Clear
The concern is explained. Continue appropriately.
Independent evidence helps resolve inconsistencies fairly.
The executive takeaway
Validation process illustration. No conversion or detection rate is implied.
Information is submitted and a check raises a question.
Check evidence quality, recording errors and innocent explanations.
After corroboration
The concern is explained. Continue appropriately.
Resolve an error using verified information.
A material concern remains. Use proportionate human review.
Measure confirmed value alongside false positives and the effect on honest customers.
Keep in mindA mismatch is not proof of fraud; detected fraud is not total prevalence.
Established: This evidence set distinguishes reported fraud, warning signals and validation tools.
Evidence to change the assessment: Reassess with measured detection value, false positives and customer outcomes.
Percayso documents comparison of quotes and retrospective policy analysis to identify manipulation.
Limit: Compare declarations across the journey and refer material inconsistencies. A changed answer alone does not establish intent or AI involvement.
Evidence period: Product documentation accessed 22 September 2026Checked in this release: 22 Sept 2026
FRISS describes checks for synthetic images, altered PDFs and reused evidence.
Limit: Combine media analysis with original files, incident evidence and human review. A synthetic-content score is not a fraud verdict.
Evidence period: Product documentation accessed 22 September 2026Checked in this release: 22 Sept 2026
Approved evidence published 2 Oct 2026This topic, its downloads and linked Data Lab views use the same selected release.
Inspect the sources and their datesProposal and claim information can be checked against independent evidence. The aim is to find meaningful inconsistencies while keeping fair treatment and human judgement.
Established: This evidence set distinguishes reported fraud, warning signals and validation tools.
Developing: AI can support checking and can also make false evidence more convincing.
Still uncertain: A mismatch is not proof of fraud; detected fraud is not total prevalence.
Percayso documents comparison of quotes and retrospective policy analysis to identify manipulation.
Limit: Compare declarations across the journey and refer material inconsistencies. A changed answer alone does not establish intent or AI involvement.
Evidence period: Product documentation accessed 22 September 2026Checked in this release: 22 Sept 2026
FRISS describes checks for synthetic images, altered PDFs and reused evidence.
Limit: Combine media analysis with original files, incident evidence and human review. A synthetic-content score is not a fraud verdict.
Evidence period: Product documentation accessed 22 September 2026Checked in this release: 22 Sept 2026
Guardian Integrity Shield describes application, claims and document checks, including AI-generated evidence.
Limit: Test the extra cases found after existing controls, and the impact on genuine customers. Supplier claims are not observed performance.
Evidence period: Supplier product description accessed 22 September 2026Checked in this release: 22 Sept 2026
New deception methods increase the need for proportionate validation.
Current response: Test
Watch next: Reassess with measured detection value, false positives and customer outcomes.
A mismatch is not proof of fraud; detected fraud is not total prevalence.
Evidence period: Calendar year 2025; Aviva UK general insurance unless stated otherwiseSource published: 8 Jun 2026Checked in this release: 22 Sept 2026
Evidence period: Calendar year 2024Checked in this release: 22 Sept 2026
Evidence period: Product documentation accessed 22 September 2026Checked in this release: 22 Sept 2026
Evidence period: Product documentation accessed 22 September 2026Checked in this release: 22 Sept 2026
Evidence period: Product documentation accessed 22 September 2026Checked in this release: 22 Sept 2026
Evidence period: Product documentation accessed 22 September 2026Checked in this release: 22 Sept 2026
Evidence period: Product documentation accessed 22 September 2026Checked in this release: 22 Sept 2026
Evidence period: Product documentation accessed 22 September 2026Checked in this release: 22 Sept 2026
Evidence period: Product documentation accessed 22 September 2026Checked in this release: 22 Sept 2026
Evidence period: Product documentation accessed 22 September 2026Checked in this release: 22 Sept 2026
Evidence period: Product documentation accessed 22 September 2026Checked in this release: 22 Sept 2026
Evidence period: Product documentation accessed 22 September 2026Checked in this release: 22 Sept 2026
Evidence period: Industry fraud resources accessed 22 September 2026Checked in this release: 22 Sept 2026
Evidence period: Product documentation accessed 22 September 2026Checked in this release: 22 Sept 2026
Evidence period: Product documentation accessed 22 September 2026Checked in this release: 22 Sept 2026
Evidence period: Product documentation accessed 22 September 2026Checked in this release: 22 Sept 2026
Evidence period: Supplier product description accessed 22 September 2026Checked in this release: 22 Sept 2026
Evidence period: Calendar year 2024, with a restated 2023 comparatorChecked in this release: 28 Sept 2026
Evidence period: Calendar year 2022Checked in this release: 28 Sept 2026
Evidence period: National Fraud Database member reporting, calendar year 2025Checked in this release: 28 Sept 2026
Evidence period: Portal data and publications since service launchChecked in this release: 28 Sept 2026
Evidence period: 1 April to 30 June 2026Checked in this release: 28 Sept 2026
Evidence period: Financial years 2018/19 to 2025/26Checked in this release: 28 Sept 2026
Evidence period: Two years of MIB claims; postcode districts ranked by defendants per 1,000 populationSource published: 10 Nov 2025Checked in this release: 28 Sept 2026
Evidence period: Monthly street-level police-recorded crimeChecked in this release: 28 Sept 2026
Evidence period: Police-force-area vehicle offences, calendar years 2016 to 2025Checked in this release: 28 Sept 2026
Evidence period: MOT tests from 2005 onwardChecked in this release: 22 Sept 2026
Evidence period: Quarterly and annual licensed-vehicle statisticsChecked in this release: 21 Sept 2026
Evidence period: Annual motor-vehicle mileageChecked in this release: 26 Sept 2026
Evidence period: Police-reported collisions and casualtiesChecked in this release: 26 Sept 2026
Evidence period: Current unauthorised-firm warningsChecked in this release: 28 Sept 2026
Evidence period: Current regulatory permissions and statusChecked in this release: 28 Sept 2026
Evidence period: Half-yearly regulated-firm complaintsChecked in this release: 28 Sept 2026
Evidence period: April to June 2026Checked in this release: 28 Sept 2026
Evidence period: Enforcement caseSource published: 1 Aug 2023Checked in this release: 28 Sept 2026
Evidence period: Enforcement caseSource published: 30 Apr 2025Checked in this release: 28 Sept 2026
Evidence period: Enforcement caseSource published: 19 Sept 2025Checked in this release: 28 Sept 2026
Evidence period: Enforcement caseSource published: 25 Jul 2024Checked in this release: 28 Sept 2026
Evidence period: Enforcement and confiscation caseSource published: 10 Mar 2025Checked in this release: 28 Sept 2026
Evidence period: Enforcement operationChecked in this release: 28 Sept 2026
This date identifies the approved release. Individual sources can describe earlier periods and publication dates.
Release reference lens-2026-10-02-3d9ceed6e4d1
INSURANCE IMPLICATIONS
What would change this view? Reassess with measured detection value, false positives and customer outcomes.
Different populations and periods. These are separate observations, not a like-for-like comparison.
Claims · 2024
Detected cases, not the total incidence of fraud. This figure does not identify AI use.
Applications · 2025
One insurer’s count; no application denominator or AI-attributed share is given.
Editorial interpretation. Detected fraud reflects both offending and detection effort; these figures do not estimate a UK-wide AI fraud rate.
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.
Separate attempted value, confirmed avoided cost and recovered cash. Compare net savings after investigation and supplier costs.
Applications, bound policies, claims and uploaded documents are different denominators. Keep stage and observation period attached to every result.
Reused identities, documents and networks can create correlated losses. Look beyond a single policy while checking false matches.
Manipulation can change which risks enter the book. Measure performance after price changes and referral rules, allowing for shifts in business mix.
Fast binding and settlement shorten the time available to intervene. Test controls against new manipulation methods before widening automation.
Measure the time and effort imposed on genuine applicants and claimants, including those with thin data histories.
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.
Trace the assessment to its publications, datasets and observation periods.
Approved evidence published 2 Oct 2026This topic, its downloads and linked Data Lab views use the same selected release.
Inspect the sources and their datesEvidence limit: A mismatch is not proof of fraud; detected fraud is not total prevalence.
Dates belong to the selected release. A source check is an inspection, not new evidence. External source links open in a new tab.
Source 1 · Aviva
Qualification: Application and AI observations exclude Direct Line. Combined group claims totals have a different scope and are not used here.
Used for: Aviva reports growing use of AI-generated claims evidence
Geography / market: UK • Aviva, excluding Direct Line
Source 2 · Association of British Insurers
Qualification: The claims count is included. The monetary total conflicts with other published reporting and is withheld pending reconciliation.
Source 3 · Motor Insurers’ Bureau
Source 4 · LexisNexis Risk Solutions
Source 5 · Percayso Inform
Used for: Changing answers is a validation problem before it is an AI claim
Geography / market: UK
Source 6 · Entrust
Source 7 · Loqate
Source 8 · GOV.UK / DVLA
Source 9 · GOV.UK
Source 10 · HPI
Source 11 · GOV.UK / Companies House
Source 12 · Cifas
Source 13 · IFB
Source 14 · Shift Technology
Source 15 · Shift Technology
Source 16 · FRISS
Used for: Images and documents need their own integrity checks
Geography / market: International; UK validation required
Source 17 · InsureLab.ai
Qualification: Read from the rendered service page. Product assertions are supplier claims; no independent performance test has been supplied.
Used for: Application and claim evidence in the same review workflow
Geography / market: UK
Source 18 · Association of British Insurers
Qualification: ABI member detection reporting. Detected fraud is not total fraud incidence.
Source 19 · Association of British Insurers
Source 20 · Cifas
Qualification: Aggregate member reporting only; this is not a census of all UK fraud.
Source 21 · Official Injury Claim
Source 22 · Official Injury Claim
Qualification: Operational claims data. Exits can have several causes and must not be treated as fraud cases.
Source 23 · Department for Work and Pensions
Source 24 · Motor Insurers’ Bureau
Source 25 · Home Office
Source 26 · Home Office
Source 27 · Driver and Vehicle Standards Agency
Source 28 · Department for Transport
Source 29 · Department for Transport
Source 30 · Department for Transport
Source 31 · Financial Conduct Authority
Source 32 · Financial Conduct Authority
Source 33 · Financial Conduct Authority
Source 34 · Financial Ombudsman Service
Source 35 · City of London Police / IFED
Source 36 · City of London Police / IFED
Source 37 · City of London Police / IFED
Source 38 · City of London Police / IFED
Source 39 · City of London Police / IFED
Source 40 · City of London Police / IFED