We're excited to announce the expansion of AI Engine to include case summarisation for new business underwriting in the UK and Ireland. It joins non-disclosure detection for post-issue audit, which was launched in March.
AI Engine now turns medical evidence into a decision-ready, source-traceable case overview, helping underwriting and audit teams spend more of their time on validation and judgement, not data gathering.
Helping underwriters and auditors focus on decisions
Medical evidence is some of the most important information an underwriter reviews, and some of the most time-consuming to get through. It's essential to sound underwriting and audit decisions, but the work that comes before the decision can be slow and fragmented.
A case's relevant history is often spread across multiple documents, with conditions to piece together, timelines to reconstruct, and disclosures to cross-reference against the evidence, before an underwriter or auditor can even start applying their expertise.
From post-issue audits to a broader underwriting opportunity
AI Engine began with post-issue audits in March 2026, helping insurers compare application disclosures with medical evidence and surface potential non-disclosure for review. Post-issue audits surfaced something important: before you can compare what was disclosed against the medical evidence, you first need to understand the evidence itself. That's the foundation AI Engine now builds in from the start, for every case, not only the ones being audited.
For new business underwriting, that means getting a structured case overview before risk assessment even begins. For post-issue audit, it means that same structured evidence can be compared against application disclosures to flag potential gaps for review.
Boost case review productivity
Picture a case where the relevant history is split across a GP summary, prescription records and lab results, with the same condition mentioned three separate times, worded three different ways. Today, an underwriter reconstructs that by hand: building a timeline, cross-checking for contradictions, catching duplicates.
AI Engine groups conditions across the case, builds the timeline, and consolidates repeat tests and prescriptions automatically. New business underwriters get to the substance faster. Post-issue audit reviewers spend less time on clean cases and get flagged cases with the evidence organised for review.
Verify every finding in seconds
In underwriting, the person making the decision remains accountable for it. That accountability doesn't end when the case closes. A finding may need to be defended to a manager, a reinsurer or a regulator months later. An AI-generated answer is only useful if the reviewer can trace it back to where it came from, on the spot and after the fact.
Every extracted insight in AI Engine links back to its exact source passage, down to the sentence, not just the page. A reviewer can open a flagged finding, see the evidence it was built from, and approve or correct it in the same motion. Reviewer feedback controls let users mark information that's missing, wrong or irrelevant, and that feedback is what the system improves against.
Build a more consistent view of risk
Manual evidence review doesn't only cost time; it creates variability. Two underwriters can organise the same case differently, an important condition can get buried in a large bundle, and a fragmented chronology can obscure whether a condition is improving or worsening.
AI Engine gives every case the same structured starting point, so no single case depends on how thoroughly one reviewer happened to dig. Multiplied across a book of business, that consistency compounds: comparable risk decisions across underwriters, and a more reliable evidence base behind the portfolio as a whole, not just the individual case in front of you.
Proven in post-issue audit
AI Engine's post-issue audit capability has already been tested with major UK insurers. Validated UK beta test results show:
75% underwriter time saved on clean post-issue audit cases
More than 50% underwriter time saved on flagged cases
98% non-disclosure detection accuracy.
AI that works with underwriting expertise
AI's real value in life insurance is freeing experts from repetitive tasks so they can focus on judgement, with the transparency and control to trust what AI produces.
With AI Engine, underwriters spend less time hunting through medical documents and more time assessing the risk in front of them. For underwriting leaders, that translates into more review capacity without more headcount, and less variability between how different underwriters organise the same case. Governance and risk stakeholders see AI outputs that stay connected to their evidence, with decisions that stay human-owned.
See AI Engine in action
Ready to see what AI Engine can do for you? Book a demo, or start a free trial and see it on your own portfolio.