Faster claims without quietly denying valid ones.

Straight-through processing is the obvious prize in claims. The risk is a system that achieves it by getting quietly stricter with genuine claimants who do not appeal.

01 / What is hard here

The problem underneath the problem.

Fraud models in insurance are trained on historically-investigated claims, and investigation was never random. It followed the intuitions and biases of the people doing it, so the model learns to reproduce those patterns and presents the result as evidence.

The commercial pressure runs the same way. A model that reduces payouts looks successful on every metric a finance function tracks, whether it reduced fraud or simply made claiming harder. Distinguishing between those requires deliberate measurement that nobody is incentivised to build.

02 / What we build

Work we take on in insurance.

Claims triage: Routing straightforward claims to fast settlement and complex ones to experienced handlers, which is where the genuine efficiency lies.
Document and image assessment: Extraction from claim forms, invoices and reports, and damage assessment from photographs with mandatory human review above a value threshold.
Fraud detection with review: Network and behavioural scoring that flags for investigation and never denies automatically, with decision rates monitored across customer segments.
Underwriting support: Risk assessment with reason codes and the evidence trail regulators expect, built to inform an underwriter rather than replace the policy.
Reserving and portfolio analytics: Claim development modelling and portfolio-level exposure analysis.

03 / Constraints

What shapes the build.

These are design constraints agreed at the start, not compliance work bolted on before launch. Retrofitting them is expensive and usually incomplete.

No automated denial: Adverse claim decisions require human judgement and a documented reason. We build the routing and the evidence; a person decides.
Segment monitoring: Approval times, settlement rates and flag rates tracked across geography, product and customer segment to catch drift into de-facto discrimination.
Regulatory evidence: Full decision reconstruction — model version, inputs, threshold, outcome — retained per your obligations.
Appeal path: Every automated flag has a route to human reconsideration, and the appeal outcomes feed back into evaluation.

04 / What we advise against

Where we would say no.

We would advise against automated claim denial in any form, and against measuring a claims model on payout reduction alone — without a genuine-claim rejection measure alongside it, that metric will improve for the wrong reasons.

We would rather lose the work than build something that creates a liability for you or a harm for the people on the other side of it. If that rules out what you had in mind, it is better established now than at delivery.

05 / Other sectors

Adjacent work.

Different domains, the same delivery discipline — a measured baseline, a real evaluation set, and monitoring that catches decay early.

Next step

Working in insurance?

Tell us which process is slow, expensive or error-prone. The diagnostic will tell you whether this is a problem worth solving with machine learning — including if it is not.

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