Podcast

Addressing the Explainability Gap in Insurance AI 


CEO Stan Smith joins A Global Tech Podcast to discuss how a new generation of AI solutions is redefining explainability, using large language model techniques layered on advanced analytics to translate complex risk patterns into clear, actionable reasoning that aligns with underwriting decision-making and claims operation.

The Challenge:

Fragmented systems and siloed data. As carriers struggle to gain a complete view of risk across their organizations, connected data and AI are creating new opportunities to improve decision-making, uncover hidden exposures, and drive stronger portfolio performance.

In this podcast, you’ll hear how a unified view of a carrier’s book of business can help insurers operate more strategically, while AI models continuously learn from historical performance to identify which risks are most likely to succeed.


Key takeaways from the episode include:


  • How insurers can break down data silos across business lines
  • Why fragmented datasets create blind spots in risk evaluation
  • The value of a holistic portfolio view for identifying duplicate exposures and hidden risk
  • How AI improves underwriting and risk selection over time through performance-based learning
  • Ways insurers can make faster, more strategic business decisions with connected intelligence
  • The future of AI-driven insights across the full lifecycle of risk management


Additionally, you’ll hear how claims operations can leverage AI to guide the Next Best Action by not just identifying the high-claim potential but the best way to address and manage those claims to reduce both cost and duration which also leads to better outcomes for the injured worker.


 “More and more, we are seeing the opportunity for AI to provide more intelligence on claims operations earlier in the life of the claim, so you can do the right thing at the right time.”