Post-audit issues are reviews carried out after a life insurance policy has been issued, comparing the information in an application with medical records or other evidence.
They help insurers spot inaccuracies before a claim is made, so issues are handled earlier. Post-audit issues are also key for an insurer’s pricing strategy and risk portfolio by ensuring that underwriting decisions and pricing have been based on complete and accurate information.
UnderwriteMe said the process helps insurers detect discrepancies or misrepresentations, while giving advisers confidence that accurate applications will be treated fairly with less risk of disputes or misunderstandings when a claim is made.
The firm said usual audit processes required a lot of resources as underwriters had to manually review medical records. As a result, many insurers audit a small proportion of policies.
UnderwriteMe said AI Engine would resolve this issue.
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It automates the detection of misrepresentations, reducing underwriter review time.
Andy Doran, CEO of 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.”
An efficient underwriting process
The programme was beta tested by four UK life insurers, finding that AI Engine had a 98% misrepresentation detection rate and reduced underwriter review time on clean cases by 75%. It also resulted in a 50% reduction in review time on flagged cases where misrepresentation was detected.
With flagged cases, UnderwriteMe will link each misrepresentation to the source medical evidence.
AI Engine can be integrated into existing workflows and is not intended to replace the judgement of human underwriters.
It is part of UnderwriteMe’s Decision Platform and is currently available to life insurers in the UK.
Doran 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.”
