UnderwriteMe has launched an artificial intelligence tool aimed at helping life insurers increase post-issue audit coverage without adding to underwriting workloads.

The new AI Engine for Post-Issue Audits is designed to automate the detection of potential misrepresentation after a policy has been issued, by comparing information provided at application stage with medical records and other supporting evidence.

Post-issue audits are used by insurers to identify inaccuracies before a claim arises, while also supporting pricing accuracy and oversight of the wider risk book. They involve checking disclosures made during the application process against evidence obtained after issue, such as GP reports or other medical records.

UnderwriteMe said the process has traditionally been labour-intensive, with underwriters often required to review medical files running to hundreds of pages. As a result, many insurers only audit a small share of issued policies.

The firm said its AI Engine is intended to remove that constraint by automating misrepresentation detection rather than simply summarising medical evidence.

Andy Doran, chief executive 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.”

UnderwriteMe said beta testing with four major UK life insurers showed a 98% misrepresentation detection rate, a 75% reduction in underwriter review time on clean cases and a reduction of more than 50% on flagged cases.

For cases where a discrepancy is identified, the system also links each finding back to the relevant part of the source medical evidence, which the firm said would support transparency and traceability for insurers, regulators and reinsurers.

Doran said: “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.”