JSJOHAN STRYDOMSubscribe free
The Actuary Lens

Validate the facts. Protect the honest customer.

Independent evidence helps resolve inconsistencies fairly.

Back to The Actuary LensEvidence as at 28 Sept 2026
Approved evidence published 4 Oct 2026 · Release details

This date identifies the approved release. Individual sources can describe earlier periods and publication dates.

Release reference lens-2026-10-04-59b57c37396f

Download this topic’s data & citationsOpen the complete release record

Insurance Fraud

UK evidence & validation toolsOpen the free-data lab

INSURANCE IMPLICATIONS

The five conclusions

Test
Direction
New deception methods increase the need for proportionate validation.
Magnitude
Detected fraud is not total prevalence; false positives also have a cost.
Timing
At proposal, during cover and at claim, according to the evidence and purpose.
Evidence
Documented threats and available checks do not establish an individual case of fraud.
Response
Test before changing portfolio assumptions.

What would change this view? Reassess with measured detection value, false positives and customer outcomes.

Evidence dates, periods and supporting sources

See the worked framework example

What do you need to validate?

Reported fraud evidence

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

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 (opens in a new tab)
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.

01

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.

02

Severity

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

03

Exposure basis

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

04

Tail and accumulation risk

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

05

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.

06

Capital vulnerability

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

07

Customer expectation

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

08

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.