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HomeUnderstand what fraud statistics show—and what they do not

DATA & EVIDENCE

Understand what fraud statistics show—and what they do not

Evidence becomes useful when the source, measure, period and limitations remain visible.

What this hub is for

Fraud and scam figures are spread across surveys, police reports, bank data, regulatory publications and platform disclosures. They measure different things. A payment-loss total is not the same as the number of victims, and recorded reports are not the same as the true prevalence of harm.

The Data & Evidence hub brings those strands together so readers can see the source, the measure, the period and the important limitations before drawing a conclusion.

Core evidence routes

Understanding fraud statisticsHow to distinguish prevalence, reports, cases and losses.Data breachesWhat exposure can mean and what action is proportionate.Current campaignsHow named incidents and impersonation claims are checked.

Sources commonly used

Depending on the question, Scamology may refer to the Office for National Statistics, Report Fraud and the National Fraud Intelligence Bureau, UK Finance, the Payment Systems Regulator, the Financial Conduct Authority, the National Cyber Security Centre, Ofcom, Cifas and peer-reviewed research.

Inclusion does not imply that a source measures the whole scam landscape. Each source has a remit, reporting population, method and publication timetable.

Read the numbers carefully

Under-reporting is central to fraud measurement. Some people do not recognise the event as fraud. Others report only to a bank, platform or insurer. Definitions change, datasets overlap and methodology can create breaks in a time series.

For that reason, Scamology avoids turning one headline number into a claim about every scam or every person. Where estimates cannot be combined safely, they should remain separate.

What comes next

This hub will expand through source profiles, clearly labelled charts and short evidence notes. New material should show provenance, review dates and limitations rather than presenting a dashboard as self-explanatory.