UnderwriteMe launches AI engine for post-issue audit misrepresentation detection
The new tool, AI Engine for Post-Issue Audits, aims to help insurers expand audit coverage without increasing underwriting resource.
UnderwriteMe has launched an AI-powered solution designed to automate misrepresentation detection in post-issue audits for life insurers.
The new tool, AI Engine for Post-Issue Audits, aims to help insurers expand audit coverage without increasing underwriting resource, while supporting pricing governance and portfolio oversight.
Post-issue audits are conducted after a life insurance policy has been issued to compare application disclosures with medical records or other evidence.
These checks are used to identify inaccuracies and ensure underwriting decisions and pricing assumptions are based on complete and accurate information.
UnderwriteMe said traditional audit processes can be resource-intensive, requiring underwriters to manually review large volumes of medical documentation.
As a result, many insurers audit only a limited proportion of policies.
The firm said its AI Engine automates misrepresentation detection, reducing the need for manual review and enabling insurers to scale audit programmes more efficiently.
In beta testing with four UK life insurers, the solution recorded a 98% misrepresentation detection rate.
It also reduced underwriter review time on clean cases by 75% and by more than 50% on flagged cases.
For flagged cases, the system links identified discrepancies directly to the relevant sections of source medical evidence, supporting transparency and auditability.
Andy Doran, chief executive officer at UnderwriteMe, said: “Post-issue audit is an important component of pricing governance for life insurers.
“AI Engine allows insurers to move from constrained sampling to scalable, consistent audit oversight — strengthening portfolio integrity without increasing operational burden.”
He added: “Working in partnership with our beta programme customers was central to how AI Engine was developed.
“Their underwriting teams worked closely with ours to test the solution in real audit workflows and challenge how misrepresentation detection should operate in practice.
“That collaboration helped us refine AI Engine so it reflects real underwriting judgement, aligns with each insurer’s philosophy, and delivers the transparency and traceability required for confident audit decisions.”
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