UnderwriteMe has launched AI Engine for post-issue audits, an automated misrepresentation detection solution designed to help life insurers dramatically expand audit coverage —without increasing underwriting resource.
Post-issue audits are reviews conducted after a life insurance policy has been issued to compare the information provided in the application with medical records or other evidence.
They aim to help insurers detect any discrepancies or misrepresentation on an application, helping to maintain consistent underwriting standards, protecting pricing integrity and ensuring fair outcomes across policies.
However, the process can prove labour intensive as underwriters must manually review extensive medical documentation, often running to hundreds of pages per case, in order to identify potential discrepancies. As a result, many insurers audit only a small proportion of issued policies, limiting the scale at which audit programmes can support governance, pricing oversight, and reinsurer reporting. AI Engine aims to remove this operational constraint.
Rather than just summarising medical evidence, AI Engine fully automates misrepresentation detection in a bid to significantly reduce underwriter review time on clean and flagged cases.
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.”
In beta testing with four unnamed major UK life insurers, AI Engine demonstrated significant improvements in audit efficiency and scalability, including a 98% misrepresentation detection rate, a 75% reduction in underwriter review time on clean cases (no misrepresentation detected) and a more than 50% reduction in review time on flagged cases (misrepresentation detected).
For flagged cases in particular, AI Engine aims to improve underwriter efficiency by automatically linking every misrepresentation finding to the precise location in the source medical evidence, ensuring outcomes are transparent, traceable, and defensible to regulators and reinsurers.
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.”
