Where claims fraud models actually pay for themselves

Claims fraud in insurance is not one problem. It is at least four, occurring at different points in the lifecycle, with different economics. Teams typically invest heavily in one and leave the others untouched.

That misallocation is why some fraud programmes plateau.

The four points

At quotation and application. Identity fraud, inflated declared values, prior-loss concealment. Small population, high value per case, and it contaminates the portfolio before a claim is ever made.

At first notification. Duplicate claims, claims against policies lapsed or not in force, claims outside coverage geography. Cheap to detect with deterministic rules, and frequently more productive than any model.

During claim handling. Inconsistent loss quantification, excessive repair estimates, unnecessary parts replacement. This is process variance, and it shows up as a distribution rather than an outlier.

At settlement. Claims that should have been subrogated, inflated settlements to avoid dispute, repeated loss patterns across a portfolio.

Each requires different data, different signals, and often a different owner.

Rules first, and not as a compromise

A common disappointment is that "we tried rules and moved to machine learning". Usually the rules were written poorly rather than the approach being wrong.

At first notification, most fraud is deterministic and checkable against existing data: has this claimant had a prior claim, is the loss inside a plausible range for this vehicle, does the incident date fall in coverage, does the repair estimate exceed a threshold without an independent assessment.

These are queries, not models. They run in milliseconds, produce an explanation for free, and are trivially auditable.

Reserve modelling for the points where the signal genuinely does not reduce to a lookup — usually claims handling variance and portfolio-level patterns at settlement.

The economics are usually dominated by one segment

Fraud loss is not evenly distributed. In motor claims, a small share of claimants accounts for a large share of identified loss. In travel, the pattern differs. In commercial property, it concentrates almost entirely in a few large claims.

Segment the data before building anything. A model trained across heterogeneous segments frequently performs adequately overall while being useless on the segment that matters.

The recovery problem

Detection is not recovery. Several common patterns lose the value:

• flags raised after payment has already been made
• cases investigated but never escalated, because the threshold sits above the investigating team’s authority
• savings counted gross rather than net of investigation cost
• legitimate claims delayed for long enough that good customers stop claiming

A programme that reduces paid claims by two per cent but adds four days to every honest customer's settlement has damaged the business. Track cycle time and complaint volume alongside the fraud number.

Explainability is not optional here

A declined claim, or a claim settled below the requested amount on fraud grounds, is a decision a customer will challenge and sometimes take to the Ombudsman.

Every adverse decision needs a retained reason: which signals contributed, what the outcome would have been without them, and what the claimant can do to contest it.

This rules out a large class of black-box approaches for individual claim decisions, which is a genuine constraint rather than a policy preference. It does not rule out modelling — it means the model should score and explain, with the decision rules written separately and reviewed by someone accountable.

A workable sequence

1. Find the repeatable frauds you are missing entirely. Deterministic checks against existing data, usually in the first weeks.
2. Segment the portfolio and identify where loss actually concentrates.
3. Instrument the investigation process before adding detection — volume, cycle time, recovery rate, escalation rate.
4. Model the specific points where the signal does not reduce to a lookup.
5. Track net recovered and customer impact together, from day one.

Most insurers find that the first item recovers more than the entire modelling programme that preceded it, because nobody had written down what the existing data already proves.