Insurance Fraud

UK evidence & validation tools

Section

What do you need to validate?

Reported fraud evidence

Different populations and periods. These are separate observations, not a like-for-like comparison.

Claims · 2024

Detected dishonest claims

98,400UK • all insurance classes • ABI reporting

Detected cases, not the total incidence of fraud. This figure does not identify AI use.

Association of British Insurers

Applications · 2025

Fraudulent applications detected

>105,000Aviva UK general insurance • excludes Direct Line

One insurer’s count; no application denominator or AI-attributed share is given.

Aviva

Insurance implications · The Pricing Actuary’s Lens

Editorial interpretation. Detected fraud reflects both offending and detection effort; these figures do not estimate a UK-wide AI fraud rate.

Frequency

Track detected cases per application or claim as well as the total count. Better detection can increase recorded fraud without an increase in underlying incidence.

Severity

Separate attempted value, confirmed avoided cost and recovered cash. Compare net savings after investigation and supplier costs.

Exposure basis

Applications, bound policies, claims and uploaded documents are different denominators. Keep stage and observation period attached to every result.

Tail and accumulation risk

Reused identities, documents and networks can create correlated losses. Look beyond a single policy while checking false matches.

Price response and selection

Manipulation can change which risks enter the book. Measure performance after price changes and referral rules, allowing for shifts in business mix.

Capital vulnerability

Fast binding and settlement shorten the time available to intervene. Test controls against new manipulation methods before widening automation.

Customer expectation

Measure the time and effort imposed on genuine applicants and claimants, including those with thin data histories.

Duty to act

Use explainable referrals, proportionate evidence requests and a correction route. Do not treat an anomaly, missing record or model score as a finding of fraud.