are setting premium rates for the future given some set of information. Each company – whether traditional carrier, stop loss carrier, MGA/MGU, or pooled risk group -- has a model they’ve developed and adjusted over time based on prior cases and on their own experience.

Incorporating artificial intelligence into underwriting processes is the industry’s way of adjusting and evolving yet again to enhance the models that exist in underwriting, but to do it in a structured way. As data science and machine learning methods improve, so does our ability to predict risk more accurately and efficiently.

How Are Health Carriers Using AI to Improve Underwriting?

I recently participated in a webinar panel discussion on this topic with Jay Sheehy, Chief Underwriting Officer at Gravie , and Sharon Howe, Senior Director of Underwriting at Providence Health Plan .

We discussed how Gravie and Providence Health Plan are currently using big data and artificial intelligence to improve underwriting, win new business, and optimally manage their book of business. Further, we discussed AI’s potential impact on experience volatility, fraud detection, and human error.

How is AI Impacting Underwriting Teams?

One of the consistent misconceptions we hear is that AI will replace the role of the underwriter. In fact, the truth is quite the contrary. AI enhances the underwriter’s ability to make better, more informed decisions by confirming intuitions built by their own experience while overcoming their own biases. It helps the underwriter identify and focus their attention on good risks and price them aggressively and profitably, while avoiding bad risks to maintain a healthy loss ratio.

My fellow panelists shared the positive impact AI has on both new and more seasoned underwriters alike -- from providing a lift to traditional manual rating to reducing bias from experienced underwriters and filling in gaps and providing additional data for newly hired underwriters.

How to Learn More About AI in Group Health Underwriting

Watch the panel discussion, How Carriers Are Using AI to Improve Underwriting , where my industry colleagues and I exchange views on how to unleash the potential of Group Health analytics and AI to make underwriting more efficient and responsive, and provide a better customer experience throughout the customer lifecycle.

The webinar and panel discussion was hosted by Becker’s Payer Issues and sponsored by Gradient AI .

Jason Oswald is Director of Solution Consulting at Gradient AI, where he leverages his actuarial and underwriting experience in various funding types to help ensure appropriate use of predictive models. Prior to coming to Gradient AI, Jason spent 15 years in health insurance actuarial and underwriting roles.

Explore the benefits of Gradient AI's Group Health Solutions.

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AI Helps Underwriters Discern Risk with Greater Accuracy and Speed, Improving Outcomes and Driving Growth

Thu, 16 Nov 2023 17:31:37 GMT

P&C INSIGHTS BLOG | Nov 16, 2023

The Gradient AI Team

Artificial intelligence (AI) is transforming the insurance landscape, providing significant customer insight, better risk management, and improved underwriting operations for insurers, while enhancing claims management and customer service for insureds.

Download our white paper › Artificial Intelligence Brings Effectiveness and Efficiency to the Underwriting Process to learn even more about how AI is already improving underwriting efficiency, effectiveness, and profitability for leading insurers.

Underwriters Often Lack the Data Necessary to Accurately Assess New Policy Risk: Why?

When processing a policy submission, an underwriter must consider a myriad of factors that influence the risk and price of a policy. And yet, insurers often lack the necessary data and market expertise to accurately assess the risk involved in underwriting a new policy:

Even in markets in which they have deep experience, even the most experienced underwriters have seen only a few thousand policies, with the ability to recall a fraction of them and their final outcomes.
An insurer may be expanding into new geographic markets or lines of business and don’t have the data or experience to accurately measure the risks.
They may have suffered losses in the past as a result of miscalculating risk or failing to identify high-risk policies.
An insurer still relying on traditional actuarial methods may be adversely selected against because their competitors are underwriting armed with powerful data and the latest AI-based analytics.

AI Helps Mitigate Issues with Data So That Underwriters Can Determine Policy Risk More Accurately and Efficiently

AI enables insurers to detect and discern underwriting risk with far greater accuracy and efficiency in four important ways:

Better identify and decline policies where losses will far exceed the premium revenue (or price them commensurately with their risk).

Since AI models can be trained to look at millions of policies and learn that certain attributes increase risk, they can enhance the policy risk assessment of a human underwriter, who may miss the data or not have the data to understand the extent of the risk associated with a particular attribute.

The age and demographics of an employee group may have higher losses in certain types of jobs such as metal work or construction. These attributes alone might or might not increase a group’s risk, but when combined with a particular geographic location and overlayed with the local incidence of opioid abuse, the risk increases beyond the insurer’s risk tolerance.
Better identify and accept policies that represent good risks that may have otherwise been overlooked — or mistakenly classified as higher risk — and price them accordingly.

Consider an insurer expanding into a new market. After 25 years of insuring electrical contractors with work comp and general liability coverage in the Southeast US, the insurer chooses to expand geographically into the upper Midwest. But how does climate and geography impact the risks of operating a similar business in an entirely different place?

AI models may determine that an employer typically categorized as very high risk may be a lower risk due to specific demographic, geographic and other attributes in one region vs. another. Factors that signal lower risk than expected might include, for example: positive customer reviews, local weather, industry certifications, staff safety training, age range of workforce, economic conditions, and type of work. Armed with these insights, the insurer can expand into new territory better prepared to assess the risks to offer or decline coverage accordingly.
Reduce quote turnaround time, enhancing the agent and customer experience, and enabling the insurer to capture more business that may otherwise have been lost to competitors.

AI models can score policies quickly to identify those that are low-risk and can therefore be processed using no-touch or low-touch operations to reduce quote turnaround time. This expedited processing also enables underwriters to have more time to address more complex policies, reducing their turnaround time as well.
Enable underwriters to focus on higher-value work by introducing efficiencies such as straight-through processing, reducing workload and time.

AI can enable automation, which frees up underwriting time. It also can look at more data and summarize it for the underwriter, saving time researching and collecting data needed to assess risk for a more complex policy.

The pressure to process more policy submissions in less time can make it difficult for underwriters to properly assess those that may be riskier, complex, or otherwise difficult to evaluate. AI helps insurers write more policies and still maintain the same standards.

The Ability to Evaluate Complex Relationships at Speed makes AI a Powerful Tool for Underwriters

The more data an algorithm sees, the stronger or more intelligent it gets. It makes predictions and learns from its errors during an initial training phase, adjusting along the way to become a better predictor until it is deemed to achieve a level of accuracy required for a particular application. At that point, the algorithm can be deployed into production.

Once an algorithm is trained in this way, it can make predictions involving very complex outcomes that could be influenced by 80 or more variables/features. What’s more, AI algorithms can make predictions nearly instantaneously, a feat impossible for human underwriters.

Using AI in underwriting can enable:

more accurate pricing
expedited operations
processing more policies with less time and effort.

The ability to evaluate complex relationships at great speed makes AI a powerful tool that has the power to impact insurers’ business performance and financial objectives in ways never before possible.

Learn More About How AI Helps Underwriters

Explore the benefits of Gradient AI's P&C or Workers' Comp Solutions.

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5 Questions with Karen Munroe, Gradient AI Senior Client Executive

Thu, 16 Nov 2023 17:31:16 GMT

GROUP HEALTH INSIGHTS BLOG | Nov 13, 2023

The Gradient AI Team

What does a Client Executive do? And why are they so important to the client experience at Gradient AI?

Gradient AI’s Client Support team leads and manages client implementation projects across all products and provides daily client support in production once clients go live. Client Executives ensure that clients receive the best possible service and support so they can effectively use Gradient AI's Group Health solutions . Senior Client Executive Karen Munroe, who is based in Florida, has worked at Gradient AI since 2022. Her extensive experience in the health insurance industry includes over 24 years at Aetna, where she was most recently a Senior PEO Account Manager.

Recently, we sat down with Karen and asked her five questions (plus a bonus question!) about her work at Gradient AI, and what she likes to do outside of work when she’s not assisting her portfolio of clients.

What is the primary focus of your role as Senior Client Executive?
My primary focus as a Senior Client Executive is to foster strong relationships with our clients, ensuring their satisfaction and success with our AI-driven insurance solutions. I strive to understand their unique needs and challenges, working collaboratively to tailor our offerings to meet their specific objectives. I am all about communication and teamwork and I think those are critical in understanding and meeting our customers’ needs and goals.
What other departments do you interact with the most at Gradient AI?
At Gradient AI, we come together as a team to ensure our clients receive world-class products and services. My role involves acting as a liaison between our clients and the product team, making sure their needs are heard and keeping up with the latest product improvements. We maintain a close partnership with our data engineering team during implementation and system enhancements, and our sales team remains dedicated to cultivating strong client relationships. Additionally, we leverage various support teams as needed to provide in depth assistance.
What is a typical day at Gradient AI look like?
On a typical day, you'll find me working closely with clients on submissions and ensuring we are aligned on processes and goals. I also dig into our product results, communicate with team members on key issues, and keep project statuses up to date. It's all about keeping things running smoothly and giving our clients top-notch service.
What do you like most about your job?
One of the aspects I truly appreciate about my role is the daily diversity it offers. Working with a range of clients utilizing our different products keeps things dynamic and interesting. Each client and their unique needs present a new challenge, which I find rewarding. I am fortunate to work with a client that is very engaged with one of our products and we discuss suggestions for enhancements. In turn, I team up with our product and engineering team to implement these features – not just for that customer but for the benefit of all our clients.

In addition, being part of a cutting-edge tech company brings an exciting element of growth; there is opportunity to grow in our roles and to make meaningful contributions to the company.
What do you like to do in your free time?
I balance my time between good books, exercise, goofing around with my adorable Goldens, SCUBA diving, spending time with my family, and enjoying the Florida sunshine. I'm also trying my hand at golf – it's a work in progress, but it's all about having fun!

Bonus Question: Pancakes or waffles?
Pancakes, especially banana pancakes!

Stay tuned to our blog as we ask 5 questions to different members of our talented staff across Gradient AI.

Explore the benefits of Gradient AI's Group Health Solutions.

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Prodigy adopts Gradient AI’s underwriting solution to enhance medical stop loss insurance offering

Wed, 15 Nov 2023 15:35:56 GMT

November 15, 2023

Prodigy Health Insurance Services is set to leverage Gradient AI ‘s underwriting solution to bolster its medical stop loss insurance offerings.

The collaboration intends to utilise Gradient AI’s SAIL solution for group health underwriting, enabling quoting for new business and aiding in transitioning clients to more cost-effective self-insured plans.

The move will also bolster Prodigy’s offerings, employing data analytics to drive proactive healthcare management and cost reduction strategies.

SAIL stands out due to its exceptional capability to utilise prescription, medical, and laboratory data extensively for predictive insights. It remains unparalleled in the group medical insurance sector as the sole solution providing such comprehensive data from a single source.

Prodigy Health Insurance Services is a managing general underwriter (MGU) specializing in medical stop loss insurance. Their Integrated Health Solutions cater to employers, providing fixed monthly costs for administration, claims payments, and stop-loss insurance.

John Youngs, CEO of Prodigy, said, “We chose Gradient AI because it provides the most accurate assessment of risk and healthcare utilisation, leading to more effective healthcare risk management and predictable insurance plan costs. SAIL’s analytics enable us to help our clients be more proactive about their employees’ healthcare, driving better patient outcomes while controlling costs.

“Its high-quality medical data and advanced analytics have been a game changer, allowing us to transition fully insured businesses to more effective self-insured plans while enhancing healthcare outcomes,” he added.

Stan Smith, CEO of Gradient AI, said, “We are thrilled to collaborate with Prodigy to enhance their medical stop loss insurance business. Our SAIL solution empowers Prodigy to optimise their offerings, enabling clients to transition to self-insurance while maintaining a strong focus on healthcare quality and cost-effectiveness.”

This article first appeared on FINTECH GLOBAL .

Prodigy Adopts Gradient AI’s SAIL for Enhanced Medical Stop Loss Insurance Risk Management

Tue, 14 Nov 2023 15:14:13 GMT

Leading MGU Harnesses AI, Advanced Analytics, Health and Medical Data to Offer Innovative Health Insurance Plans

Boston, MA November 14, 2023 - Gradient AI , a leading enterprise software provider of artificial intelligence (AI) solutions in the insurance industry, today announced that Prodigy Health Insurance Services , a managing general underwriter (MGU) specializing in medical stop loss insurance, has chosen Gradient AI’s SAIL ™ solution to optimize its self-funded insurance plans.

Prodigy is using Gradient AI’s SAIL group health underwriting solution to quote new business and transition clients from fully insured to more cost-effective self-insured plans. The company’s Integrated Health Solutions offers employers fixed monthly costs for administration, claims payments, and stop-loss insurance. Prodigy uses SAIL’s analytics to help employers be more proactive in improving healthcare outcomes while reducing healthcare costs.

Gradient AI’s rich medical data and advanced analytics enable Prodigy to offer innovative self-funded plans and to partner with employers to offer affordable high-quality healthcare to their employees. SAIL is unique in its ability to leverage prescription, medical, and lab data at scale to inform predictions, and is the only solution in the group medical insurance market that can offer such extensive data from a single source.

“We chose Gradient AI because it provides the most accurate assessment of risk and healthcare utilization, leading to more effective healthcare risk management and predictable insurance plan costs,” said John Youngs, CEO of Prodigy. “SAIL's analytics enable us to help our clients be more proactive about their employees' healthcare, driving better patient outcomes while controlling costs. Its high-quality medical data and advanced analytics have been a game changer, allowing us to transition fully insured businesses to more effective self-insured plans while enhancing healthcare outcomes."

Using SAIL's analytics, Prodigy’s solutions focus on delivering proactive employee healthcare management and cost reduction. This approach benefits employees and employers, and aligns with Prodigy's mission of ensuring quality care and outcomes.

Stan Smith, CEO of Gradient AI, said, "We are thrilled to collaborate with Prodigy to enhance their medical stop loss insurance business. Our SAIL solution empowers Prodigy to optimize their offerings, enabling clients to transition to self-insurance while maintaining a strong focus on healthcare quality and cost-effectiveness."

About Gradient AI

Gradient AI is a leading provider of proven artificial intelligence (AI) solutions for the insurance industry. Its solutions improve loss ratios and profitability by predicting underwriting and claim risks with greater accuracy, as well as reducing quote turnaround times and claim expenses through intelligent automation. Unlike other solutions that use a limited claims and underwriting dataset, Gradient's software-as-a-service (SaaS) platform leverages a vast industry data lake comprising tens of millions of policies and claims. It also incorporates numerous other features including economic, health, geographic, and demographic information. Customers include some of the most recognized insurance carriers, MGAs, MGUs, TPAs, risk pools, PEOs, and large self-insured employers across all major lines of insurance. By using Gradient AI's solutions, insurers of all types achieve a better return on risk. To learn more about Gradient AI, please visit: https://www.gradientai.com .

Media Contact

Elyse Familant

Results PR

978-376-5446

elysef@resultspr.net

Gradient AI announces new partnership

Wed, 01 Nov 2023 22:26:01 GMT

November 1, 2023

PEO selects insurtech to streamline its health insurance underwriting

By Ryan Smith

Insurance software provider Gradient AI has announced that Questco, a professional employer organization (PEO) that specializes in outsourced HR services, has selected Gradient AI’s SAIL product to streamline its underwriting process and expedite quote turnaround time.

Questco focuses on small and mid-sized businesses. It offers a range of services, including payroll, health insurance, HR services, workers’ compensation and more, Gradient AI said.

In an effort to enhance its health insurance underwriting process, Questco selected SAIL – Gradient AI’s group medical underwriting product – due to its AI analytics capabilities and rich source of medical, lab and prescription data.

“Innovation has been a key ingredient in fueling our growth,” said Laura Platero, vice president of growth acceleration at Questco. “As an early adopter of SAIL, we leveraged its high-quality medical industry data lake and advanced AI predictive analytics to evaluate group health risk at a deeper level. This has allowed us to quickly turn around highly accurate quotes with the confidence that we have a comprehensive understanding of the group’s risk. We were seeking a solution that could deliver competitive, accurate, and predictably stable pricing, and we certainly found it with Gradient AI.”

“AI is transforming the way PEO’s underwrite health policies, and Questco is at the forefront, driving innovation and value for its employer customers,” said Stan Smith, founder and CEO of Gradient AI. “By leveraging Gradient AI-powered products, Questco can offer competitive solutions on an entirely new level. We look forward to supporting them as they redefine the landscape of employee benefits for PEOs.”

In August, Gradient AI announced an expanded relationship with workers’ compensation insurer MEMIC. In July, it announced a collaboration with ATS Underwriting .

This article first appeared on Insurance Business .

Questco Partners with Gradient AI to Streamline Health Insurance Underwriting, Fueling Expansion

Tue, 31 Oct 2023 13:08:16 GMT

Solution Accelerates Quote Turnaround Time, Improves Underwriting Accuracy, Enhances Member Experience

Boston, MA, October 31, 2023 Gradient AI, a leading enterprise software provider of artificial intelligence (AI) solutions in the insurance industry, today announced that Questco , a professional employer organization (PEO) specializing in outsourced HR solutions, is leveraging Gradient AI’s SAIL ™ solution to streamline the underwriting process, expedite quote turnaround time, and improve the customer experience.

Dedicated to serving the small to mid-sized businesses (SMBs) market, Questco offers a range of cost-effective, high-value benefit packages, including payroll, health insurance, HR services, workers’ compensation, and more, for its member companies. Committed to continuous innovation, Questco was looking to enhance its health insurance underwriting process to better serve its customers as its business grew. Recognizing the importance of competitive and accurate health policy quotes to maintain customer satisfaction and loyalty, the company sought an AI solution to expedite quote turnaround times and improve pricing accuracy.

After evaluating competitive solutions, Questco selected SAIL, Gradient AI’s group medical underwriting solution due to its advanced AI analytics capabilities and rich data source of medical, prescription, and lab data.

“Innovation has been a key ingredient in fueling our growth. As an early adopter of SAIL, we leveraged its high-quality medical industry data lake and advanced AI predictive analytics to evaluate group health risk at a deeper level,” said Laura Platero, vice president of growth acceleration, Questco. “This has allowed us to quickly turn around highly accurate quotes with the confidence that we have a comprehensive understanding of a group’s risk. We were seeking a solution that could deliver competitive, accurate, and predictably stable pricing, and we certainly found it with Gradient AI.”

Stan Smith, founder and CEO of Gradient AI said, “AI is transforming the way PEOs underwrite health policies, and Questco is at the forefront, driving innovation and value for its employer customers. By leveraging Gradient AI-powered products, Questco can offer competitive solutions on an entirely new level. We look forward to supporting them as they help redefine the landscape of employee benefits for PEOs.”

About Gradient AI

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Elyse Familant

Results PR

978-376-5446

elysef@resultspr.net

What is data profiling and is it reshaping insurance?

Mon, 23 Oct 2023 13:32:26 GMT

October 20, 2023

By Heeren Pathak

In the data-intensive insurance landscape, significant strides in Machine Learning (ML) and data profiling are driving progress. These technologies enable the anticipation of policy losses and the projection of claim trajectories, offering previously unattainable insights. Unlike conventional approaches, these advanced tools possess the capacity to consider numerous inputs simultaneously, enabling underwriters and adjusters to make informed decisions, leading to improved outcomes for insurance companies and policyholders alike.

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CLICK HERE to read the full article.

Heeren Pathak
Chief Technology Officer

This article first appeared on Digital Insurance .

Enterprise Viewpoint Ranks Gradient AI as Top 20 Innovative InsurTech Solution Provider

Thu, 28 Sep 2023 13:07:55 GMT

Recognized for its Innovative AI Solutions for Insurance Underwriting and Claims Management

Boston, MA September 28, 2023 — Gradient AI, a leading enterprise software provider of artificial intelligence (AI) solutions in the insurance industry, today announced it has been recognized by Enterprise Viewpoint , a leading business and technology magazine, as one of the Top 20 Innovative InsurTech Solution Providers in 2023.

Gradient AI helps insurers and self-insured organizations improve profitability by leveraging AI and its industry data lake to deliver highly accurate predictions of underwriting and claim risks. Its solutions improve underwriting operations by substantially reducing quote turnaround time and increasing pricing accuracy. On the claims side, its solutions reduce claim duration and costs with AI-powered proactive claims management. By harnessing data from tens of millions of policies and claims, the company's platform increases insurers' efficiency and effectiveness ultimately leading to better decision-making.

Enterprise Viewpoint based its selection process for the “Top 20 Innovative InsurTech Solution Providers” on a combination of factors, including the company’s technology, innovation, market potential, and impact. A panel of industry experts and leaders who closely watch InsurTech innovations and trends evaluated and curated the selected companies.

“Our experts chose Gradient AI as a Top 20 InsurTech Innovator based on its exceptional contributions, cutting-edge technologies, proven track record, and commitment to driving technological excellence,” said Edward John, managing editor, Enterprise Viewpoint. “The company is a pioneer in defining the way insurance providers leverage AI and its solutions are transforming the way insurers manage both their underwriting and claims management operations.”

“We are honored to be acknowledged as one of the leading InsurTech Solution Providers in 2023 by Enterprise Viewpoint,” said Stan Smith, CEO and founder, Gradient AI. “It’s gratifying to be recognized for delivering our advanced AI capabilities which help insurers better assess risk, improve their loss ratios and profitability through streamlined claims management, and provide more personalized and competitive products and services to better address their clients’ needs.”

Learn more about the Top 20 Innovative InsurTech Solution Providers .

About Enterprise Viewpoint

Enterprise Viewpoint is a business-oriented website that provides news, insights, and analysis on various industries, such as technology, healthcare, finance, and marketing. The website features a variety of articles, including industry trends, product reviews, and interviews with business leaders. For more information, news, and perspectives from Enterprise Viewpoint, please visit their website at https://enterpriseviewpoint.com/

About Gradient AI

Gradient AI is a leading provider of proven artificial intelligence (AI) solutions for the insurance industry. Its solutions improve loss ratios and profitability by predicting underwriting and claim risks with greater accuracy, as well as reducing quote turnaround times and claim expenses through intelligent automation. Unlike other solutions that use a limited claims and underwriting dataset, Gradient's software-as-a-service (SaaS) platform leverages a vast industry data lake comprising tens of millions of policies and claims. It also incorporates numerous other features including economic, health, geographic, and demographic information. Customers include some of the most recognized insurance carriers, MGAs, MGUs, TPAs, risk pools, PEOs, and large self-insured employers across all major lines of insurance. By using Gradient AI's solutions, insurers of all types achieve a better return on risk. To learn more about Gradient AI, please visit: https://www.gradientai.com.

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Elyse Familant

Results PR

978-376-5446

elysef@resultspr.net

The role of artificial intelligence in the modern insurance landscape

Wed, 27 Sep 2023 14:45:02 GMT

September 27, 2023

"AI can understand jurisdictions but misses details not found in data"

By Sammie Eastwood

Sammie Eastwood investigates the current push towards integration of artificial intelligence (AI) tools within the insurance industry, the implications of this, and what it means for both employees and customers.

There has been a growing demand for AI tools within the insurance industry, driven by its ability to increase efficiency, support underwriting decisions, improve customer service, and automate processes for faster claims. Since the mainstream release of AI tools, such as ChatGPT or Stable Diffusion, the business world has been abuzz with speculation about this emerging technology trend. While some argue that AI is necessary for businesses to stay competitive, others see it as little more than a gimmick. So, is there really an urgent need for this new technology, or is the insurance se...

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CLICK HERE to read the full article, including insights from Gradient AI's CMO Paul Cataldo and Director of Claims Gia Sawko.

This article first appeared on COVER .

AI reduces costs for most expensive types of workers' comp claims

Tue, 26 Sep 2023 21:35:52 GMT

September 26, 2023

By Steve Hallo

Review how AI can tackle the three reasons why injured workers seek legal representation.

Artificial intelligence can help reduce the legal involvement in lost-time workers’ comp claims by as much as 15%, which in turn can reduce costs for these claims by as much as 5%, according to a study of more than 200,000 claims by Gradient AI .

Those savings equate to about $3.5 million, annually, based on the study’s insurers averaging $70 million in lost-time claims. On a per-claim basis, avoiding a lawyer can save $38,000, Gradient reported.

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Become a Free PropertyCasualty360 Digital Reader HERE .

This article first appeared on ALM PropertyCasualty360 .

Research Study: The Impact of AI on Workers’ Comp Lost-Time Claims Legal Involvement

Sat, 23 Sep 2023 17:46:57 GMT

P&C INSIGHTS BLOG | Feb 26, 2024

The Gradient AI Team

Abstract

Gradient AI engaged in a research study of over 200,000 workers’ compensation lost time claims gathered from more than 60 insurance carriers over a 10-year period. The study found that integrating artificial intelligence (AI) models in Workers’ Comp claims management can reduce legal involvement in lost-time claims – the most expensive Work Comp claims – by 15%. This reduction translates into a 5% savings in claim costs, equating to an estimated savings of $3.5 million per year, based on the insurers in the study managing an average of $70 million in lost-time claims.

Legal involvement is a major cost driver in casualty claims, particularly in the context of lost-time claims. These are cases where an injury is severe enough to require the injured employee to remain out of work for an extended period of time.

Study Methodology and Goals

To better understand the efficacy of AI models trained on industry data lakes, Gradient AI’s data scientists conducted a comprehensive study on workers’ compensation insurance claims. They analyzed over 200,000 lost-time workers' compensation claims, collected from a diverse pool of more than 60 insurance carriers over a 10-year period. Within this dataset, half of the 200,000 claims underwent assessment prior to the integration of AI, while the remaining half were evaluated after AI implementation.

The study’s primary goal was to better understand AI’s potential to deliver value and shape critical decisions around workers’ compensation insurance claims – specifically in management of lost-time claims.

Key Findings

1) 15% Reduction in Legal Involvement:

Researchers found that lost time workers' comp claims involving lawyers cost 3x more than claims without legal involvement and lasted nearly 2x as long.

The study revealed that insurers leveraging AI effectively reduced legal involvement by 15% because AI models were able to assess claim complexities, predict the likelihood of legal involvement, and provide early warnings to claims adjusters.

2) 5% Reduction in Lost-Time Claims Costs:

AI's proactive identification of potential legal engagements resulted in a notable 5% reduction in lost-time claims costs, equivalent to an annual $3.5 million based on the study’s insurers averaging $70 million in lost-time claims. This savings was achieved by providing adjusters with early alerts regarding injury severity and changes in claims status. Early alerts enabled timely actions such as additional attention and outreach by the claims manager and proactive steps to arrange for additional medical treatment.

3) Mitigating the 3 Primary Reasons for Legal Representation:

Three key factors drive claimants to seek legal representation, and this study demonstrated that AI-based models provide early warnings enabling insurers to mitigate these factors:

Erosion of Trust: Prolonged open claims can erode trust between claimants and insurance adjusters over time. AI mitigated this by expediting the process, reducing the need for claimants to seek legal assistance.
Fear of the Unknown: Claimants often seek legal counsel as a safety net when facing severe injuries or doubts about recovery. AI provided insurers with the ability to proactively address these concerns, thus avoiding legal escalation.
Intent to Litigate: Some claimants are determined to pursue legal action. AI empowered insurers to intervene early, potentially averting costly legal engagement.

Conclusion

The research demonstrated that early warnings, based on AI models trained on an extensive industry data lake of workers' compensation policies and claims, enable insurers to proactively manage workers’ compensation lost time claims much more efficiently and effectively. This approach results in faster resolution, reduced legal involvement, and substantial cost savings.

The research also reaffirms that AI is a valuable tool in managing these claims, especially in avoiding costly legal fees and time-consuming litigation. The results not only show substantial cost reductions but also highlight AI's potential to streamline the claims process, benefiting injured workers, employers, and insurers.

Gradient AI publicly announced the results of this study on Sept. 20, 2023, via press release .

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AI claims management can help reduce legal involvement in comp: Study

Thu, 21 Sep 2023 18:16:40 GMT

September 21, 2023

By Jon Campisi

Workers compensation claims management that utilizes artificial intelligence can help reduce the need for legal involvement in lost-time claims, according to findings from an AI software provider.

Gradient AI, a Boston-based company offering AI services for the insurance industry, said it analyzed more than 200,000 lost-time comp claims from more than 60 insurers during a 10-year period and found there to be a 15% reduction in legal involvement in lost-time claims using AI.

The study also showed a 5% reduction in lost-time claims costs, which researchers said can equate to $3.5 million annually.

The study attributes the savings to providing insurance adjusters with early alerts regarding injury severity and claims status changes.

The use of AI models enabled insurers to manage comp claims more efficiently and effectively, resulting in reduced legal involvement and cost savings, the study said.

Researchers said AI helped reduce attorney involvement because models could assess claim complexities, predict the likelihood of legal involvement and provide early warnings to claims adjusters.

This article first appeared on Business Insurance .

Research Study: The Impact of AI on Workers’ Comp Lost-Time Claims Legal Involvement

Wed, 20 Sep 2023 19:56:20 GMT

Abstract

Study Methodology and Goals

Key Findings

1) 15% Reduction in Legal Involvement:

Researchers found that lost time workers' comp claims involving lawyers cost 3x more than claims without legal involvement and lasted nearly 2x as long.

2) 5% Reduction in Lost-Time Claims Costs:

3) Mitigating the 3 Primary Reasons for Legal Representation:

Three key factors drive claimants to seek legal representation, and this study demonstrated that AI-based models provide early warnings enabling insurers to mitigate these factors:

Erosion of Trust: Prolonged open claims can erode trust between claimants and insurance adjusters over time. AI mitigated this by expediting the process, reducing the need for claimants to seek legal assistance.
Fear of the Unknown: Claimants often seek legal counsel as a safety net when facing severe injuries or doubts about recovery. AI provided insurers with the ability to proactively address these concerns, thus avoiding legal escalation.
Intent to Litigate: Some claimants are determined to pursue legal action. AI empowered insurers to intervene early, potentially averting costly legal engagement.

Conclusion

Media inquiries and study reprint requests may be directed to Elyse Familant .

Gradient AI publicly announced the results of this study on Sept. 20, 2023, via press release .

Insurers wishing to learn more about how AI can help the Workers’ Comp claims process can set up a meeting with a Gradient AI subject matter expert.

Gradient AI Study: AI Reduces Legal Involvement in Workers’ Compensation Lost-Time Claims by 15%, Saving Insurers Millions

Wed, 20 Sep 2023 13:05:50 GMT

New Research of More than 200,000 Claims from 60 Insurers Highlights AI's Role in Significantly Reducing Attorney Involvement

LAS VEGAS, NV — National Workers’ Compensation and Disability Conference September 20, 2023 — Gradient AI, a leading enterprise software provider of artificial intelligence (AI) solutions in the insurance industry, today announced the results of a comprehensive research study showing that AI-enabled workers’ compensation claims management reduced legal involvement for lost-time claims by 15%. This reduction translates into a 5% savings in lost-time claim costs, equating to an estimated annual savings of $3.5 million based on the study’s insurers managing an average of $70 million in lost-time claims.

"Our goal was to better understand AI’s potential to deliver value and shape critical decisions around workers’ compensation claims,” said Stan Smith, CEO and founder of Gradient AI. “This study reaffirms that AI is a valuable tool in managing these claims, especially in avoiding costly legal fees and time-consuming litigation. The results not only show substantial cost reductions but also highlight AI's potential to streamline the claims process, benefiting injured workers, employers, and insurers. It’s a win-win-win, making the results of the study particularly gratifying."

To better understand the efficacy of AI models trained on industry data lakes, Gradient AI conducted a comprehensive study on workers’ compensation claims. This research encompassed an analysis of over 200,000 lost-time workers' compensation claims, collected from a diverse pool of more than 60 insurance carriers over a 10-year period. Within this dataset, half of the 200,000 claims underwent assessment prior to the integration of AI, while the remaining half were evaluated after AI implementation.

Key Findings

15% Reduction in Legal Involvement: Gradient AI's researchers found that lost-time workers' comp claims involving lawyers cost 3x more than claims without legal involvement and lasted nearly 2x as long. The study revealed that insurers leveraging AI effectively reduced legal involvement by 15% because AI models were able to assess claim complexities, predict the likelihood of legal involvement, and provide early warnings to claims adjusters.

5% Reduction in Lost-Time Claims Costs: AI's proactive identification of potential legal engagements resulted in a notable 5% reduction in lost-time claims costs, equivalent to an annual $3.5 million based on the study’s insurers averaging $70 million in lost-time claims. This savings was achieved by providing adjusters with early alerts regarding injury severity and changes in claims status. Early alerts enabled timely actions such as additional attention and outreach by the claims manager and proactive steps to arrange for additional medical treatment.

Mitigated the Three Primary Reasons for Legal Representation: Three key factors drive claimants to seek legal representation:

Erosion of Trust: Prolonged open claims can erode trust between claimants and insurance adjusters over time. AI mitigated this by expediting the process, reducing the need for claimants to seek legal assistance.
Fear of the Unknown: Claimants often seek legal counsel as a safety net when facing severe injuries or doubts about recovery. AI provided insurers with the ability to proactively address these concerns, thus avoiding legal escalation.
Intent to Litigate: Some claimants are determined to pursue legal action. AI empowered insurers to intervene early, potentially averting costly legal engagement.

Gradient AI's study demonstrated that early warnings, based on AI models trained on an extensive industry data lake of workers' compensation policies and claims, enable insurers to proactively manage claims much more efficiently and effectively. This approach results in faster resolution, reduced legal involvement, and substantial cost savings.

“This research unveils how AI insights empower insurers to take proactive measures,” said Jeff Snider, GM of Property & Casualty, Gradient AI. “As an attorney, I recognize the importance of minimizing the cost of legal involvement. This study demonstrates how AI’s predictions provide adjusters with an early warning, enabling them to significantly mitigate legal involvement."

Full details of the study are available on Gradient AI’s website .

About Gradient AI

Gradient AI is a leading provider of proven artificial intelligence (AI) solutions for the insurance industry. Its solutions improve loss ratios and profitability by predicting underwriting and claim risks with greater accuracy, as well as reducing quote turnaround times and claim expenses through intelligent automation. Unlike other solutions that use a limited claims and underwriting dataset, Gradient's software-as-a-service (SaaS) platform leverages a vast industry data lake comprising tens of millions of policies and claims. It also incorporates numerous other features including economic, health, geographic, and demographic information. Customers include some of the most recognized insurance carriers, MGAs, MGUs, TPAs, risk pools, PEOs, and large self-insured employers across all major lines of insurance. By using Gradient AI's solutions, insurers of all types achieve a better return on risk. To learn more about Gradient AI, please visit: https://www.gradientai.com.

Media Contacts

Elyse Familant

Results PR

978-376-5446

elysef@resultspr.net

Gradient AI deepens partnership with workers' comp insurer

Tue, 22 Aug 2023 18:46:47 GMT

August 22, 2023

Software will allow insurer to enhance its claims management outcomes

By Steven Byerley

Enterprise software provider Gradient AI has announced an expanded relationship with workers’ compensation insurer MEMIC. This announcement was made during the WCI 2023 Conference, marking a significant step forward in their collaboration.

MEMIC has already been utilizing Gradient AI's underwriting solution to improve risk prediction and capture institutional knowledge. Now, they have decided to leverage Gradient AI's capabilities further to enhance their claims management outcomes. This expanded agreement will see MEMIC utilizing Gradient AI's ClaimsBenchmarking and Total Incurred Prediction solutions.

The ClaimsBenchmarking solution allows MEMIC to identify trends and outliers within their claims data, enabling them to refine their decision-making process and improve reserve management. By comparing their claims' reserves to industry norms, MEMIC's claims managers can determine if a reserve is adequate or requires adjustment.

The Total Incurred Prediction solution allows MEMIC to predict the future cost of each case, enabling claims managers to set reserves within a certain range based on these predictions. This approach provides a more accurate and consistent method for managing reserves, ensuring that future predictions align with predetermined parameters, Gradient AI said.

With approximately 11,000 open claims at any given time, MEMIC is constantly seeking strategies to enhance the efficiency and accuracy of its claims management operations. Recognizing the value of artificial intelligence and machine learning, MEMIC chose Gradient AI's Benchmarking and Total Incurred Prediction solutions to optimize claim outcomes, reduce costs, and improve reserve accuracy.

“Our claim team is excellent but Gradient AI takes our game to a new level,” said Matt Harmon, senior vice president of claims at MEMIC. “The benchmarking tool compares individual claims to similar industry claims. It highlights those claims deviating from norms, enabling us to decide if reserve levels are adequate. Adequate reserves create greater accuracy when calculating experience modification factors, and in turn, better serve the interest of the policyholder and MEMIC alike.”

“MEMIC's visionary approach to unleashing the potential of data within its workers’ comp claim operations is commendable," said Stan Smith, founder and CEO of Gradient AI. "Through strategic benchmarking against industry-wide data, and leveraging powerful predictive insights, the company is poised to elevate the precision of its case reserves, amplify claims management effectiveness, and enhance its ability to grow profitably. We are proud to provide industry-leading underwriting and claims management solutions and thrilled to continue to support MEMIC on its journey toward greater excellence.”

Earlier this month, Gradient AI announced a partnership with American Trucking and Transportation Insurance Company. In July, it announced a collaboration with ATS Underwriting .

This article first appeared on Insurance Business .

MEMIC opts Gradient AI tools to enhance claims management operations

Tue, 22 Aug 2023 18:46:45 GMT

August 22, 2023

Gradient AI’s platforms will allow MEMIC to detect trends and outliers and enhance decision making.

Insurance company MEMIC has teamed up with technology company Gradient AI to bolster its workers’ compensation claims management outcomes.

Under the latest agreement, Gradient AI’s Claims Benchmarking and Total Incurred Prediction solutions will be used by MEMIC for the purpose.

Levering these tools, MEMIC can detect trends and outliers, enhance decision making and improve reserve management methods, the tech vendor claims.

This approach will facilitate allocating adequate funds for addressing projected claim expenditures.

MEMIC had previously leveraged the underwriting solution of Gradient AI to improve risk prediction and capture institutional data.

The company focuses on offering safety and injury management services for more than 20,000 employers and approximately 300,000 employees from Maine to Florida.

MEMIC claims senior vice-president Matt Harmon said: “The benchmarking tool compares individual claims to similar industry claims. It highlights those claims deviating from norms, enabling us to decide if reserve levels are adequate.

“Adequate reserves create greater accuracy when calculating experience modification factors, and in turn better serve the interest of the policyholder and MEMIC alike.”

The benchmarking solution of Gradient AI uses a data pool comprising numerous claims and policies, facilitating customers to compare the reserves of their claims to those from similar ones in the industry.

Claims managers can subsequently evaluate if a particular claim’s reserve is on target or requires any adjustments.

In June 2023, Gradient AI and full-service managing general underwriter (MGU) ATS Underwriting collaborated to expand the latter’s Medical Stop Loss insurance business.

This article first appeared on Life Insurance International .

MEMIC Selects Gradient AI to Enhance Claims Outcomes

Mon, 21 Aug 2023 13:31:41 GMT

August 21, 2023

Expanding on an existing relationship, the insurer will use advanced benchmarking and predictive solutions to manage better forecast trends, optimize case reserves and manage costs.

By Anthony R. O’Donnell

MEMIC (Portland, Maine), an A.M. Best “A” rated workers’ compensation insurer, has expanded its relationship Gradient AI (Boston) , an enterprise software provider of artificial intelligence solutions for the insurance industry. Having already adopted Gradient AI’s underwriting solution to enhance risk prediction and capture institutional knowledge, MEMIC has now chosen Gradient AI to enhance its claims management outcomes.

Under this expanded agreement, MEMIC will leverage the advanced capabilities of Gradient AI’s Claims Benchmarking and Total Incurred Prediction solutions. These tools empower MEMIC to identify trends and outliers, improve the decision-making process and refine its reserve management processes so that sufficient funds are set aside to address anticipated claim costs, according to a vendor statement.

MEMIC serves more than 20,000 employers and their estimated 300,000 employees with dedicated safety and injury management service teams from Maine to Florida. With approximately 11,000 open claims at any given time, MEMIC is constantly looking for strategies to improve the efficiency and accuracy of its claims management operations, according to a joint statement. Recognizing the value of leveraging artificial intelligence and machine learning, MEMIC sought to gain additional insights to optimize claim outcomes, reduce claim costs, and improve the accuracy of caseload reserves. These needs motivated MEMIC to choose Gradient AI’s Benchmarking and Total Incurred Prediction solutions, the statement says.

Gradient AI says its benchmarking solution uses a data lake containing tens of millions of claims and policies, enabling users to see how their claims’ reserves compare to similar industry claims. Claims managers can then assess whether the reserve for a specific claim is on target or needs attention because it is over- or under-reserved, the vendor says.

Raising the Bar

“Our claim team is excellent but Gradient AI takes our game to a new level,” comments Matt Harmon, SVP, Claims, MEMIC. “The benchmarking tool compares individual claims to similar industry claims. It highlights those claims deviating from norms, enabling us to decide if reserve levels are adequate. Adequate reserves create greater accuracy when calculating experience modification factors, and in turn better serve the interest of the policyholder and MEMIC alike.”

The companies’ joint statement explains that the total incurred prediction solution predicts the future cost on a case-by-case basis. This enables MEMIC’s claims managers to set future reserves within a certain range of the predicted cost.

“We can create guardrails on our reserve levels so that more of our future predictions fall within those parameters, enabling us to be much more consistent than we are today, and delivering more reliable results,” Harmon adds. “We operate under considerable pressure to ensure the utmost accuracy in our case file reserves. While we have engaged with other AI providers, Gradient AI stands apart by introducing reserve-based solutions that are truly game changers. The inclusion of both benchmarking and predictive tools represents new and exciting territory for us. They are filling a previously unmet need, which holds immense promise.”

Journey Toward Greater Excellence

“MEMIC’s visionary approach to unleashing the potential of data within its workers’ comp claim operations is commendable,” says Stan Smith, founder and CEO, Gradient AI. “Through strategic benchmarking against industry-wide data, and leveraging powerful predictive insights, the company is poised to elevate the precision of its case reserves, amplify claims management effectiveness, and enhance its ability to grow profitably. We are proud to provide industry-leading underwriting and claims management solutions and thrilled to continue to support MEMIC on its journey toward greater excellence.”

This article first appeared on Insurance Innovation Reporter .

MEMIC Selects Gradient AI to Gain Competitive Advantage in Its Workers’ Compensation Claims Operations

Mon, 21 Aug 2023 12:38:45 GMT

Advanced Benchmarking and Prediction Solutions Help MEMIC Better Forecast Claim Trends, Optimize Case Reserves, and Manage Claims Costs More Effectively

Orlando, FL August 21, 2023 - Gradient AI , a leading enterprise software provider of artificial intelligence (AI) solutions in the insurance industry, today announced at WCI 2023 Conference an expanded relationship with MEMIC, an A.M. Best “A” (Excellent) rated workers’ compensation insurer. Having already harnessed Gradient AI’s underwriting solution to enhance risk prediction and capture institutional knowledge, MEMIC has now chosen Gradient AI to enhance its claims management outcomes.

Under this expanded agreement, MEMIC will leverage the advanced capabilities of Gradient AI’s Claims Benchmarking and Total Incurred Prediction solutions. These innovative tools empower MEMIC to identify trends and outliers, improve the decision-making process and refine its reserve management processes so that sufficient funds are set aside to address anticipated claim costs.

MEMIC serves more than 20,000 employers and their estimated 300,000 employees with dedicated safety and injury management service teams from Maine to Florida. With approximately 11,000 open claims at any given time, MEMIC is constantly looking for strategies to improve the efficiency and accuracy of its claims management operations. Recognizing the value of leveraging artificial intelligence and machine learning, MEMIC sought to gain additional insights to optimize claim outcomes, reduce claim costs, and improve the accuracy of caseload reserves. These needs motivated MEMIC to choose Gradient AI’s Benchmarking and Total Incurred Prediction solutions.

Gradient AI’s benchmarking solution leverages an industry-leading data lake containing tens of millions of claims and policies, enabling users to see how their claims’ reserves compare to similar industry claims. Claims managers can then assess whether the reserve for a specific claim is on target or needs attention because it is over- or under-reserved.

“Our claim team is excellent but Gradient AI takes our game to a new level, said Matt Harmon, SVP of Claims at MEMIC. “The benchmarking tool compares individual claims to similar industry claims. It highlights those claims deviating from norms, enabling us to decide if reserve levels are adequate. Adequate reserves create greater accuracy when calculating experience modification factors, and in turn better serve the interest of the policyholder and MEMIC alike.”

The total incurred prediction solution predicts the future cost on a case-by-case basis. This enables MEMIC’s claims managers to set future reserves within a certain range of the predicted cost.

“We can create guardrails on our reserve levels so that more of our future predictions fall within those parameters, enabling us to be much more consistent than we are today, and delivering more reliable results,” Harmon added. “We operate under considerable pressure to ensure the utmost accuracy in our case file reserves. While we have engaged with other AI providers, Gradient AI stands apart by introducing reserve-based solutions that are truly game changers. The inclusion of both benchmarking and predictive tools represents new and exciting territory for us. They are filling a previously unmet need, which holds immense promise."

“MEMIC's visionary approach to unleashing the potential of data within its workers’ comp claim operations is commendable," said Stan Smith, CEO and founder of Gradient AI. "Through strategic benchmarking against industry-wide data, and leveraging powerful predictive insights, the company is poised to elevate the precision of its case reserves, amplify claims management effectiveness, and enhance its ability to grow profitably. We are proud to provide industry-leading underwriting and claims management solutions and thrilled to continue to support MEMIC on its journey toward greater excellence.”

About Gradient AI

Gradient AI is a leading provider of proven artificial intelligence (AI) solutions for the insurance industry. Its solutions improve loss ratios and profitability by predicting underwriting and claim risks with greater accuracy, as well as reducing quote turnaround times and claim expenses through intelligent automation. Unlike other solutions that use a limited claims and underwriting dataset, Gradient's software-as-a-service (SaaS) platform leverages a vast industry data lake comprising tens of millions of policies and claims. It also incorporates numerous other features including economic, health, geographic, and demographic information. Customers include some of the most recognized insurance carriers, MGAs, MGUs, TPAs, risk pools, PEOs, and large self-insured employers across all major lines of insurance. By using Gradient AI's solutions, insurers of all types achieve a better return on risk. To learn more about Gradient AI, please visit: https://www.gradientai.com .

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Elyse Familant

Results PR

978-376-5446

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From Laggard To Leader: How The Insurance Industry Is Embracing AI To Deliver Real Business Benefits

Mon, 14 Aug 2023 13:38:41 GMT

August 8, 2023

By Stan Smith, Forbes Councils Member, Forbes Technology Council

AI has made significant progress in the insurance industry. The traditionally cautious insurance sector now widely accepts AI as a powerful tool for cost reduction, growth, operational efficiency and employee satisfaction. This transformation is being driven in large part by a new category of companies known as "insurtechs," which excel at leveraging AI, data analytics and industry data lakes to gain a competitive advantage. While AI chatbots are still in their early stages, purpose-built AI solutions in insurance offer tangible benefits in claims management, underwriting and other crucial areas of the insurance value chain.

AI models can now predict potential policy losses and claim directions due to their ability to consider numerous inputs simultaneously, like medical history, demographics, driving records, weather information and adjuster's notes. The speed and accuracy of the trained AI models provide valuable insights for underwriters and adjusters, leading to better outcomes.

Progressive insurers are embracing the transformative power of AI to improve their operations, attract customers and enter new markets. Let's examine this paradigm shift and explain how insurers who are on the fence about AI can get started with it.

AI-Driven Underwriting Reduces Risk, Saves Time And Increases Profits

The ability to accurately assess risk is fundamental for insurers. By leveraging AI and advanced analytics, insurers can access a wealth of information that enables underwriters to make better pricing decisions. AI serves as a knowledgeable digital assistant, utilizing industry data lakes containing millions of policies to enhance underwriters' risk assessment abilities and evaluate policies more efficiently.

Typical underwriters might only see about 10,000 policies throughout their careers and only retain insights from a few hundred. In contrast, AI models can learn from millions of policies, providing underwriters with deeper insights than ever before. These AI-enabled digital assistants continuously learn and improve their performance, contributing to the underwriters’ expertise.

AI Manages Claims Faster, Identifies Red Flags And Prioritizes Them

Prioritizing the right claim at the right time is important to successful claims management. However, claims managers often face challenges identifying and assessing severe claims due to large caseloads and vast amounts of data to manage. AI-powered solutions alleviate this burden by learning from millions of claims and constantly evaluating their severity and risk. These solutions also act as highly experienced digital assistants, tirelessly examining claims and surfacing those that require attention while automatically processing the straightforward ones.

This allows adjusters and claims managers to proactively manage claims, focusing their efforts where needed. Everyone benefits from this approach; workers get the specific treatment they need sooner and recover from injuries more quickly, claims costs are reduced and management efficiencies are improved.

How AI Bridges The Talent Gap

The insurance industry is facing a significant talent crisis as many experienced workers approach retirement age. As noted in a 2021 report from the U.S. Chamber of Commerce , "According to the U.S. Bureau of Labor Statistics, the number of insurance professionals aged 55 and older has increased 74% in the last ten years, leading the BLS to estimate that over the next 15 years, 50% of the current insurance workforce will retire, leaving more than 400,000 open positions unfilled." The industry risks losing valuable knowledge and expertise. Fortunately, AI solutions offer a remedy for this “brain drain” by capturing the experience of seasoned professionals and enabling new employees to learn from it.

This process leverages “institutional knowledge,” which includes the data, expertise and best practices accumulated by employees over time. Insurers can leverage this valuable knowledge to train AI models, effectively transferring it to newer employees. By providing new hires with AI-powered virtual “guardrails,” insurers can reduce learning curves mitigating the potential loss of expertise due to retiring underwriters and adjusters.

Getting Started With AI In Insurance

Where To Focus: Before you start on your AI journey, it’s important to assess your business strategy and determine the areas that would benefit the most from AI. Harvard professor Michael Porter's three generic strategies of cost, differentiation and focus are a good place to start. Select the strategy consistent with your business model and think about how AI could impact that strategy. For example, if cost reduction is a priority, AI can be used to automatically process low-risk policies, freeing up underwriters to focus on more complex policy submissions. AI can help staff make more decisions quickly, maximizing efficiency and allocating resources more effectively.

Check The State Of Your Data: Just like an aircraft pilot does a preflight check, checking the state of your data quality is crucial for AI initiatives. Evaluate if you have the necessary clean, normalized data and identify any gaps. Integrating AI into insurance workflows can be challenging due to the massive amounts of formatted data required by AI models. Even after data preparation is complete, additional time will be needed to train AI models. However, insurers wanting to bypass these steps and immediately benefit from AI can leverage industry data lakes and pretrained models. When ready, insurers can enhance model accuracy by incorporating their own data, further unlocking the value of AI for their businesses.

Organizational Resources: To determine if your organization has the necessary resources to support your AI initiative, assess your existing workflows and identify key stakeholders involved. Aligning these stakeholders and making decisions on how to utilize AI within your operations is crucial for success. Consider how AI will be applied within the context of your strategic goals.

Revolutionizing Insurance With AI: The Future Has Arrived

The insurance sector has embraced AI to improve customer service, increase profitability and address the talent gap. AI impact is proving to be greater than the digital transformation that preceded it.

AI enables insurers to enter new markets, price more competitively, reduce loss ratios, settle claims more efficiently and transfer knowledge.

Incorporating AI into strategic, operational areas is essential for insurance companies to stay more competitive in today’s challenging economy. This is only the beginning, as new advancements like generative AI will accelerate change. But that is a topic for another article, which I will cover soon. The important point is that insurers are already gaining a competitive advantage and growing their businesses profitably by leveraging AI. It has become a critical technology enabler for growth and efficiency, and those who fail to adopt it risk falling behind.

Stan Smith is CEO and founder of Gradient AI .

This article first appeared on Forbes .

Transportation insurance company partners with Gradient AI

Wed, 02 Aug 2023 21:47:26 GMT

August 2, 2023

Partnership will provide better insight into individual claims

By Steven Byerley

Gradient AI, a software provider for the insurance industry, has announced a new partnership. American Trucking and Transportation Insurance Company, a Risk Retention Group (ATTIC, RRG), has selected Gradient AI's claims management solution to enhance their ability to predict claim risk, triage claims, and efficiently close commercial transportation claims.

ATTIC, RRG is a member-owned risk retention group that offers auto liability insurance to small and midsize commercial trucking companies. The company offers access to high premium limits and large deductible options without onerous collateral requirements.

The trucking industry has witnessed a surge in claim verdicts in recent years, according to research from the American Transportation Research Institute. Factors contributing to this escalation include an increase in injury and fatality crashes involving commercial trucks, as well as the impact of social inflation, which leads to claims exceeding overall economic inflation. In response to these challenges, insurers serving the trucking sector, including ATTIC, have been actively seeking new strategies to handle claims effectively and anticipate outcomes.

Previously, ATTIC relied primarily on adjusters' previous claims experience and intuition to predict claims. However, limited access to third-party datasets and machine-learning capabilities hindered their ability to benchmark their performance against similar insurers facing similar auto incident scenarios.

ATTIC selected Gradient AI based on its experience in applying AI to the commercial auto insurance industry, its extensive commercial auto industry data lake containing millions of claims, and its predictive analytics capabilities for claims solutions. The partnership will enable ATTIC to utilize industry data to gain better insights into the direction and development of individual claims, triaging higher-risk claims to experienced adjusters and operating more efficiently overall.

“Gradient AI’s commercial auto industry data lake and machine learning are game changers for us,” said Ken Crippen, CEO of ATTIC, RRG. “We leverage Gradient AI’s claims management solution to access extensive datasets and forecast claim outcomes more accurately and much earlier in their life cycles. This enables us to achieve greater financial stability and predictability, resolve claims more quickly, and deliver substantial cost savings to our member clients.”

ATTIC is the latest in a string of insurance companies to partner with Gradient AI. Last month, the insurtech announced a partnership with ATS Underwriting . In May, it partnered with Signal Mutual Indemnity Association .

This article first appeared on Insurance Business .

American Trucking and Transportation Insurance Company Selects Gradient AI

Wed, 02 Aug 2023 14:32:37 GMT

August 2, 2023

The risk retention group seeks to enhance claims management and facilitate better outcomes through an integrated solution with Origami Risk.

By Anthony R. O’Donnell

American Trucking and Transportation Insurance Company , a Risk Retention Group (ATTIC, RRG) selected Gradient AI’s (Boston) claims management solution to predict claim risk, triage claims, and close commercial transportation claims more quickly and cost-effectively.

ATTIC, RRG a member-owned risk retention group in the commercial automotive industry, offers auto liability insurance to small and midsized commercial trucking companies. Committed to empowering motor carriers with greater control of every aspect of insurance, ATTIC, RRG provides access to high premium limits and large deductible options without burdensome collateral requirements.

In recent years, the trucking industry has witnessed an unprecedented surge in claim verdicts, according to research from the American Transportation Research Institute. This escalation can be attributed to several factors, including a notable increase in injury and fatality crashes involving commercial trucks, as well as the impact of social inflation, which leads to claims exceeding the overall economic inflation. In response to these challenges, ATTIC and other insurers serving the trucking sector have been actively exploring new strategies to effectively handle claims and anticipate potential outcomes, according to a Gradient AI statement.

Prior to adopting Gradient AI, ATTIC’s claims predictions were based primarily on an adjuster’s previous claims experience and professional intuition, the vendor statement says. However, limited access to third-party datasets and machine-learning capabilities hindered their ability to assess how they were performing against similar insurers in comparable auto incident scenarios. ATTIC, RRG recognized the need for a larger dataset and more advanced tools to innovate and continue to drive improved results for its members.

ATTIC, RRG chose Gradient AI due to its prior experience applying AI to the commercial auto insurance industry, its commercial auto industry data lake containing millions of claims, and its predictive analytics capabilities for claims solutions, the vendor reports. As a result, ATTIC, RRG can leverage industry data to better understand the direction and development of individual claims to triage higher-risk ones to the more experienced adjusters, enabling them to operate more efficiently, Gradient says.

Game Changers

“Gradient AI’s commercial auto industry data lake and machine learning are game changers for us,” comments Ken Crippen, CEO, ATTIC, RRG. “We leverage Gradient AI’s claims management solution to access extensive datasets and forecast claim outcomes more accurately and much earlier in their lifecycles. This enables us to achieve greater financial stability and predictability, resolve claims more quickly, and deliver substantial cost savings to our member clients.”

ATTIC, RRG is using Gradient AI’s claims management system within Origami Risk ’s (Chicago) insurance platform. Gradient AI’s partnership with Origami Risk makes it possible for Gradient AI to deliver a well-integrated solution to ATTIC, RRG and similar insurers that rely on Origami Risk’s platform.

“As commercial automotive businesses experience surges in potential liability and claims costs, AI-powered claims management has become critical to predict, control and contain costs,” says Stan Smith, CEO, Gradient AI. “We are gratified that ATTIC, RRG turned to Gradient Ai to improve claims management and we are excited to partner with Origami Risk to achieve next-level outcomes for the commercial auto industry. The increasing demand we’re seeing from midsized providers and self-insured companies speaks volumes about the challenges these companies are facing and the opportunities that AI-enabled claims management can deliver for them.”

This article first appeared on Insurance Innovation Reporter .

American Trucking and Transportation Insurance Company Selects Gradient AI to Improve Trucking Claims Outcomes

Tue, 01 Aug 2023 13:40:36 GMT

Enhances Claims Management and Facilitates Better Outcomes Through an Integrated Solution with Origami Risk

Boston, MA August 1, 2023 - Gradient AI, a leading enterprise software provider of artificial intelligence (AI) solutions in the insurance industry, today announced that American Trucking and Transportation Insurance Company , a Risk Retention Group (ATTIC, RRG) selected Gradient AI’s claims management solution to predict claim risk, triage claims, and close commercial transportation claims more quickly and cost-effectively.

Prior to adopting Gradient AI, ATTIC’s claims predictions were based primarily on an adjuster’s previous claims experience and professional intuition. However, limited access to third-party datasets and machine-learning capabilities hindered their ability to assess how they were performing against similar insurers in comparable auto incident scenarios. ATTIC, RRG recognized the need for a larger dataset and more advanced tools to innovate and continue to drive improved results for its members.

ATTIC, RRG chose Gradient AI due to its prior experience applying AI to the commercial auto insurance industry, its commercial auto industry data lake containing millions of claims, and its predictive analytics capabilities for claims solutions. As a result, ATTIC, RRG can leverage industry data to better understand the direction and development of individual claims to triage higher risk ones to the more experienced adjusters, enabling them to operate more efficiently.

“Gradient AI’s commercial auto industry data lake and machine learning are game changers for us,” said Ken Crippen, CEO of ATTIC, RRG. “We leverage Gradient AI’s claims management solution to access extensive datasets and forecast claim outcomes more accurately and much earlier in their lifecycles. This enables us to achieve greater financial stability and predictability, resolve claims more quickly, and deliver substantial cost savings to our member clients.”

ATTIC, RRG is using Gradient AI’s claims management system within Origami Risk’s insurance platform. Gradient AI’s partnership with Origami Risk makes it possible for Gradient AI to deliver a well-integrated solution to ATTIC, RRG and similar insurers that rely on Origami Risk’s platform.

“As commercial automotive businesses experience surges in potential liability and claims costs, AI-powered claims management has become critical to predict, control and contain costs,” said Stan Smith, CEO of Gradient AI. “We are gratified that ATTIC, RRG turned to Gradient Ai to improve claims management and we are excited to partner with Origami Risk to achieve next-level outcomes for the commercial auto industry. The increasing demand we’re seeing from midsized providers and self-insured companies speaks volumes about the challenges these companies are facing and the opportunities that AI-enabled claims management can deliver for them.”

About Gradient AI

Gradient AI is a leading provider of proven artificial intelligence (AI) solutions for the insurance industry. Its solutions improve loss ratios and profitability by predicting underwriting and claim risks with greater accuracy, as well as reducing quote turnaround times and claim expenses through intelligent automation. Unlike other solutions that use a limited claims and underwriting dataset, Gradient's software-as-a-service (SaaS) platform leverages a vast dataset comprising tens of millions of policies and claims. It also incorporates numerous other features including economic, health, geographic, and demographic information. Customers include some of the most recognized insurance carriers, MGAs, TPAs, risk pools, PEOs, and large self-insured employers across all major lines of insurance. By using Gradient AI's solutions, insurers of all types achieve a better return on risk. To learn more about Gradient, please visit: https://www.gradientai.com .

Media Contacts

Elyse Familant

Results PR

978-376-5446

elysef@resultspr.net

AI technology builds momentum in claims sector

Wed, 12 Jul 2023 17:07:24 GMT

July 12, 2023

By Jon Campisi

The insurance claims sector is tapping into technologies such as machine learning and artificial intelligence to increase efficiency, improve outcomes and detect fraud.

Whether it is a simpler version of AI that can be used to automate back-office processes or the newer generation AI, which can be used to analyze policyholders’ claims history and policy preferences, these new technologies are becoming more widely used in the industry.

“There are a lot of policies that are written and a lot of claims that occur, which all have data around them,” said Stan Smith, founder and CEO of Boston-based Gradient AI , which provides AI services for the insurance industry. “As I looked into it, claims were more of a challenge than underwriting. There was a lot of focus on how to get our claims costs under control.”

Yet “the vast majority of claims being pretty benign means a lot of people have busy work to do,” he said, adding that technology can help mitigate workloads.

Gia Sawko, Gradient’s senior product manager of claims , said AI will change the way jobs are performed.

“There are jobs out there that are duplicative, or the resources could be divided differently so you’re spending more time on the complex claims,” Ms. Sawko said. “There are things that could be done that could be automated with the use of AI.”

While the term AI is used broadly, it encompasses various platforms and processes. Automation and AI, for example, are sometimes used interchangeably, but they differ, said Leah Cooper, Chattanooga, Tennessee-based managing director of IT for Sedgwick Claims Management Services Inc.

“AI is just the tool,” Ms. Cooper said. “Automation is putting all of the pieces together.”

When rolling out AI pilot programs, Sedgwick saw success in document digitization and validation, reading for content, and automatic eligibility confirmation used to validate whether someone has proper coverage.

“AI is not going to replace someone’s job. What it’s going to do is take the red tape and the busy work off someone’s desk so that you change the story about that person’s job,” Ms. Cooper said.

She said AI is also being used to detect claims oddities and discrepancies that could potentially signal insurance fraud (see related story below).

“There are a lot of different opportunities when it comes to plugging into tools that can spot abnormalities,” she said. “Using AI, we’re able to spot the outliers, what’s not normal.”

The insurance industry traditionally lagged in technology, said Cheryle Tuttle, Tampa, Florida-based vice president of property/casualty and workers compensation for Sapiens International Corp.

“A lot of the problems have not changed,” Ms. Tuttle said. “It’s just technology and things like AI, data analytics, automation, these are all helping people in being proactive instead of reactive.”

New technologies such as AI have “tremendous promise for our industry but if misused could be certainly damaging and counterproductive,” said Joe Powell, Fort Wayne, Indiana-based senior vice president for data and analytics with Gallagher Bassett Services Inc.

Mr. Powell drew distinctions between the different forms of AI, including “narrow” AI, or machine learning, where models are built or “trained” on data targeting a specific desired decision outcome, and generative AI, which can involve taking a “single, very general model and (applying) it to a whole host of use cases.”

“Those two are not substitutes for each other,” he said. “It’s not like we’ve moved from one to the other. I think the new, generative AI models are going to complement our narrow AI models in a lot of ways,” Mr. Powell said.

Generative AI has the potential to improve the claims process because it enables claims professionals to “deliver a consistent product in an efficient way,” he said.

Mike Cwynar, Orange County, California-based senior vice president of product delivery for Mitchell Casualty Solutions Group, said AI can benefit back-office claims management as well as customer service and document digitization tasks.

“A lot (of our clients) are really putting thought into how to make it a really user-friendly claims experience,” he said.

Insurance personnel considering using generative AI should start small. “I wouldn’t go in and try to rewrite your back-office process here,” Mr. Cwynar said.

“It’s key to partner with folks who understand not just the technology but the business domain, the problem that you’re trying to solve, and being really careful in what you end up going after,” he said.

Generative AI, he said, can generate content such as photos, video, text and code, and attempts to read questions and provide answers. But it can also suffer from what’s known as “hallucination,” a term referring to times when AI provides an answer not in line with what is expected.

“You really need to understand what you’re trying to get out of tools like this, because they’re tools right now,” Mr. Cwynar said. “I wouldn’t throw generative AI at the most complicated problem that I have out there. I’d use it to automate tasks and build my expertise and sophistication with it over time.”

This article first appeared on Business Insurance .

ATS Underwriting and Gradient AI to jointly augment underwriting processes

Wed, 07 Jun 2023 13:02:44 GMT

June 7, 2023

Gradient AI’s SAIL Solution boasts a high-quality medical industry data lake, powered by AI predictive analytics.

Full-service managing general underwriter (MGU) ATS Underwriting has teamed up with artificial intelligence (AI) solutions supplier Gradient AI to expand its Medical Stop Loss insurance business.

Under the partnership, ATS Underwriting implemented Gradient AI’s SAIL platform to improve its underwriting processes within the small group transitional market.

This market is mostly made up of smaller businesses that are transitioning from fully insured to self-funded plans.

The tech vendor claims that ATS switched to its SAIL Solution due to its “high-quality” medical industry data lake, which is powered by AI predictive analytics.

Gradient AI noted that ATS’s team acquired important insights and improved underwriting decision-making by utilising SAIL’s advanced features.

ATS Underwriting president Andrew Trupiano, said: “Gradient AI has enabled ATS’s team to quote more groups even when the data is lacking. By leveraging AI-based underwriting together with traditional underwriting, we’re taking a hybrid approach to qualify groups, price risk more accurately, and better serve our clients.”

Gradient AI CEO and founder Stan Smith said: “Until now, lack of medical claims data represented a barrier to entry to the small business self-funded market for medical stop loss providers.

“However, innovative leaders like ATS have embraced AI-powered analytics and underwriting to better meet the needs of this large and growing segment. We are proud to provide ATS with advanced AI capabilities, to serve this important and growing market.”

This article first appeared on Life Insurance International .

Gradient AI announces collaboration with ATS Underwriting

Wed, 07 Jun 2023 12:54:39 GMT

June 7, 2023

Partnership will improve MGU's underwriting process

By Steven Byerley

Insurance software provider Gradient AI has announced a collaboration with ATS Underwriting, a managing general underwriter focused on medical stop-loss insurance. The partnership aims to improve underwriting processes within the small group transitional market, catering to smaller companies transitioning from fully insured to self-funded plans.

After initially using a competitive AI solution, ATS switched to Gradient AI's SAIL solution due to its high-quality medical industry data lake, Gradient AI said.

According to Andrew Trupiano, president of ATS Underwriting, the collaboration with Gradient AI allows ATS to leverage opportunities in the stop-loss space and establish a stronger presence.

“By leveraging AI-based underwriting together with traditional underwriting, we’re taking a hybrid approach to qualify groups, price risk more accurately, and better serve our clients,” Trupiano said.

Traditionally, underwriting for small groups involves the use of individual health questionnaires (IHQs), which can be burdensome and administratively complex. However, with Gradient AI's SAIL solution, IHQs are no longer necessary to predict health plan risk. This removes a significant barrier for smaller groups seeking self-funded options, granting them access to cost savings, flexibility, and benefits typically reserved for larger groups, the company said.

“Even when the data is lacking, leveraging AI-based underwriting together with traditional underwriting allows us to quote more groups.,” Trupiano said. This enables ATS to serve a wider range of clients and seize opportunities in the small group transitional market.

“Innovative leaders like ATS have embraced AI-powered analytics and underwriting to better meet the needs of this large and growing segment,” said Stan Smith, founder and CEO of Gradient AI.

Gradient AI also recently announced partnerships with Signal Mutual Indemnity Association and Western Skies .

This article first appeared on Insurance Business .

ATS Underwriting Chooses Gradient AI to Expand Medical Stop Loss Insurance Business

Tue, 06 Jun 2023 14:08:27 GMT

Gradient AI’s SAIL Removes Barriers for Small Groups in Transition to Self-Funded Plans

Boston, MA June 6, 2023 - Gradient AI , a leading enterprise software provider of artificial intelligence (AI) solutions in the insurance industry, today announced that ATS Underwriting , a privately held, full-service managing general underwriter (MGU) specializing in Medical Stop Loss insurance, has adopted Gradient AI’s SAIL ™ Solution to enhance its underwriting processes within the small group transitional market. This market primarily consists of smaller companies undergoing a significant shift from fully insured to self-funded plans.

ATS Underwriting specializes in delivering affordable medical stop loss insurance solutions. With a 30-year track record in underwriting and working with AM Best rated “A” (excellent) carriers, the company is dedicated to meeting the evolving needs of its clients. Recognizing the importance of expanding self-funded opportunities for its small business and carriers, ATS determined that AI technology would best support this strategic goal.

After using a competitive AI solution, ATS switched to Gradient AI’s SAIL Solution due to its high-quality medical industry data lake, powered by AI predictive analytics. By harnessing the advanced features of SAIL, ATS’s team has gained valuable insights and increased confidence in its underwriting decision-making. As a result, the company can deliver greater service to its clients in a challenging yet growing market.

“As a leader in the small group transitional market, partnering with Gradient AI is helping ATS leverage a massive opportunity in the stop loss space and establish a stronger presence in the market,” said Andrew Trupiano, president of ATS Underwriting. “Gradient AI has enabled ATS’s team to quote more groups even when the data is lacking. By leveraging AI-based underwriting together with traditional underwriting, we’re taking a hybrid approach to qualify groups, price risk more accurately, and better serve our clients.”

Traditionally, underwriting for small groups requires the use of individual health questionnaires (IHQs), meaning employees must provide detailed information about themselves and their dependents. This process can be burdensome for employees, challenging for HR departments, and administratively complex for MGUs. Consequently, most smaller groups and MGUs forego IHQs, missing out on potential self-funded opportunities that may otherwise have been available to them.

However, when using a medical underwriting AI tool like SAIL, IHQs are no longer needed to predict health plan risk. This removes a significant barrier for smaller groups that want to self-fund, granting them access to cost savings, flexibility, and benefits usually reserved for larger groups.

Since 2007, ATS estimates that the number of companies with three to 500 employees buying self-funded medical coverage has grown by over 50%. As market pressure continues to push for the elimination of IHQs, predictive analytics enables insurers to secure quotes earlier and provides greater flexibility for stop loss carriers that cover companies of this size. Similarly, for carriers that cover larger companies, the use of predictive analytics also helps streamline underwriting and accelerate quote turnaround times.

“Until now, lack of medical claims data represented a barrier to entry to the small business self-funded market for medical stop loss providers,” said Stan Smith, CEO and founder of Gradient AI. “However, innovative leaders like ATS have embraced AI-powered analytics and underwriting to better meet the needs of this large and growing segment. We are proud to provide ATS with advanced AI capabilities, to serve this important and growing market.”

About Gradient AI

Gradient AI is a leading provider of proven artificial intelligence (AI) solutions for the insurance industry. Its solutions improve loss ratios and profitability by predicting underwriting and claim risks with greater accuracy, as well as reducing quote turnaround times and claim expenses through intelligent automation. Unlike other solutions that use a limited claims and underwriting dataset, Gradient's software-as-a-service (SaaS) platform leverages a vast dataset comprising tens of millions of policies and claims. It also incorporates numerous other features including economic, health, geographic, and demographic information. Customers include some of the most recognized insurance carriers, MGAs, MGUs, TPAs, risk pools, PEOs, and large self-insured employers across all major lines of insurance. By using Gradient AI's solutions, insurers of all types achieve a better return on risk. To learn more about Gradient, please visit: https://www.gradientai.com .

Media Contacts

Elyse Familant

Results PR

978-376-5446

elysef@resultspr.net

Gradient AI partners with Signal Mutual Indemnity Association

Wed, 24 May 2023 15:40:40 GMT

May 24, 2023

Partnership will allow insurer to triage claims rapidly for more effective resolution.

By Ryan Smith

Insurtech provider Gradient AI has partnered with Signal Mutual Indemnity Association.

Signal Mutual is the largest self-insured group provider of longshore benefits in the country. With approximately 5,000 new claims each year, the association is constantly looking for strategies to improve its claims management efficiency and effectiveness. Recently, Signal Mutual turned to Gradient AI to use artificial intelligence to optimize its workers’ compensation claims workflow and triage claims more efficiently.

Gradient AI will allow Signal Mutual to triage claims rapidly for more effective resolution, the insurtech said. Simpler claims are sent to less experienced staff and processed quickly, while claims predicted to become complex are assigned to more experienced adjusters. This strategy frees up internal resources to focus on claims that need more attention.

“While we have only recently started integrating Gradient AI into our claims process, it’s already making a positive impact on the way we work,” said Ann Latimer, senior vice president and head of claims operations at Signal Mutual. “At Signal, we are committed to expediting injured workers’ return to work, minimizing claims costs, and enhancing our members’ claims experience. We look forward to continuing to leverage Gradient AI’s capabilities to resolve claims more quickly and reduce our overall cost of claims.”

Gradient AI has also collaborated with Signal Mutual to create an adjuster’s scorecard that utilizes analytics to ensure that members’ claims are managed efficiently, the insurtech said.

“We are excited to support Signal Mutual’s goal of continuously improving its claims management operations and outcomes,” said Stan Smith, founder and CEO of Gradient AI. “Our suite of AI-powered solutions and the scorecards we’ve created together are just the beginning. We look forward to continuing our partnership with the Signal team to improve the claims experience for its members’ organizations and employees.”

Earlier this month, Gradient AI announced that managing general underwriter Western Skies had chosen its SAIL product to provide stop-loss access to smaller, self-funded groups. In April, the company announced that health insurance captive Roundstone Insurance had begun to utilize the SAIL product.

This article first appeared on Insurance Business .

Signal Mutual Selects Gradient AI’s Claims Management Solution to Improve Workers’ Comp Claims Outcomes

Tue, 23 May 2023 13:39:14 GMT

Enhances Claims Management and Facilitates Faster Return-to-Work for Injured Employees

Boston, MA May 23, 2023 - Gradient AI , a leading enterprise software provider of artificial intelligence (AI) solutions in the insurance industry, today announced that Signal Mutual Indemnity Association has partnered with Gradient AI to more efficiently rank risks, triage claims and improve claim management performance.

Signal Mutual is the largest self-insured group provider of Longshore benefits in the United States. Its success stems from its unique business model for selecting, underwriting, and serving its members, while supporting the broader community of maritime employers. The Signal claims management and adjusting teams are highly regarded in the industry for their superior claim outcomes and unsurpassed service to the Association’s members.

With approximately 5,000 new claims each year, the Association is constantly seeking ways to improve its claims management efficiency and effectiveness. Recently, Signal Mutual turned to artificial intelligence and engaged with Gradient AI to optimize the workers’ compensation claims workflow and triage claims more efficiently.

With Gradient AI, Signal Mutual can now triage claims rapidly for more effective resolution. Simpler claims are sent to less experienced staff and processed quickly through expedited processes, while claims predicted to become complex are promptly assigned to more seasoned adjusters. This approach frees up internal resources to focus on claims that need more attention at the time when it is needed most.

Signal Mutual chose Gradient AI due to its extensive experience in AI within the insurance industry, as well as its predictive analytics capabilities for claims solutions and its vast industry data lake containing tens of millions of policies and claims.

“While we have only recently started integrating Gradient AI into our claims process, it’s already making a positive impact on the way we work,” said Ann Latimer, SVP and head of claims operations for Signal Mutual. “At Signal, we are committed to expediting injured workers' return to work, minimizing claims costs, and enhancing our members’ claims experience. We look forward to continuing to leverage Gradient AI's capabilities to resolve claims more quickly and reduce our overall cost of claims.”

Gradient AI has also collaborated with Signal to create an adjusters’ scorecard that leverages analytics to ensure members’ claims are managed effectively and efficiently, leading toward the best possible resolution.

In the future, Signal Mutual plans to further leverage Gradient AI’s industry data lake, benchmarking and reserves-setting capabilities.

“We are excited to support Signal Mutual’s goal of continuously improving its claims management operations and outcomes,” said Stan Smith, CEO and founder of Gradient AI. “Our suite of AI-powered solutions and the scorecards we’ve created together are just the beginning. We look forward to continuing our partnership with the Signal team to improve the claims experience for its members’ organizations and their employees.”

About Gradient AI

Gradient AI is a leading provider of proven artificial intelligence (AI) solutions for the insurance industry. Its solutions improve loss ratios and profitability by predicting underwriting and claim risks with greater accuracy, as well as reducing quote turnaround times and claim expenses through intelligent automation. Unlike other solutions that use a limited claims and underwriting dataset, Gradient's software-as-a-service (SaaS) platform leverages a vast dataset comprising tens of millions of policies and claims. It also incorporates numerous other features including economic, health, geographic, and demographic information. Customers include some of the most recognized insurance carriers, MGAs, TPAs, risk pools, PEOs, and large self-insured employers across all major lines of insurance. By using Gradient AI's solutions, insurers of all types achieve a better return on risk. To learn more about Gradient, please visit: https://www.gradientai.com .

Media Contacts

Elyse Familant

Results PR

978-376-5446

elysef@resultspr.net

AI Can Be a ‘Virtual Assistant’ for Claims Adjusters

Thu, 11 May 2023 17:52:41 GMT

May 11, 2023

By Allen Laman

Claims adjusting resonated with Gia Sawko.

Throughout her 30-year insurance career, she’s viewed adjusters as people who make others whole again. While working as the managing director of global safety and claims for Whole Foods Market, she saw her work as the process of putting folks back together.

“And that really was a big focus for me my entire career,” Sawko shared. “That it wasn’t a claim. It was an actual person.”

Sawko is a former adjuster and currently the director of claims for Gradient AI. She recently spoke about the roles artificial intelligence can play in the claims process during Carrier Management’s InsurTech Summit.

She doesn’t envision AI eliminating adjusters. Instead, she sees the technology making them more efficient.

“So that’s where I think it’s a benefit to adjusters or to carriers,” Sawko said, “and where people will start to embrace it more. Because they’ll see it as this virtual assistant.”

She began her insurance career as an adjuster at Kemper before joining the team at Whole Foods. She was named vice president of business impact at Gallagher Bassett in 2016 and joined Gradient AI six years later.

How AI Can Help Claims

Gradient AI is a predictive modeling company. On the claims side, the platform’s pattern predictions serve as another tool adjusters can use to make informed decisions. Sawko described Gradient AI’s capabilities as an expert “tap on the shoulder” that can sound early warnings for claims.

“And that does really push toward a very positive outcome or mitigating some of those losses,” she added.

In short, predictive modeling uses statistics to project outcomes. The models are “trained” on historical data that informs these predictions. Sawko believes capturing institutional knowledge – and pairing it with predictive modeling that can spot red flags – has the potential to be hugely helpful for supervisors.

The National Association of Insurance Commissioners reports that machine learning models “can help quickly assess the severity of damages and predict the repair costs from historical data, sensors and images.”

According to the Gradient AI website, the company’s product identifies claims risks earlier in life cycles, which enables proactive steps towards effective outcomes and cost containment. It also reduces the duration of outstanding claims to better contain the costs and identify high-risk claims that require the attention of a more seasoned adjuster who can help manage claims to more favorable outcomes.

When asked how the insurance industry talent gap is affecting claims management, Sawko said there’s not only a shortage of adjusters, but also that the industry is “begging for change.” She believes AI has the power to quicken and enhance claims processes and could prove especially important as institutional knowledge leaves the industry in the coming years.

Gia Sawko

Senior Product Manager

Gradient AI

Challenges

When asked about the biggest challenges regarding claims handling and the use of AI in insurance, Sawko pointed to a need for good data quality and the ability to clearly explain to clients why the AI is telling adjusters to do something.

According to Rutgers University , bias “can cause artificial intelligence to make decisions that are systematically unfair to particular groups of people, and researchers have found this can cause real harm.”

The university reports that even if creators make conscious efforts to eschew bias in the data used in their algorithms, bias can still be baked into the data they select.

Another AI challenge is ensuring claims adjusters code unstructured data properly. When adjusters don’t understand the importance of properly entering incident tables and reports, entries can become directionless – and in turn, predictive models can suffer, Sawko said.

She added that being able to explain why a predictive model is designed a certain way is crucial. The inability to explain what the models are doing in a claim log means adoption suffers, she said.

“So, if someone says, ‘I’m going to raise this reserve,’ if the question was why, and somebody said, ‘Because the model told us to,’ as a risk manager, I wouldn’t allow that,” Sawko explained. “But I think understanding and having the ability to share that knowledge with clients, especially on the TPA (third party administrator) side, that would be really a very big deal.”

One Big Quote

Sawko said that there is so much work to do right now in the insurance industry that, without AI, claims can become very task oriented.

“I want adjusters to stay in the business, but I want them to be able to go to a soccer game and be able to not work the weekends,” she said. “So, this is really where balance between the two and that human touch can really come out, but it also can take away a lot of this duplication. And happy adjusters make happy claims and make happy clients, for sure.”

Western Skies selects Gradient AI product to serve small businesses

Wed, 10 May 2023 14:24:38 GMT

May 10, 2023

MGU to use key product to provide stop-loss access to small, self-funded groups

By Ryan Smith

Insurtech Gradient AI has announced that managing general underwriter Western Skies is now using its SAIL product to provide stop-loss access to smaller, self-funded groups.

Western Skies is using Gradient AI’s SAIL to tap into new markets and offer more affordable insurance plans to small and mid-size businesses, the insurtech said.

Small and mid-size businesses often want more affordable, self-funded insurance plans, however, limited access to small business claims data means that stop-loss insurers often decline to take on SME clients. That includes Western Skies, which has historically partnered with brokers, general agents and third-party administrators that represent companies with 150 or more employees.

However, that excludes a large slice of the market, Gradient AI said. According to recent data from the NAICS Association, more than 89% of the 18 million businesses in the US operate with fewer than 100 employees.

SAIL’s breadth and depth of data and predictive analytics will allow Western Skies to extend its reach to the small business market, Gradient AI said.

“Gradient AI’s technology is revolutionary and on the leading edge of the industry,” said Dave Kesler, vice president of underwriting for Western Skies. “It has enabled us to innovate and deliver new insurance solutions to the market. With Gradient AI, we can combine our strengths from our traditional business with SAIL’s predictive analytics to reach new markets that were previously unattainable. This has been a game-changer for Western Skies.”

In addition to growing its target market, Western Skies is also expanding geographically. Having done most of its business in the Southeast, the MGU is now using SAIL’s data sets and capabilities to expand west and northeast, Gradient AI said.

“We’re excited to partner with Western Skies, offering innovative new insurance opportunities to the small business market,” said Stan Smith, founder and CEO of Gradient AI. “With this stop-loss insurance partnership, everybody wins. Employers and employees can obtain insurance at more competitive rates, and Western Skies and their partnering brokers, agents and TPAs can now serve a new market segment.”

Last month, Gradient AI announced that health insurance captive Roundstone Insurance had chosen the SAIL product to help customers better assess risk. In March, the company announced that The Provant Group , a national commercial insurance brokerage, was utilizing the product.

This article first appeared on Insurance Business .

Western Skies Adopts Gradient AI to Grow SMB Stop-Loss Business

Tue, 09 May 2023 14:17:01 GMT

May 9, 2023

The MGU took advantage of Gradient AI’s SAIL for its rich medical data lake and predictive analytics to tap into new markets.

By Anthony R. O’Donnell

Western Skies , a Las Vegas managing general underwriter (MGU), has selected Gradient AI ’s (Boston) SAIL solution to provide stop-loss insurance access to smaller, self-funded groups.

Western Skies, a ManhattanLife (Houston) company and risk-bearing entity, is using SAIL to tap into new markets and offer more affordable insurance plans to small and midsize businesses, according to a Gradient AI statement. The company chose Gradient AI’s SAIL solution for its advanced AI and analytics capabilities that leverage its rich industry data lake of medical, lab, and prescription data.

Gradient AI contextualizes its engagement with Western Skies with reference to small and midsize businesses’ need for more affordable, self-funded insurance plans. Due to limited access to small business claims data that underwriters need to effectively assess group risk, stop-loss insurers have often declined to take on small business clients, the vendor notes. That includes Western Skies, which traditionally partners with brokers, general agents, and TPAs that represent companies with 150+ employees. Yet, this excludes a the large majority of the market, as recent data from the NAICS Association reveals that over 89 percent of the more than 18 million businesses in the United States operate with fewer than 100 employees.

With SAIL’s breadth and depth of data and predictive analytics, Western Skies can now extend its reach to the small business market of 25, 50, and 100 lives, according to Gradient AI. This opens opportunities for Western Skies to exponentially grow its prospective customer pool as well as its partnership potential with brokers, agents, and TPAs that work with smaller organizations, the vendor says. Gradient AI adds that SAIL’s analytics have also empowered Western Skies to move beyond traditional medical underwriting into the statistical probabilities behind financial underwriting, providing greater accuracy in assessing risk and pricing policies.

“Gradient AI’s technology is revolutionary and on the leading edge of the industry,” says Dave Kesler, VP, underwriting, Western Skies. “It has enabled us to innovate and deliver new insurance solutions to the market. With Gradient AI, we can combine our strengths from our traditional business with SAIL’s predictive analytics to reach new markets that were previously unattainable. This has been a game changer for Western Skies.”

Geographical Expansion

In addition to expanding its target market to large numbers of smaller employers, Western Skies is also expanding geographically, Gradient AI reports. Having found most of its success in the Southeast, the company is now leveraging SAIL’s vast data sets and capabilities to expand west and northeast, reaching new regions with its services, the vendor says. Western Skies can now accurately assess risk and compete against larger insurance providers as it taps into new markets, according to Gradient AI.

“We’re excited to partner with Western Skies, offering innovative new insurance opportunities to the small business market,” comments Stan Smith, founder and CEO, Gradient AI. “With this stop-loss insurance partnership, everybody wins. Employers and employees can obtain insurance at more competitive rates, and Western Skies and their partnering brokers, agents, and TPAs can now serve a new market segment.”

This article first appeared on Insurance Innovation Reporter .

Gradient AI Signs Up Western Skies

Tue, 09 May 2023 13:57:53 GMT

May 9, 2023

Gradient AI , a one of the fastest growing Artificial Intelligence (AI) insurtechs in the industry, today announced that Western Skies , a managing general underwriter (MGU), is using Gradient AI’s SAIL ™ solution to provide stop-loss insurance access to smaller, self-funded groups.

Western Skies is leveraging SAIL to tap into new markets and offer more affordable insurance plans to small and midsize businesses. The company chose Gradient AI’s SAIL solution for its advanced AI and analytics capabilities that leverage its rich industry data lake of medical, lab, and prescription data.

Small and midsize businesses have long desired more affordable, self-funded insurance plans. However, due to limited access to small business claims data that underwriters need to effectively assess group risk, stop-loss insurers have often declined to take on small business clients. This includes Western Skies, which traditionally partners with brokers, general agents, and third-party administrators (TPAs) that represent companies with 150+ employees. Yet, this excludes a the large majority of the market, as recent data from the NAICS Association reveals that over 89% of the more than 18 million businesses in the United States operate with fewer than 100 employees.

With SAIL’s breadth and depth of data and predictive analytics, Western Skies can now extend its reach to the small business market of 25, 50, and 100 lives. This opens opportunities for Western Skies to exponentially grow its prospective customer pool as well as its partnership potential with brokers, agents, and TPAs that work with smaller organizations. SAIL’s analytics have also empowered Western Skies to move beyond traditional medical underwriting into the statistical probabilities behind financial underwriting, providing greater accuracy in assessing risk and pricing policies.

This article first appeared on Insurance Edge .

Western Skies MGU Leverages Gradient AI to Grow its SMB Stop-Loss Insurance Market Share

Tue, 09 May 2023 13:47:16 GMT

Rich Medical Data Lake and Predictive Analytics Capabilities of Gradient AI’s SAIL Solution Enable Western Skies to Tap into New Markets

Boston, MA May 9, 2023 - Gradient AI , one of the fastest growing Artificial Intelligence (AI) insurtechs in the industry, today announced that Western Skies , a managing general underwriter (MGU), is using Gradient AI’s SAIL ™ solution to provide stop-loss insurance access to smaller, self-funded groups.

Small and midsize businesses have long desired more affordable, self-funded insurance plans. However, due to limited access to small business claims data that underwriters need to effectively assess group risk, stop-loss insurers have often declined to take on small business clients. This includes Western Skies, which traditionally partners with brokers, general agents, and third-party administrators (TPAs) that represent companies with 150+ employees. Yet, this excludes the large majority of the market, as recent data from the NAICS Association reveals that over 89% of the more than 18 million businesses in the United States operate with fewer than 100 employees.

In addition to expanding its target market to large numbers of smaller employers, Western Skies is also expanding geographically. Having found most of its success in the Southeast, the company is now leveraging SAIL’s vast data sets and capabilities to expand west and northeast, reaching new regions with its services. Western Skies MGU, a division of ManhattanLife — a risk-bearing entity— can now accurately assess risk and compete against larger insurance providers as it taps into new markets.

“We’re excited to partner with Western Skies, offering innovative new insurance opportunities to the small business market,” said Stan Smith, CEO and founder of Gradient AI. “With this stop-loss insurance partnership, everybody wins. Employers and employees can obtain insurance at more competitive rates, and Western Skies and their partnering brokers, agents, and TPAs can now serve a new market segment.”

About Western Skies, MGU - a ManhattanLife Company

As a full-service managing general underwriter specializing in employer funded medical stop-loss solutions, Western Skies enables producers to offer clients both traditional network options and cost-effective, Reference Based Pricing (RBP) reimbursement models. The company offers the value of a direct carrier with the underwriting expertise of an MGU. ManhattanLife is a group of five operating life and health insurance companies: Manhattan Life Insurance Company, ManhattanLife Insurance and Annuity Company, Western United Life Assurance Company, Family Life Insurance Company and Standard Life and Casualty Insurance Company. Since 1850, these companies have provided secure and innovative life and health insurance products and services throughout the United States and Puerto Rico.

About Gradient AI

Gradient AI is a leading provider of proven artificial intelligence (AI) solutions for the insurance industry. Its solutions improve loss ratios and profitability by predicting underwriting and claim risks with greater accuracy, as well as reducing quote turnaround times and claim expenses through intelligent automation. Unlike other solutions that use a limited claims and underwriting dataset, Gradient's software-as-a-service (SaaS) platform leverages a vast dataset comprising tens of millions of policies and claims. It also incorporates numerous other features including economic, health, geographic, and demographic information. Customers include some of the most recognized insurance carriers, MGAs, TPAs, risk pools, PEOs, and large self-insurers across all major lines of insurance. By using Gradient AI's solutions, insurers of all types achieve a better return on risk. To learn more about Gradient, please visit: https://www.gradientai.com .

Media Contacts

Elyse Familant

Results PR

978-376-5446

elysef@resultspr.net

Warp Speed Scotty? No Cap’n We Need WriteSpeed

Thu, 27 Apr 2023 17:24:17 GMT

April 27, 2023

The latest product from Gradient AI is WriteSpeed, which will be of interest to any brands active in the US Workers Comp sector;

Gradient AI , a leading enterprise software provider of artificial intelligence (AI) solutions in the insurance industry, today announced the launch of WriteSpeed , a turnkey solution that allows workers’ compensation insurers to rapidly leverage AI-enabled underwriting, accelerating time to value.

WriteSpeed leverages Gradient AI’s vast industry data lake to deliver a pre-trained AI model that workers’ compensation underwriters can put to work immediately using the easy-to-use WriteSpeed solution web portal. With WriteSpeed, insurers can assess the risk of policy submissions without requiring internal IT resources or customization. The pre-trained model has been trained on millions of policies and delivers significant value in evaluating policy risk, enabling insurers to make informed underwriting decisions and accurately price policies.

Integrating AI into underwriting is often challenging because of the massive amounts of formatted data that AI models require, which insurers often lack. As a result, data preparation can be time-consuming, error-prone, and potentially require third-party experts. Even after data preparation is complete, additional time is needed to train AI models. With WriteSpeed, insurers can bypass these steps and immediately benefit from AI-enabled underwriting. Insurers can also gain additional value when they are ready, by adding their own data to further enhance the accuracy of Gradient AI’s pre-trained model.

“I think WriteSpeed is a great solution. I really like its simplicity and ease-of-use. It enables insurers to start leveraging AI in their underwriting from day one with very little effort,” commented Stu Thompson, CEO of The Builders Group, a self-insured workers’ compensation fund exclusive to the construction trades, and a Gradient AI underwriting and claims management client.

By providing deep insights into underwriting risks, WriteSpeed empowers insurers to price policies more accurately, quote policies they might have declined, and decline policies they might have quoted, all from day one. As a result, insurers can achieve greater operational efficiency and add an entirely new level of insight to their underwriting process. WriteSpeed facilitates rapid return on investment (ROI) without imposing any demands on internal IT resources.

WriteSpeed also puts insurers on an accelerated path to a powerful, fully integrated AI implementation. Its scalability enables insurers to easily move from the web portal to full integration with their insurance operating system, allowing AI to be embedded directly into their operations.

“WriteSpeed is a game changer for businesses looking to leverage the power of AI in their underwriting operations, without the typical data-related hurdles, roadblocks, and delays,” said Stan Smith, founder and CEO of Gradient AI. “With our pre-trained models learning from one of the industry’s largest data lakes, insurers can reap immediate benefits. This helps make AI in underwriting more accessible to businesses that want to get started but may not have the data resources readily available.”

This article first appeared on Insurance Edge .

Gradient AI unveils tech for workers comp underwriting

Wed, 26 Apr 2023 17:10:14 GMT

April 26, 2023

Boston-based software company Gradient AI on Tuesday announced the launch of WriteSpeed, a program that enables workers compensation insurers to use artificial intelligence in underwriting.

WriteSpeed will use Gradient AI’s industry data to deliver a “pre-trained” AI model that workers comp underwriters can put to work immediately using a web portal, assessing the risk of policy submissions without requiring internal resources or customization.

The company said in a statement that the program intends to price policies more accurately and efficiently, and quote policies insurers might have declined.

This article first appeared on Business Insurance .

Gradient AI Introduces WriteSpeed: The Out-of-the-Box AI Solution for Workers’ Compensation Underwriting

Tue, 25 Apr 2023 13:34:44 GMT

Enables Insurers to Improve Workers’ Comp Underwriting Immediately, Bypassing Typical AI Data and Implementation Hurdles

Boston, MA April 25, 2023 - Gradient AI , a leading enterprise software provider of artificial intelligence (AI) solutions in the insurance industry, today announced the launch of WriteSpeed , a turnkey solution that allows workers’ compensation insurers to rapidly leverage AI-enabled underwriting, accelerating time to value.

“WriteSpeed is a game changer for businesses looking to leverage the power of AI in their underwriting operations, without the typical data-related hurdles, roadblocks, and delays,” said Stan Smith, founder and CEO of Gradient AI. “With our pre-trained models learning from one of the industry's largest data lakes, insurers can reap immediate benefits. This helps make AI in underwriting more accessible to businesses that want to get started but may not have the data resources readily available.”

Availability

WriteSpeed has moved from the pilot phase into production and is now available immediately.

Learn more about WriteSpeed at gradientai.com/writespeed .

About Gradient AI

Gradient AI is a leading provider of proven artificial intelligence (AI) solutions for the insurance industry. Its solutions improve loss ratios and profitability by predicting underwriting and claim risks with greater accuracy, as well as reducing quote turnaround times and claim expenses through intelligent automation. Unlike other solutions that use a limited claims and underwriting dataset, Gradient's software-as-a-service (SaaS) platform leverages a vast dataset comprising tens of millions of policies and claims. It also incorporates numerous other features including economic, health, geographic and demographic information. Customers include some of the most recognized insurance carriers, MGAs, TPAs, risk pools, PEOs and large self-insurers across all major lines of insurance. By using Gradient AI's solutions, insurers of all types achieve a better return on risk. To learn more about Gradient, please visit: https://www.gradientai.com .

Media Contacts

Elyse Familant

Results PR (for Gradient AI)

978-376-5446

elysef@resultspr.net

Health insurance captive chooses Gradient AI

Wed, 19 Apr 2023 17:01:47 GMT

April 19, 2023

Solution will help the captive better assess risk

By Ryan Smith

Gradient AI, a software provider for the insurance industry, has announced that Roundstone Insurance, a health insurance captive for small to medium-sized employers, has selected its SAIL solution to help companies better assess risk.

Roundstone is a health benefits captive that specializes in offering cost-effective employee health insurance to small and medium-sized businesses. Such captives have become increasingly popular in recent years for providing smaller companies with the same insurance purchasing power enjoyed by larger corporations.

Many businesses that join Roundstone come from traditional insurance companies that do not share employers’ own claims history transparently with the employer, Gradient AI said. SAIL closes this information gap.

Roundstone selected Gradient AI’s SAIL product for its machine learning capabilities and its ability to access large amounts of de-identified data to assess risk for member organizations. SAIL can utilize prescription, medical and lab data at scale to inform insurance decisions.

Using SAIL’s data analytics and AI features, Roundstone can assess specific group risks and drivers with more granularity, Gradient AI said. This knowledge helps Roundstone’s underwriters to make more informed decisions when quoting new business.

“Gradient AI enables us to get an objective snapshot projection of claims within minutes of submission,” said Bruce Dunham, underwriting manager at Roundstone. “More importantly, we get the critical information we need without conducting time-consuming personal health questionnaires, which can be tedious, error-prone, and viewed by employees as intrusive.”

“We are thrilled that SAIL is proving to be an effective tool for Roundstone at both an employer and employee level,” said Stan Smith, founder and CEO of Gradient AI. “We look forward to continuing this relationship and supporting Roundstone’s mission to provide affordable, quality healthcare plans to companies of all sizes.”

Last month, Gradient AI announced that The Provant Group and The Builders Group had chosen the SAIL solution to streamline their operations.

This article first appeared on Insurance Business .

Roundstone Insurance selects Gradient AI’s solution for risk assessment

Wed, 19 Apr 2023 16:56:10 GMT

April 19, 2023

Roundstone Insurance has selected Gradient AI’s SAIL solution to help companies assess risk and afford “high-quality” healthcare plans.

Ohio-based Roundstone is a health insurance captive of small- to medium-sized employers. Gradient AI is a Boston-headquartered AI software provider for the insurance industry.

Gradient AI’s SAIL solution is designed to close the information gap that is created when traditional insurance companies do not share employers’ claims history.

Roundstone says it chose Gradient AI’s SAIL solution for Roundstone’s group health underwriting. This will help it to access de-identified data to assess risk for member organisations. The solution’s AI and data analysis will help Roundstone’s underwriters quote new businesses.

In addition,Gradient AI’s SAIL solution will be able to leverage prescription, medical and lab data to inform predictions.

Bruce Dunham, underwriting manager at Roundstone says: “Gradient AI enables us to get an objective snapshot projection of claims within minutes of submission.”

He adds: “More importantly, we get the critical information we need without conducting time-consuming personal health questionnaires, which can be tedious, error-prone, and viewed by employees as intrusive.”

This article first appeared on CaptiveInsuranceTimes .

Roundstone Insurance Selects Gradient AI’s SAIL Solution to More Effectively Assess Risk and Enhance Underwriting Process

Tue, 18 Apr 2023 14:05:04 GMT

AI-powered SAIL Helps Roundstone Offer Competitive Healthcare Plans to SMBs

Boston, MA, April 18, 2023 - Gradient AI, a leading enterprise artificial intelligence (AI) software provider for the insurance industry, today announced that Roundstone Insurance, a health insurance captive for small- to medium-sized employers, has selected its SAIL ™ Solution to help companies better assess risk and afford high-quality healthcare plans.

Roundstone is a rapidly growing health benefits captive that specializes in offering cost-effective employee health insurance to small and mid-sized businesses. With its self-funded group captive solution, member organizations save on average 20 percent annually. Health insurance captives such as Roundstone Insurance have become increasingly popular in recent years by providing smaller companies with the same insurance purchasing power as larger enterprises. Through self-funded captive insurance models, these companies can receive similar coverage and rates to larger enterprises, resulting in direct cost savings for both employers and employees.

Many businesses that join Roundstone come from traditional insurance companies that do not share employers’ own claims history transparently with the employer. To meet the demands of its growing customer base, Roundstone continues to innovate and enhance its underwriting process to gain a more complete understanding of a group's risk profile, especially when previous claims history is not available. Gradient AI’s SAIL Solution closes this informational gap for Roundstone and its advisor network.

After evaluating several options, Roundstone selected Gradient AI’s SAIL Solution for Group Health Underwriting, due to its powerful machine learning capabilities and its ability to access vast amounts of de-identified data to comprehensively assess risk for member organizations. SAIL’s unique ability to leverage prescription, medical, and lab data at scale to inform predictions is a key reason for Roundstone’s choice. Gradient AI is the only company in the group medical insurance market that can offer this breadth and depth of data from a single source.

With SAIL’s data analytics and AI features, Roundstone is now able to visualize and assess specific group risks and drivers with finer granularity. This knowledge helps Roundstone’s underwriters to sharpen their insights when quoting new business. “Gradient AI enables us to get an objective snapshot projection of claims within minutes of submission,” said Bruce Dunham, underwriting manager at Roundstone. “More importantly, we get the critical information we need without conducting time-consuming personal health questionnaires, which can be tedious, error-prone, and viewed by employees as intrusive.”

Stan Smith, founder and CEO of Gradient AI commented, “We are thrilled that SAIL is proving to be an effective tool for Roundstone at both an employer and employee level. We look forward to continuing this relationship and supporting Roundstone’s mission to provide affordable, quality healthcare plans to companies of all sizes.”

About Roundstone

Roundstone was founded in 2003 with a vision of giving small and mid-size businesses a proven strategy for affordable, high-quality employee health benefits through our self-funded group captive solution. Roundstone is headquartered in Lakewood, Ohio with representatives throughout the United States. Connect with us on our blog , LinkedIn and Twitter (@captiveinsurer)

About Gradient AI

Gradient AI is a leading provider of proven artificial intelligence (AI) solutions for the insurance industry. Its solutions improve loss ratios and profitability by predicting underwriting and claim risks with greater accuracy, as well as reducing quote turnaround times and claim expenses through intelligent automation. Unlike other solutions that use a limited claims and underwriting dataset, Gradient's software-as-a-service (SaaS) platform leverages a vast dataset comprising tens of millions of policies and claims. It also incorporates numerous other features including economic, health, geographic, and demographic information. Customers include some of the most recognized insurance carriers, MGAs, TPAs, risk pools, PEOs, and large self-insurers across all major lines of insurance. By using Gradient AI’s solutions, insurers of all types achieve a better return on risk. To learn more about Gradient, please visit https://www.gradientai.com .

Media Contacts

Elyse Familant

Results PR (for Gradient AI)

978-376-5446

elysef@resultspr.net

From Data to Decision Making: How AI Is Streamlining Claims Operations

Fri, 14 Apr 2023 19:55:04 GMT

April 14, 2023

A survey finds one-third of claimants are unhappy with their experience — 60% link that dissatisfaction to speed. AI might be the missing link between customer expectations and what you deliver.

Insurance organizations handle more data than ever, but using that data efficiently isn’t always easy. That’s why many are turning to artificial intelligence (AI) to accelerate workflows and get the right insights to the right adjusters. These capabilities promise lower costs but also create a faster path to claims resolution, which is increasingly important: A recent survey found that one-third of claimants weren’t satisfied with their experience — and slow claim speed was cited by 60% of those participants.

“We handle about 5,000 new claims each year,” says Ann Latimer, senior vice president, head of claims operations at Signal Mutual Indemnity Association, the premier Longshore mutual in the United States. “And with every new claim, we need to figure out the severity quickly in order to route it to the right adjuster. So recently, we began using artificial intelligence from Gradient AI to help improve processes, such as claims triage.”

As helpful as AI is for expediting claims triaging and providing critical insights, getting adjusters — especially the more seasoned ones — on board can present a cultural challenge.

Ann Latimer, senior vice president, head of claims operations at Signal Mutual Indemnity Association

Winning the trust of adjusters

Adjusters are highly skilled at making decisions, so giving them more than just the data is important. And that requires going a step further to provide the “why” behind every insight so they aren’t trusting blindly.

“We realized early on that adjusters need more than just AI-generated recommendations,” says Latimer. “Adjusters need to understand the logic behind those insights to build trust and confidence and ultimately start using the tools consistently.”

An easy point of entry for claims management professionals considering AI is claims triage, according to Latimer. Triage is a good first step because it takes only a few keen internal players to get started, and the insurer can build out new capabilities from there.

Building from the right starting point

Latimer underscores that need to build trust in the system. The adjuster should be able to feel comfortable that predictions are consistent with their own view of the claim, and not feel the analysis is arbitrary or otherwise in error. She recommends measures like focus groups to ensure buy-in, and that the output makes sense to the adjusters.

And in the future, Signal plans to look for new opportunities to leverage AI to improve speed, close claims faster, and reduce the cost of claims for members. Latimer notes the value of measuring these key metrics to record improvements. “It’s so important to continually evaluate your process,” says Latimer. “You don’t want to just assume that because you’re doing one thing this year, you want to keep doing the same thing the next. Continue to evaluate and work with a supportive partner who can help you on that journey.”

To hear the full interview with Ann Latimer in an exclusive podcast, click here .

This article first appeared on PropertyCasualty360 .

Leveraging AI to Upskill Employees

Mon, 03 Apr 2023 13:12:50 GMT

April 3, 2023

Many insurers are turning to AI technology to supplement their current employee education and institutional knowledge management strategies.

By Stan Smith

Insurance companies around the globe are facing one of their biggest challenges to date: a workforce shortage, magnified by the Baby Boomer generation’s retirement, the migration of skilled labor during the Great Resignation and shrinking pools of aspiring insurance professionals among Millennials and Generation Z.

According to the U.S. Bureau of Labor Statistics and the National Association of Mutual Insurance Companies , 50% of the current insurance workforce will retire in the next 15 years. In many cases, the highly nuanced knowledge these professionals have developed over the years will retire with them. This “retirement brain drain” is exacerbated by supervisors finding themselves increasingly short on time because they need to do more with fewer resources. Due to the considerable amount of time it takes to manage key tasks and processes, they have little time to train new employees, making this challenge more acute.

So, what can you do to safeguard your organization’s most valuable data, including the information that only lives in the heads of your most seasoned professionals? Hands-on training and job shadowing are incredibly important and have been used for decades to train new employees. However, they can only do so much to ensure relevant industry knowledge is preserved and passed on to the next generation of workers, especially given the high retirement rate of many veteran insurers.

To combat this growing problem, many insurers are turning to AI technology to supplement their current employee education and institutional knowledge management strategies. Specifically, they are training AI models with terabytes of data from industry data lakes along with their own internal datasets. This enables AI models to learn from a wide variety of scenarios insurers might encounter when underwriting or resolving claims. Many AI solutions now even ingest and learn from unstructured data—like the notes adjusters have made over the years about specific claims.

AI can serve as a virtual digital assistant, automatically scanning all aspects of claims data and updates to identify attributes or events that may affect claims’ cost and duration. Unlike a human, the digital assistant can work 24x7, read and understand millions of policies and claims, has total recall and never gets tired. It can detect a myriad of factors such as updated notes, comorbidities, psychosocial factors, recent medical tests and diagnoses that may affect a claim’s severity. Armed with this insight, the digital assistant can triage and prioritize claims, reducing claims cycle times and losses. It directs the adjuster to what needs the most attention and when to act to ensure maximum claimant satisfaction and profitability. Similarly, on the underwriting side, a virtual digital assistant can examine a policy and use its learnings from millions of other policies and third-party data to give underwriters deeper insight into policy risks to help them price more accurately.

The virtual digital assistant can give both underwriters and adjusters more confidence in making decisions. It provides valuable guardrails for the new underwriter or adjuster, and, for the experienced professional, AI amplifies what they already know and do, enabling them to accomplish more at a higher quality level with less work and time.

AI Upskills the Insurance Industry

It’s encouraging and frankly exciting to see the 500-year-old insurance industry starting to lean into AI and other supportive technologies the same way they embraced digital transformation. We are now seeing AI grow in its adoption exponentially in both underwriting and claims management.

In a recent webinar , leaders from both MEMIC and Builders Mutual Insurance shared how their companies have benefited from using AI technology to upskill and knowledge-share with newer employees. Greg Jamison, senior vice president of underwriting at MEMIC , explained how AI models have helped the underwriting side of the business build dashboards to better understand and quantify underwriting risks. He remarked, “We leverage not only our own internal data but Gradient AI’s [industry data lake].” Ken Bunn, vice president of claims at Builders Mutual Insurance , found that AI enables new adjusters to benefit from models that have learned from millions of past claims and “provides guardrails for our newer folks. For example, new adjusters can learn from the predictions that the models make to help them better assess a claim’s severity and gain insight into how to properly reserve a specific type of claim."

Using AI Models to Upskill Insurance Professionals

AI technology offers an increase of depth of training and knowledge-sharing beyond because it leverages expanded access to datasets. As more companies make AI part of their business operations, here’s what I expect they’ll find:

AI leverages institutional data to mitigate lack of experience. AI captures institutional knowledge, learning from past claim decisions and resolutions, allowing new insurers to perform at a level commensurate with their more experienced co-workers.

Less data gets lost in the shuffle. Traditional job shadowing can be time-consuming, prone to misunderstanding and data loss. When trainees leverage AI unified databases, they have access to a single source of truth, resulting in more accurate, consistent and profitable decisions.

Industry data lakes can help insurers grow in new markets. When AI insurance models are trained using industry data lakes containing multiple insurers, geographies and industry segments, insurers can expand into new industries and geographies with confidence. By leveraging models trained on actual loss histories in new markets, insurers can quote new policies and manage claims more accurately.

AI training tools create a better working environment for younger generations. The insurance industry needs to make the industry more interesting and attractive to younger generations. AI technology has the potential to reform the insurance industry’s staid reputation and position it as a technology leader, and help attract and retain younger staff.

Stan Smith is founder and CEO of Gradient AI, which has used AI to revolutionize the underwriting and claims management process.

Previously, Smith held founding- or executive-level roles for multiple startup companies, including MatrixOne, Agile Software and OpenRatings. Smith also led the development of several patents, including technology that predicts bankruptcies and technologies that enhance performance management.

He obtained his bachelors degree from Dartmouth College.

This article first appeared on Insurance Thought Leadership .

National brokerage taps Gradient AI's analytics solution

Thu, 30 Mar 2023 13:21:16 GMT

March 30, 2023

Product to allow the brokerage to customize insurance policies

By Ryan Smith

Insurance software provider Gradient AI has announced that The Provant Group, a national commercial insurance brokerage, has chosen Gradient AI’s SAIL product to quantify and gain deeper insights into group health risk.

SAIL will allow the brokerage to customize insurance policies to fit the unique needs of each customer, Gradient AI said. SAIL’s advanced AI capabilities, predictive analytics and extensive medical database will allow Provant to conduct a more thorough analysis of group health risks.

This insight will provide clients with greater control over their healthcare coverage at rates usually available only to larger employers, Gradient AI said.

“We help our customers move from a reactive to a proactive state thanks to SAIL’s ability to eliminate blind spots, predict risks and uncover cost savings opportunities,” said Vince DiBenedetto, CEO of The Provant Group. “By leveraging SAIL, we’re able to offer our clients a level of transparency and insights into their group health risk that was previously unattainable. As a result, we can create tailored group health solutions that meet the specific requirements of each client’s employee group.”

“We are thrilled that Provant is using Gradient AI’s SAIL solution to assist their small business clients in gaining a more complete understanding of their group risk,” said Stan Smith, founder and CEO of Gradient AI. “In the past, these businesses lacked transparency in this area, leaving them at a disadvantage when working with larger carriers.

“By leveraging the latest in AI, data analytics, and medical health data, Provant is addressing a previously unmet need in the small business market, enabling customers to obtain the best value and plans that are tailored to their specific requirements. This level of personalization has the potential to revolutionize the way companies approach employee health benefits, and we’re proud to be at the forefront of this shift.”

Earlier this month, Gradient AI announced that workers’ compensation provider The Builders Group had selected Gradient AI’s products to streamline its claims process. Managing general underwriter Medical Risk Managers recently implemented SAIL to offer medical stop-loss coverage to small, self-funded groups.

This article first appeared on Insurance Business .

Gradient AI’s SAIL Solution Gives the Provant Group Greater Insights into Group Health Risks

Tue, 28 Mar 2023 13:51:46 GMT

Enables Tailored Health Plans, Streamlined Cost Savings and Advanced Risk Management for Brokerage’s Clients

Boston, MA, March 28, 2023 - Gradient AI , a leading enterprise software provider of artificial intelligence (AI) solutions in the insurance industry, today announced that The Provant Group , a national commercial insurance brokerage, has chosen Gradient AI’s SAIL™ solution to quantify and gain deeper insights into group health risk enabling the brokerage to customize insurance policies that cater to the unique needs of each customer.

The Provant Group strives to deliver personalized services that are characteristic of a boutique firm, while also leveraging sophisticated analytics and methods that are typical of larger operations. To offer the best possible insurance policies and pricing to its clients, Provant has implemented Gradient AI's SAIL group health underwriting solution. By leveraging the solution’s advanced AI capabilities, predictive analytics, and extensive medical database, Provant can now conduct an even more thorough analysis of group health risks than ever before.

This insight enables Provant to gain a deeper understanding of a client’s current and future group risk and empowers the firm to offer more personalized and optimized insurance policies tailored to specific client requirements. This in turn provides clients with a greater degree of control over their healthcare coverage at rates typically only available to larger employers.

“We help our customers move from a reactive to a proactive state, thanks to SAIL’s ability to eliminate blind spots, predict risks and uncover cost savings opportunities,” said Vince DiBenedetto, CEO of The Provant Group. "By leveraging SAIL, we’re able to offer our clients a level of transparency and insights into their group health risk that was previously unattainable. As a result, we can create tailored group health solutions that meet the specific requirements of each client's employee group."

"We are thrilled that Provant is using Gradient AI's SAIL solution to assist their small business clients in gaining a more complete understanding of their group risk," said Stan Smith, founder and CEO of Gradient AI. “In the past, these businesses lacked transparency in this area, leaving them at a disadvantage when working with larger carriers. By leveraging the latest in AI, data analytics, and medical health data, Provant is addressing a previously unmet need in the small business market, enabling customers to obtain the best value and plans that are tailored to their specific requirements."

Smith added, “This level of personalization has the potential to revolutionize the way companies approach employee health benefits, and we're proud to be at the forefront of this shift.”

About Gradient AI

Gradient AI is a leading provider of proven artificial intelligence (AI) solutions for the insurance industry. Its solutions improve loss ratios and profitability by predicting underwriting and claim risks with greater accuracy, as well as reducing quote turnaround times and claim expenses through intelligent automation. Unlike other solutions that use a limited claims and underwriting dataset, Gradient's software-as-a-service (SaaS) platform leverages a vast dataset comprised of tens of millions of policies and claims. It also incorporates numerous other features including economic, health, geographic and demographic information. Customers include some of the most recognized insurance carriers, MGAs, TPAs, risk pools, PEOs and large self-insureds across all major lines of insurance. By using Gradient AI's solutions, insurers of all types achieve a better return on risk. To learn more about Gradient, please visit: https://www.gradientai.com .

Media Contacts

Elyse Familant

Results PR (for Gradient AI)

978-376-5446

elysef@resultspr.net

CCMSI Saves Employers $300 Million in Workers’ Compensation Claims with AI

Thu, 09 Mar 2023 15:42:18 GMT

March 9, 2023

By Gia Sawko

Gradient AI’s claims solution enables teams to triage claims proactively by automatically predicting the risk level of new and existing claims.

Cannon Cochran Management Services, Inc. ( CCMSI ) is the largest independent third-party administrator (TPA) in the United States, providing innovative risk management solutions to thousands of companies. From the moment CCMSI opens a claim, it works toward a successful close. That’s because it views every open claim as an open door to costs that can quickly get out of control. CCSMI’s focus on innovation and its deep level of claims experience have led to a solid reputation for significant claim reductions with a positive impact on aged-loss portfolios and the management of new claims.

An early adopter of artificial intelligence (AI) , the company developed an AI-driven intervention model that flags at-risk claims for adjusters. As its AI initiative grew, CCMSI turned to Gradient AI for a solution that could provide both the claims administration team and their clients with up-to-the-minute information and cost estimates based on the most recent claims information.

AI Becomes MVP Against Converging Industry Challenges

As part of its quest to get injured workers back to work quickly, CCMSI continually focuses on improving its claims process, and with good reason. According to a recent EY survey , 87% of customers say effective claims processing is one of the most important insurance operations. However, until recently, many insurance companies handled claims processing tasks manually, resulting in too much time, labor, and money spent on the process. Both employers and injured workers want workers’ compensation claims to be resolved quickly and accurately so that the worker can return to work as soon as possible. If they don’t get the appropriate level of service, employers may move on to a new insurer.

In addition to claims process inefficiencies, many insurers are grappling with the talent gap brought on by a rapidly aging and retiring workforce. According to the U.S. Bureau of Labor Statistics , 50% of the current insurance workforce will retire in the next 15 years, leaving more than 400,000 open positions unfilled.

Building an AI-Driven Claims Solution

As part of its claims enhancement program, CCMSI first studied the impact of co-morbidity factors on claims. The company soon realized that this alone added an entirely new level of claims analysis complexity. Additionally, CCMSI realized that many other factors, captured in the adjusters’ notes, also had an impact on claims but could not be mined for valuable information.

CCMSI set out to address these issues by expanding its system’s data fields to track almost 80 additional claim elements to leverage the predictive value of this additional data to resolve claims sooner. These new fields allowed CCMSI’s adjusters to capture and quantify a variety of other factors such as a claimant’s mood during a conversation, enthusiasm for returning to work, and punctuality at an appointment, among other elements related to the claimant’s case.

CCMSI turned to Gradient AI, a leading provider of proven AI solutions for the insurance industry, to leverage the value of this data with the goal of improving claims processing speed and accuracy. Using the data elements above, algorithms score each claim as high, medium, or low risk. The model then uses the data to assist adjusters, supervisors, and clients in managing claims and improving communications.

Gradient AI’s claims solution enables teams to triage claims proactively by automatically predicting the risk level of new and existing claims. The solution not only learns from an insurance company’s own data but from a high-resolution federated database with tens of millions of observations and ten times (10x) the features of traditional legacy assessment processes. Furthermore, Gradient AI’s solution leverages Natural Language Processing to extract valuable insights from adjusters’ notes.

WITH GRADIENT AI, CCMSI QUICKLY ADDRESSES CLAIMS WITH HIGHER POTENTIAL SEVERITY, ENSURING APPROPRIATE CARE FOR INJURED WORKERS WHILE BETTER MANAGING COSTS AND REDUCING RISK FOR THE COMPANY’S CLIENTS.

CCMSI’s claims system notifies adjusters and supervisors early in the life of a claim of potential injury severity and presents them with a claim risk profile. Adjusters can make referrals for additional services, adjust reserves, notify the client, or take other actions. Injured employees can now return to work more quickly, minimizing serious onsite disruptions and economic impact on the company.

AI Saves CCMSI Clients $300 Million

Gradient AI has helped CCMSI reduce workers’ compensation claims costs by about 10%, resulting in savings of more than $300 million in savings for its clients. These findings were verified by an independent study, which conducted an analysis of indemnity claims over a six-year period.

The table below summarizes the positive results that CCMSI achieved by leveraging Gradient AI in its operations:

In the future, CCMSI anticipates improving the quality of projections and reducing the severity and duration of claims events for clients and their employees.

“We are realizing tremendous value from integrating our AI claims models into the Gradient AI solution,” said Skip Brechtel, executive vice president, of CCMSI. “As a TPA, a key goal is to support and deliver exceptional value to our clients. By investing in innovative technology, we’ve been able to improve the claims journey for our customers. And our clients are thrilled with the results because of the increased capacity to triage incoming claims, decrease processing times and reduce the costs of claims.”

AI will continue to be a key enabler in the workers’ comp insurance industry. The challenges of mitigating employee attrition, defending against intensifying competition, and maintaining a healthy bottom line despite increasing pressures on profitability can all be addressed through the use of AI. CCMSI is leading the industry in embracing AI to meet the challenges of claims management today and into the future.

Visit AITechPark for cutting-edge Tech Trends around AI, ML, Cybersecurity, along with AITech News, and timely updates from industry professionals!

Gia Sawko

Gia has 30 years of experience in the Property Casualty Claims Industry. As Gradient AI’s Senior Product Manager of Claims, Gia leads product development for its claims management solution leveraging AI and machine learning to drive better outcomes for claim operations. Her passion and focus are empowering adjusters with the benefits of machine learning to reduce claim duration, and cost and improve operational efficiency. Gia’s career started in Personal Auto but also includes Commercial Auto, Commercial Liability, and Workers' Compensation as an adjuster and supervisor. She has also served as Whole Foods Market’s Managing Director of Safety, Claims, and IBNR. This unique opportunity gave Gia a powerful trio of skills including loss prevention, risk management, and claim financials. Following a successful career at Whole Foods, Gia spearheaded solution development as Vice President of Business Impact at Gallagher Bassett leveraging her unique skills to drive business value from technology and artificial intelligence.

This article appeared first on AI Tech Park .

Gradient Provides Medical Risk Managers (MRM) Customers Greater Insurance Security

Wed, 08 Mar 2023 19:38:47 GMT

March 8, 2023

Please read this article in CIO Applications .

The Builders Group taps into AI-powered solution

Wed, 08 Mar 2023 18:35:06 GMT

March 8, 2023

By Lyle Adriano

Insurance software provider Gradient AI announced that The Builders Group (TBG) is using its solutions to streamline claims and to better evaluate underwriting risks.

TBG offers workers’ compensation policies for contractors, builders, remodelers, subcontractors and suppliers. The group’s self-insured and member-owned workers’ compensation fund counts nearly 700 member companies.

In a release, TBG explained that it selected Gradient AI to reduce the cost of claims and improve its underwriting risk assessment. The solution is AI-powered, which means it learns from both TBG’s data and Gradient AI’s industry data with “tens of millions” of claims and underwriting records.

On the claims side, Gradient AI’s solution enables adjusters to conduct earlier claim assessments, evaluate risk KPIs, and drive better outcomes. And on the underwriting side of things, Gradient AI will help TBG assess risk more accurately for workers’ compensation policies.

Gradient AI can also integrate with the cloud-based Origami Risk insurance platform, a claims management solution for consolidating claims data and streamlining workflow processes. This feature enables TBG to work more efficiently.

“We selected Gradient AI because of its vast industry dataset containing tens of millions of claims and policies, excellent customer service and extensive experience in AI and insurance,” said TBG CEO Stu Thompson. “As a result, our underwriters and claims adjusters can perform their jobs more proficiently. And because Gradient AI’s solutions seamlessly integrate with the Origami Risk insurance platform, our users will be able to obtain key decision metrics within the same platform that they are using to quote policies and manage claims.”

TBG also hopes to utilize Gradient AI’s solution to capture and retain the expertise of seasoned talent, to prevent a potential talent gap.

“The Builders Group, known for its technology innovation, is using AI to better predict risk and improve efficiencies for both workers’ compensation underwriting and claims,” said Gradient AI founder and CEO Stan Smith. “With Gradient AI, TBG can leverage our industry data lake and their own data to gain deeper insight into claims and underwriting risks. We are thrilled to be working with TBG to help it manage claims more efficiently, speed quote turnaround times, and most importantly, deliver better outcomes and experiences for its members and their employees.”

Last week, managing general underwriter Medical Risk Managers (MRM) announced that it is also using Gradient AI’s solutions . Specifically, MRM is using Gradient AI’s SAIL product to offer stop-loss insurance to small, self-funded groups.

This article first appeared on Insurance Business .

The Builders Group Leverages Gradient AI to Streamline Claims and Underwriting for Workers' Compensation Insurance

Tue, 07 Mar 2023 14:30:18 GMT

Delivers Enhanced Claims Management and More Accurate Underwriting

Boston, MA, March 7, 2023 - Gradient AI , a leading enterprise software provider of artificial intelligence (AI) solutions in the insurance industry, today announced that The Builders Group (TBG) is using its solutions to streamline claims and to better evaluate the risks associated with underwriting workers’ compensation insurance.

TBG provides workers’ compensation policies for contractors, builders, remodelers, subcontractors and suppliers. Self-insured and member-owned, TBG’s workers’ compensation fund has about 700 member companies. The company’s goal is to offer affordable, high-quality workers’ comp that prioritizes medical care while delivering speed and accuracy of claims settlements by leveraging the latest technologies.

To enhance support for its claims and underwriting operations, TBG chose Gradient AI to reduce the cost of claims and improve TBG’s underwriting risk assessment. The solution’s AI models learn from both TBG’s data and Gradient AI’s industry data lake containing tens of millions of claims and underwriting records. On the claims side, Gradient AI enables adjusters to conduct earlier claim assessments, evaluate risk KPIs, and drive better outcomes. On the underwriting side, Gradient AI helps TBG assess risk more accurately for workers' compensation policies.

Gradient AI also integrates with Origami Risk insurance platform, a cloud-based, end-to-end claims management solution for consolidating claims data and streamlining workflow processes which TBG is in the process of implementing. Gradient AI’s integration with Origami will allow TBG to work more efficiently by seeing key decision metrics such as loss predictions and risk rankings right at the point of decision.

“We selected Gradient AI because of its vast industry dataset containing tens of millions of claims and policies, excellent customer service and extensive experience in AI and insurance,” said Stu Thompson, CEO of TBG. “As a result, our underwriters and claims adjusters can perform their jobs more proficiently. And because Gradient AI’s solutions seamlessly integrate with the Origami Risk insurance platform, our users will be able to obtain key decision metrics within the same platform that they are using to quote policies and manage claims.”

Like other insurers, TBG faces a generation of experienced underwriters and adjusters who will soon retire. To fill the potential talent gap, TBG is using Gradient AI’s solution to capture and retain the expertise of seasoned talent with decades of industry experience to help new employees become productive more quickly.

“The Builders Group, known for its technology innovation, is using AI to better predict risk and improve efficiencies for both workers’ compensation underwriting and claims,” said Stan Smith, founder & CEO, Gradient AI. “With Gradient AI, TBG can leverage our industry data lake and their own data to gain deeper insight into claims and underwriting risks. We are thrilled to be working with TBG to help it manage claims more efficiently, speed quote turnaround times, and most importantly, deliver better outcomes and experiences for its members and their employees.”

About Gradient AI

Gradient AI is a leading provider of proven artificial intelligence (AI) solutions for the insurance industry. Its solutions improve loss ratios and profitability by predicting underwriting and claim risks with greater accuracy, as well as reducing quote turnaround times and claim expenses through intelligent automation. Unlike other solutions that use a limited claims and underwriting dataset, Gradient's software-as-a-service (SaaS) platform leverages a vast dataset comprised of tens of millions of policies and claims. It also incorporates numerous other features including economic, health, geographic and demographic information. Customers include some of the most recognized insurance carriers, MGAs, TPAs, risk pools, PEOs and large self-insureds across all major lines of insurance. By using Gradient AI's solutions, insurers of all types achieve a better return on risk. To learn more about Gradient, please visit: https://www.gradientai.com .

Media Contacts

Elyse Familant

Results PR (for Gradient AI)

978-376-5446

elysef@resultspr.net

Medical Risk Managers Expands Stop-Loss Insurance to Small Businesses with Gradient AI

Tue, 28 Feb 2023 15:22:13 GMT

Greater Access to Key Claims and Underwriting Data Enables Insurer to Offer Stop Loss Solutions to Small, Self-Funded Businesses

Boston, MA February 28, 2023 - Gradient AI , a leading enterprise software provider of artificial intelligence (AI) solutions in the insurance industry, announced today that Medical Risk Managers (MRM) , the largest full service Managing General Underwriter (MGU) in the nation, is using Gradient AI’s SAIL™ solution to offer stop loss insurance to small, self-funded groups.

As healthcare costs continue to rise, more small and midsize employers are turning to self-funded insurance plans. To offset the financial risks of these plans, employers often buy high-deductible stop-loss policies to cover the risk of unexpected catastrophic claims. In the past, mostly large companies have self-insured since it has been difficult for small companies to obtain the claims data essential to purchase stop-loss insurance.

For over 35 years, Medical Risk Managers, Inc. has underwritten stop loss insurance on behalf of its insurance carrier partners. In response to increased client requests and the changing business landscape, MRM is seeking to assist more small businesses with self-funded solutions. This led MRM to develop its small business underwriting expertise and to expand its underwriting and pricing capabilities to specifically serve this market. After evaluating multiple options, MRM chose Gradient AI’s SAIL solution based on its advanced analytics capabilities as well as its breadth and depth of medical, lab, and prescription data.

“With the increasing cost of healthcare, we are finding that self-funded plans with stop loss insurance have become very popular with smaller groups,” explained Chris Rice, president, Medical Risk Managers. “Self-funded plans offer greater flexibility, control, and cost-saving opportunities. However, until now, we were not able to offer these plans to smaller businesses due to the lack of claims history available. Now with access to robust data and analytics that Gradient AI’s SAIL solution provides, we can quote business that was simply not possible before.”

With Gradient AI’s vast dataset and machine learning capabilities, MRM can evaluate group health risk at a deeper level. Combined with its own expertise, MRM can create highly customized and cost-effective self-insured solutions, enabling employers who may have previously been unable to self-insure to now do so. By leveraging Gradient AI's SAIL medical underwriting solution, MRM is able to obtain a complete and accurate picture of the health profile of a particular group.

“MRM’s ability to now offer stop-loss insurance to small businesses has expanded its market while enabling these businesses and their employees, and families to enjoy better, more cost-effective healthcare coverage. We are delighted to be part of the solution that makes all of this possible,” said Stan Smith, CEO and founder of Gradient AI. “As MRM continues to grow, we are committed to supporting their mission of offering stop loss coverage at any deductible level, over any network, and in any zip code nationwide.”

About Gradient AI

Gradient AI is a leading provider of proven artificial intelligence (AI) solutions for the insurance industry. Its solutions improve loss ratios and profitability by predicting underwriting and claim risks with greater accuracy, as well as reducing quote turnaround times and claim expenses through intelligent automation. Unlike other solutions that use a limited claims and underwriting dataset, Gradient's software-as-a-service (SaaS) platform leverages a vast dataset comprised of tens of millions of policies and claims. It also incorporates numerous other features including economic, health, geographic and demographic information. Customers include some of the most recognized insurance carriers, MGAs, TPAs, risk pools, PEOs and large self-insureds across all major lines of insurance. By using Gradient AI's solutions, insurers of all types achieve a better return on risk. To learn more about Gradient, please visit: https://www.gradientai.com .

Media Contacts

Elyse Familant

Results PR (for Gradient AI)

978-376-5446

elysef@resultspr.net

Gradient AI Closes 2022 with 100+ Customers and 100% Year-Over-Year Growth

Wed, 18 Jan 2023 14:32:42 GMT

Caps Off Year of Record Growth Solidifying a Leadership Position as Provider of AI Solutions for Insurance Underwriting and Claims

Boston, MA, January 18, 2023 - Continuing momentum from a successful H1 2022 , Gradient AI , a leading enterprise software provider of artificial intelligence (AI) solutions in the insurance industry, today announced that it closed out 2022 with more than 100 customers and well over 100% year-over-year growth. The company has experienced four consecutive quarters of record growth, capping off a year of unprecedented success.

“To be selected by many leading insurance providers serves as testimony to our team’s relentless commitment to delivering AI-powered solutions that drive meaningful and positive outcomes,” said Stan Smith, Gradient AI’s founder and CEO. “This significant market adoption has propelled us into a leadership position in providing underwriting and claims solutions for the insurance industry. We expect continued momentum in 2023 as we further innovate and create compelling solutions for our markets.”

Gradient AI’s H2 2022 milestones include:

Revenue Growth, Customers, Staffing

In 2022, the company grew its Q3 and Q4 revenue by more than 100% over the same period in 2021, acquiring new accounts to end the year with over 100 customers. These additions are across its multiple lines of business including Property and Casualty, Workers’ Compensation, and Group Health. To meet this strong demand, the company grew its workforce which now exceeds 100 employees and continues to look for talent with positions open in engineering, field operations, marketing and finance.

Acquisitions and Partnerships

In October 2022, Gradient AI acquired Prognos Health’s analytics business underwriting unit, providing access to an extensive collection of integrated medical records comprised of 325 million de-identified US patients as well as lab data, which has been a missing factor for many insurers when predicting group risk. Gradient AI is uniquely positioned in its ability to offer healthcare insurers improved risk assessment through access to medical, prescription and lab data on a single platform, improving underwriting accuracy.

Gradient AI also has partnerships with leading insurtech companies that integrate its technology into their platforms, including Guidewire, Origami Risk, Duck Creek, and Socotra. As a result, platform providers’ clients benefit from a combined value-added, end-to-end solution, enriched by AI at the point of decision.

Looking Ahead

Despite many experts prognosticating challenging economic headwinds, Gradient AI projects strong growth ahead as insurers look to bolster their core underwriting and claims operations. This is driving the industry to seek solutions that leverage data and AI to amplify staff intelligence and operational efficiency. Recent customer successes include over $300 million in savings for a customer’s clients and over 100% revenue growth as a direct result of implementing Gradient AI’s solutions.

Stan Smith added, “AI is also enabling insurers to meet one of their greatest challenges-- how to mitigate the ever-growing talent exodus created by the Great Resignation, particularly as the baby boomer generation retires.”

About Gradient AI

Gradient AI is a leading provider of proven artificial intelligence (AI) solutions for the insurance industry. Its solutions improve loss ratios and profitability by predicting underwriting and claim risks with greater accuracy, as well as reducing quote turnaround times and claim expenses through intelligent automation. Unlike other solutions that use a limited claims and underwriting dataset, Gradient's software-as-a-service (SaaS) platform leverages a vast dataset comprised of tens of millions of policies and claims. It also incorporates numerous other features including economic, health, geographic and demographic information. Customers include some of the most recognized insurance carriers, MGAs, TPAs, risk pools, PEOs and large self-insureds across all major lines of insurance. By using Gradient AI’s solutions, insurers of all types achieve a better return on risk. To learn more about Gradient, please visit: https://www.gradientai.com .

###

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Elyse Familant

elysef@resultspr.net

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What P&C AI lessons can be applied to medical stop loss

Thu, 12 Jan 2023 21:19:28 GMT

January 12, 2023

By Brett Heineman

As healthcare costs continue to rise, more employers are turning to self-funded insurance plans to offer health insurance to their employees. Self-funding allows businesses to save on healthcare costs and customize coverage to suit employees' specific needs. In the United States, approximately 64% of workers with health insurance are covered by self-funded employer plans, with 62% covered by a medical stop loss policy.

Self-insured employers buy high-deductible stop-loss policies to ensure that they are reimbursed for unexpected large claims that could otherwise wreak havoc on their cash flow. In the face of rising costs and an increasing focus on quality, medical stop-loss insurance will remain important for managing volatility.

However, many of today's medical stop loss insurers base their underwriting and pricing on outdated actuarial models and static data. This simplistic approach to estimating healthcare costs often leaves underwriters unable to predict coverage limits accurately and therefore challenged to maintain their loss ratios. Luckily, property and casualty insurance may provide the answers that stop loss carriers have been looking for with the way that it leverages artificial intelligence. AI and predictive analytics provide a more rigorous approach to risk management and can help stop loss carriers improve risk selection and limit costs while still providing coverage for self-funded plans.

P&C and workers' comp insurers are ahead of medical insurers when it comes to identifying risks and making more accurate predictions using AI. As an example, if someone slips and falls at work, a P&C insurer will likely use AI to assess a wide variety of risk factors and incorporate them into predictions, to alert claims managers of potentially catastrophic outcomes.

Medical stop loss, however, does not assess risk in the same manner. And with medical stop loss premiums up 10% for 2022 according to Segal's national stop-loss database, it's time to rework how stop loss insurance is calculated so that self-insured employers can take advantage of value-added quality and cost-containment solutions afforded to self-funded employers. Medical stop loss can learn great lessons from what P&C insurers have already done.

How are P&C providers leveraging AI

In addition to the workers comp claims management example cited above, P&C providers are already leveraging AI in creative and game-changing ways. Let's consider just a few:

Consumers expect speed, and in the B2B market, turning quotes around quickly can mean the difference between winning and losing an account. AI is providing P&C underwriters with that speed in several ways. For one, it enables straight-through processing, meaning that AI algorithms allow low-risk applications to be processed automatically. This means claims are processed more quickly and quotes can be written instantly without requiring an underwriter to get involved in the process. As an added benefit, the underwriter can instead focus on more complex applications that would benefit from their attention and insights.

Additionally, AI can quickly identify signals that often appear together and imply future risk. In the case of a P&C auto insurer, higher driving mileage per year in addition to an older car and a young driver can lead to a higher risk profile. Therefore, the algorithm may recommend a higher premium to underwriters, allowing them to turn the policy around faster.

In today's P&C insurance industry, the biggest risk is a lack of insight. In addition to speed, P&C insurers also use AI to identify hidden risks — picking out what appears to be a clear-cut case that could be problematic in the future. AI can consider hundreds of variables and many more inputs than humans in a far shorter amount of time. This can surface more risks, leading to more accurate policies.

How medical stop loss carriers can adopt P&C methods

AI can provide unique opportunities for medical stop loss underwriters as well. By applying statistical models, AI algorithms can look at thousands of policies in a fraction of the time it would take human underwriters and automatically process simple or straightforward quotes. This frees up underwriters to tackle the more complicated accounts, allowing them more time to make accurate predictions on complex cases.

AI algorithms can also identify people who might not present as high-risk today but could become high-risk in the future, according to their claim's history. It can consider many more inputs. Is the person taking certain medications? Do they have a history of certain treatments? Do they present with other risk factors? Because AI can analyze an enormous amount of patient medical data, more signals can be considered and hidden risks can surface that might not have been detected by a human.

AI recognizes patterns better and faster than humans. Through pattern matching, AI can also benefit stop loss coverage by providing key indicators of a particular illness or injury. AI can detect minute anomalies that even the most experienced physicians would not be able to notice. These intelligent insights help patients get the correct diagnosis and into tailored programs that are specific to that diagnosis. Not only does this give policyholders an opportunity for enhanced care and the best ROI, but insurers profit because they're able to take proactive actions during policy creation. In addition, patients benefit because they're getting better information about their own health and can proactively address their specific medical issues.

There are thousands of programs to help specific targeted populations, and AI can perform pattern matching to not only suggest programs that could be beneficial, but make predictions about the ROI of those programs.

Staying competitive

AI helps medical stop loss providers stay competitive and scale more easily. Some providers can get requests for as many as 10,000 quotes a year, but they may only actually get to process 2,000 of them because they just don't have the capacity to analyze them all. However, by using AI, providers can identify the best 10% of the 10,000 and work on those first to maximize their resources. And AI can automatically process straightforward cases, allowing them to produce even more quotes and price policies more aggressively.

By automating parts of the process in this way, medical stop loss providers can also improve the efficiency of their seasoned underwriters because the underwriters don't have to be involved in every step of the process. They can review simple policies quickly before they go out, but they remain free to analyze more complicated accounts and provide better predictions.

AI assists medical stop loss providers in limiting costs without removing a common stop-loss industry practice in which the group is charged a higher deductible for certain plan members based on their prior claims experience or the likelihood of becoming high-cost claimants in the future.

As the medical stop loss market grows, underwriters must learn from their P&C colleagues and incorporate AI into their underwriting process, to stay competitive and scale their capacity. Not only will it reduce risk, but it will also allow them to provide better service to their policyholders by offering more information about their health and recommendations about the best course of care.

Brett E. Heineman, FSA, is General Manager of Health Products at Gradient AI.

Heineman is a health actuary with broad experience in the health insurance industry, ranging from provider contracting analysis for carriers to actuarial valuation and actuarial transformation for consulting firms. His professional interests include organizational and personal productivity, actuarial process automation, practical applications of predictive modeling and actuarial modernization/actuarial transformation.

This article first appeared on Digital Insurance .

500 Workers’ Compensation Stakeholders Identified the Top 10 Industry Challenges

Thu, 12 Jan 2023 16:36:30 GMT

January 12, 2023

Stakeholders from across the workers’ comp industry listed the top challenges their organizations faced this past year in a survey conducted by Risk & Insurance at National Comp and online.

By Courtney DuChene

In its fifth year, the Risk & Insurance® survey of workers’ compensation professionals, sponsored by Healthesystems, highlighted the top challenges industry professionals faced in 2022 and their priorities heading into 2023.

The survey data was garnered from attendees at National Comp in Las Vegas from Oct. 18 through Oct. 20, 2022.

This year, medical care challenges reigned supreme. Health care provider and service shortages, medical price inflation and increasingly complex claims were the top three obstacles the survey identified.

As the industry struggles with medical challenges, it’s grappling with a talent shortage of its own. Fortunately, new technologies are improving efficiency across the workers’ compensation system and enhancing injured worker experiences, though implementation is still slow.

Here’s a look at the top 10 challenges the survey identified.

1) Health Care Provider or Service Shortages

Health care provider and service shortages ranked as the number one challenge workers’ compensation stakeholders faced in 2022, with 58.28% of survey participants naming it as a top five concern for their organizations.

The drivers behind the health care provider shortage are myriad. In 2020, the Association of American Medical Colleges estimated that there would be a shortage of between 54,100 to 139,000 physicians by 2034 — and that was before the COVID-19 pandemic increased medical provider burnout , causing many physicians, nurses, and other allied health professionals to leave the industry.

“The fallout from the pandemic has affected all types of health care workers,” said Silvia Sacalis, VP of clinical services at Healthesystems. “Physicians certainly, but also nurses, pharmacists, technicians, and home health care workers are in short supply. Health care is one of the industries most severely impacted by the Great Resignation and it will take time to recover.”

Injured workers face additional hurdles to finding available care as a number of medical practices and related services refuse to accept workers’ compensation insurance payments due to low fee schedules.

The health care provider and service shortage has a number of consequences for workers’ compensation:

“These challenges make it difficult for adjusters to provide timely access to health care services for injured workers, resulting in more problematic claims outcomes, slower healing, longer disability, and likely, higher litigation rates,” said Kimberly Wiswell, CMSP-fellow senior product manager, workers’ compensation, Gradient AI, a participant in this year’s survey.

Although the heath care provider and service shortage was named the number one challenge, access to timely and quality care was only one of several barriers to care cited by survey participants, which included injured workers who are unfamiliar with the workers’ comp system, comorbidities, limited transitional or light duty assignments, communication challenges and social determinants of health.

2) Medical Price Inflation

The second issue pressing on workers’ compensation stakeholders’ minds was medical price inflation. Just over 57% of respondents listed it among their top concerns.

Overall, medical costs per claim rose two percent in 2021 and NCCI projects costs in the personal health care index will increase 3.7% in 2022.

Though these numbers are lower than consumer inflation, which has risen dramatically over the past few years, many in workers’ comp are wary. Health care inflation typically lags behind other price inflation and medical care costs are increasingly accounting for greater shares of total claims spending. Workers’ compensation payers are already seeing price inflation for some medical services and products.

“Labor shortages are having a disproportionate impact on health care and we still see supply chain disruptions for pharmaceuticals and medical devices,” said Kristine Kennedy, SVP of product and innovation for Healthesystems.

“The best way to mitigate the dual challenges of shortages and price inflation is through vigilant selection and management of all medical provider and service networks.”

3) Increasingly Complex Claims

More than 56% of respondents placed increasingly complex claims as a top five concern for their organizations, making it the third biggest challenge.

Increasingly complex claims have been a concern for a number of years now. This year, mental health issues were the primary concern for respondents worried over claims complexity, with 61.73% naming it as problematic. Closely following mental health issues was comorbidities, which 61.05% saw as a concerning claim complexity.

4) Workers’ Comp Litigation

Nuclear verdicts — those that award plaintiffs more than $10 million — have been on the rise and it has a number of workers’ compensation professionals on edge. Over 55% of respondents placed litigation concerns toward the top of their lists of industry challenges.

One reason litigation may be top-of-mind: the insurance industry talent shortage. Experienced adjusters are able to prevent claims from turning litigious. Less experienced adjusters may not have the breadth of knowledge needed to detect at-risk claims and intervene.

The silver lining? The industry has tested strategies for preventing litigation. The injured worker advocacy movement is believed to reduce workers’ comp litigation.

“There’s a large effort really pushing worker advocacy,” said a veteran executive with a national medical management firm who attended National Comp and took the survey. “And I think that’ll help lower litigation.”

Others seem to agree with him. Sixty-five percent of survey participants said that personalizing an injured workers’ experience was the most valuable data application and 48% reported success with initiatives aimed at improving employee’s experiences with workers’ compensation.

5) New Workplace Safety Challenges

Workers’ comp is always adapting to new workplace safety issues and 2022 was no different.

The survey found that 53.46% of workers’ comp stakeholders believe new workplace safety challenges to be a primary concern for their organization.

The phrase “new workplace safety challenges” covers a variety of trending issues in workers’ compensation, from workplace marijuana use to telecommuting, safety considerations and fears over communicable disease. Workplace violence was another issue respondents listed as a recent safety challenge.

6) Comorbidities and Poor Worker Health

Combating comorbidities remains a major challenge for workers’ comp professionals. Over 52% listed it as a primary concern and 71% said it was a major medical program priority, up from 46% in prior surveys.

Comorbidities were the second biggest barrier to recovery, with 52% of respondents naming it a hurdle, and the second most worrying type of claims complexity.

At the heart of tackling comorbidities is a simple mission: get workers back to health and back on the job as quickly as possible.

“We see more workers who have chronic conditions, such as hypertension and diabetes” said Healthesystems’ Dr. Sacalis. “And, unfortunately, mental health concerns are now more prevalent across the board, including in workers’ comp.”

“This is why it’s so important to identify injured workers who are at risk and intervene as early as possible. A full recovery requires treating the whole patient.”

7) Insurance Industry Labor Shortage

The commercial insurance industry was preparing for a talent shortage of 400,000 workers even before the Great Resignation began making headlines across the country. Now, 56% of insurance companies said they had plans to increase staff last year, per an Insurance Information Institute report.

There continues to be a lot of turnover in the industry — so much so that 49.27% of survey participants believe the insurance industry talent shortage to be a top concern.

Claims professionals are feeling the effects of the talent shortage most acutely. Sixty percent said there are too many claims to manage, up from 40% who said the same in last year’s survey .

“Reducing the burden on front-line claims staff is so important,” said Kennedy. “Automating appropriate tasks and providing reliable clinical decision support allows claims professionals to put their time and attention where it is needed most.”

A number of negative outcomes can occur when claims professionals are overwhelmed due to a lack of resources. Injured workers may take longer to recover and may be more likely to pursue litigation, making it important to develop a strategy for improving efficiency and supporting claims staff.

8) Increasing Regulation and Expanding Coverage Requirements

Number eight on the list of challenges was increasing regulation and expanding coverage requirements. More than 47% of participants named this a top challenge.

Workers’ compensation regulations are always shifting. But in recent years as states tried to manage the COVID-19 pandemic a number of new laws came into play, possibly pushing regulation into the list of top concerns this year.

“Expanding presumption laws continue to broaden the responsibility scope for the workers’ compensation industry,” said Andrea L. Shopher, director of excess workers’ compensation for Safety National.

Insureds with multi-state programs may find shifting regulations difficult to keep up with, especially since each locality t may have differing requirements.

9) Outdated Technology and Insufficient Technological Support

Workers’ compensation has long struggled to keep pace with group health when it comes to adopting innovative technology solutions.

“Some employees and stakeholders are resistant to adopting new technologies. This can lead to a lack of support for new initiatives and uptake of new technologies,” Gradient AI’s Wiswell said.

In the past, old ways of doing things seemed to get the job done. Now, insurance personnel and provider shortages, increasingly complex claims and ever-changing regulations are highlighting the need for automation and other new tools. Over 41% of survey respondents named outdated and insufficient tech as one of their top five challenges.

Wiswell identified four types of new technologies that can help improve efficiency and claims outcomes: virtual and remote technologies, analytics and artificial intelligence services, predictive modeling and automation.

These tools can help adjusters manage claims remotely, identify potential claims obstacles, model outcomes and automate repetitive tasks to help reduce the strain of the industry talent shortage.

“These technologies are helping organizations to automate repetitive tasks such as data entry and document management, freeing up adjusters to focus on more complex tasks,” Wiswell said.

10) Cybersecurity Risks

Increased use of technology can bring increased cyber exposures; workers’ comp isn’t immune to the scores of cyber risk challenges companies have faced in recent years.

Cybersecurity risks rounded out the list of top challenges workers’ compensation stakeholders faced this year, with 29.14% naming it as a pressing threat.

These fears may be driven by the increased uptake of telemedicine and the growing number of adjusters and claims professionals who are working from home, where digital and online setups may not be as secure.

As the industry continues to invest in new technologies, fears over cyber risks amongst industry professionals will be an area to watch.

Key Takeaways

Medical issues are weighing on workers’ comp professionals’ minds this year. The top three challenges selected by attendees surveyed at National Comp all related to the medical system.

Claims process improvement and the insurance industry talent shortage were two other issues that ranked highly for survey participants. Respondents were keen to remark on the talent shortage’s connection to other major industry challenges, including litigation rates and tackling claims complexities.

In the face of many of these issues, workers’ compensation stakeholders are turning to digital tools to manage talent shortages and increase satisfaction for injured workers and their employers alike.

More than 33% of respondents said that technological integration between providers and partners has been the most beneficial program they’ve implemented over the past two to three years. Another 28.17% named analytics and artificial intelligence usage as their top improvement.

“Workers’ comp organizations understand that technology is essential to simplifying injured worker care,” said Kennedy. “Automation, analytics, mobile apps, and telehealth, to name just a few, will all play a role in further streamlining the processes between payers, providers, and patients.

Stakeholders are invested in using these tools to drive improvements in injured worker experience and to provide quality care that helps employees get back to work in a timely manner.

Readers who would like to receive a copy of the full survey report when published in February, can request a complimentary digital copy from Healthesystems .

About the Survey

In all, 503 stakeholders took part in the survey, representing every facet of the workers’ comp industry including executives, senior claims leaders, front-line claims professionals and adjusters, brokers and agents, medical providers, clinical case managers, attorneys, medical program managers and procurement professionals.

Insurance carrier response increased from 15% to 21% between this year’s and last year’s survey. Participation from health care providers, claims leaders and clinical case managers also increased while responses from employers and risk managers held steady.

This article first appeared on R isk & Insurance .

RESOURCES, INSIGHTS AND THOUGHT LEADERSHIP

What Claims Teams Don’t Know Is Costing Them

How hidden comorbidities and delayed insights drive workers’ comp claim costs and why early detection changes everything

The Hidden Cost Inside a “Routine” Worker’s Comp Claim

The Two Biggest Drivers of Claim Severity

The Real Problem: Timing, Not Awareness

Closing the Gap with Early Insight

Deeper Clinical Context = Smarter Decisions

The Compounding Impact Over Time

﻿ Final Thought

Explore the benefits of Gradient AI's P&C or Workers' Comp Solutions.

AI in P&C Insurance Has Hit an Inflection Point

Where Carriers Are on the AI Journey

AI Adoption is not Equal

Where AI Is Delivering Real Value

What’s Slowing Scale

Build, Buy, or Partner? It’s All Three

What Leaders Are Doing Differently

The Next 12–24 Months Will Separate Leaders

AI Implementation Roadmap for P&C Insurers

The Bottom Line

Explore the benefits of Gradient AI's P&C or Workers' Comp Solutions.

Managing Workers’ Compensation Claims: Turning Complexity into Actionable Insight

Q&A with Gradient AI’s General Manager of Property & Casualty, Brook Rosenbaum

Turning Experience into Practice: Bridging Insight and Execution

Q: Let’s start with the big picture. What are the most prominent challenges in managing workers’ compensation claims from both a cost and outcome perspective?

Clarity that Drives Trust: Making Complex Risk Understandable

Q: Even with aligned goals, why does execution remain so difficult?

The Compounding Engine: Embedding Analytics into Workflows

Q: How does data, and increasingly AI solutions, help address these challenges?

Conclusion

Key Takeaways for Claims Leaders

Explore the benefits of Gradient AI's P&C or Workers' Comp Solutions.

Getting to the How and Why: AI Shows Its Work

With insurance sector AI solutions, explainability was once a hindrance to widespread adoption. Those days are waning thanks to next-gen models, according to Gradient AI’s Stan Smith.

Gradient AI Launches Workers’ Comp Insurance Triage Solution That Flags Potentially Expensive or Complex Claims Upon First Notice of Loss

With a match rate exceeding 90%, ClaimVoyant™ assesses relative claim complexity given underlying co-morbidities and other health issues, providing opportunities for early staff assignment and optimized care management.

Move Beyond Static Workers’ Comp Reporting

How AI-Driven Claims Benchmarking Helps Brokers Deliver Deeper Client Insights

The Limitations of Traditional Claims Analytics

Why Claims Benchmarking Matters

AI Is Changing How Claims Performance Is Analyzed

Supporting Better Outcomes for Clients

﻿

Designed for Broker Workflows

Moving Beyond Static Reporting

Explore the benefits of Gradient AI's P&C or Workers' Comp Solutions.

At SIIA Spring Exchange, Gradient AI to Give Presentation on Best Practices for Choosing AI-driven Risk Management Solutions

Amid an uptick of new AI solutions in the insurance landscape, discussion

will explore key considerations to help guide the partner selection process.

﻿

CIBC Innovation Banking Provides Growth Capital to Gradient AI

Gradient AI Launches Workers’ Comp Benchmarking Tool

The AI-powered ClaimVector solution provides brokers with claims-based performance benchmarks grounded in real data.

New Gradient AI Solution Provides AI-Powered Workers’ Compensation Claims Benchmarking for Brokers

Why Explainable, Integrated AI Is the Future of Insurance Underwriting

Q: What are your thoughts on the current insurance underwriting landscape overall? What’s happening right now?

Q: As AI-driven underwriting solutions rapidly evolve, insurers have very different levels of AI maturity. How important is it to meet underwriters where they are, especially when their exposure to AI is still fairly basic?

Q: What factors have most constrained broader adoption of AI-driven underwriting solutions?

Q: Many underwriters’ early experiences with AI involved first-generation solutions. How does Gradient AI address the concerns that came from those early limitations?

Q: Would you also say that Gradient AI overcomes adoption roadblocks by acting as a true partner to its clients?

Q: What are some key signals or patterns that AI-enabled underwriting solutions detect that humans might miss?

Q: As AI solutions become more sophisticated, how do you ensure they remain user-friendly?

Q: How does more accurate underwriting benefit consumers and the insured, not just insurers?

Q: Gradient AI differentiates itself through the integration of previously siloed AI solutions. How far ahead is the company in this area?

Q: How does an integrated risk management lifecycle improve enterprise-level performance?

Q: What’s the next game-changing milestone for AI-driven underwriting, and how is Gradient AI moving toward it?

Five Keys to Choosing the Right AI Tool and the Right Partner for Profitable Risk Management

1. Get the right data to make accurate decisions.

2. Make sure your AI solution evolves and provides the insight you need.

3. Choose AI That Connects the Entire Risk Lifecycle.

4. Find a great partner, not just a great tool.

5. Change Management: where adoption succeeds or fails.

Explore the benefits of Gradient AI's Group Health Solutions.

Gradient AI Earns “Best Places to Work” Awards from Technology Recruitment Platform Built In

A leading provider of AI-driven enterprise software for the insurance sector, Gradient AI was recognized by Built In for the sixth consecutive year.

Group Health Insurers Must Integrate AI

Group health insurers using siloed AI tools miss opportunities that connected solutions across policy lifecycles could provide.

The AI Silo Trap

Better Together: Connected AI Solutions

Final Thought