AI Helps Underwriters Discern Risk with Greater Accuracy and Speed, Improving Outcomes and Driving Growth

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:

1. 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.
   
   
2. 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.
   
   
3. 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.
   
   
4. 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.

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