Financial Crime and Fraud Risk
The Global Risks Alliance (GRA)
Payments fraud takes money through unauthorised transactions, such as stolen card use and account takeover, and through authorised push payment fraud, where victims are tricked into sending funds. Instant and irrevocable payments raise the stakes. This page sets out how payments fraud develops and moves between channels, how it is measured, who bears losses and which institutions decide on controls and reimbursement.
GRA · In development · service level R0
Payments fraud is the theft of money through payment systems, either by unauthorised transactions such as stolen card use or by tricking victims into authorising payments. Instant payments, mule networks and liability rules shape it. Payment regulators, fraud functions, scheme operators and law enforcement hold the decisions.
How a failure at one financial centre can spread through settlement links. Design links, not real dependencies.
The working screen behind this subject: its structure, terms and states. Names are placeholders and values illustrative.
Payments fraud · Bank B monitoring · Today
red: accounts under review
Fraud in digital transactions. The dossier below sets out how the risk develops, what is measured, where the data come from, how it is modelled and who decides.
Instant payments settle in seconds and cannot easily be recalled. Fraudsters move funds through mule accounts before victims or banks notice, so prevention must happen before release.
Strong authentication cut card-not-present and unauthorised fraud in several markets, and criminals shifted to manipulating customers into authorising payments themselves.
Stolen funds pass through accounts held by recruited, coerced or synthetic mules. Detecting and closing mule accounts limits the fraud economy.
Who reimburses victims, the sending bank, the receiving bank or no one, decides how much each invests in prevention. Rules differ sharply between jurisdictions.
Each term as this page uses it. Where a standard-setting body defines the term, the wording follows that body.
| Term | Definition |
|---|---|
| Unauthorised fraud | A payment made without the account holder's consent, such as with stolen card details. |
| Verification of payee | A check that the payee name matches the account details before a payment is sent. |
| Mule account | An account used to receive and move criminal proceeds on behalf of others. |
Each indicator carries its unit, its cadence, the kind of source behind it and the evidence label a value would carry. No values are shown: a programme supplies them under its own authority.
| Indicator | Unit | Cadence | Source type | Label |
|---|---|---|---|---|
| Fraud losses by payment type | currency per year | annual | Industry and central bank reports | REPORTED |
| Fraud rate on card payments | fraud value as share of transaction value | annual | ECB and scheme reports | OFFICIAL |
| Authorised push payment fraud cases | count and value | half-yearly | Industry reports | REPORTED |
| Reimbursement rate to victims | share of losses returned | periodic | Regulator data | OFFICIAL |
| Mule accounts identified | count | annual | Industry and law enforcement data | REPORTED |
| Payments subject to verification of payee | share of credit transfers | periodic | Scheme data | REPORTED |
The topic as a computational graph: named sources feed the methods, an evidence review gates the outputs, and the decisions stay with the authorities that hold them.
Fig. 02Nodes carry their type, two metric rows and a state; connectors show what feeds what; dashed frames name the owners; the timeline is the cadence. Design of the pipeline, not a record of a run.
Named public datasets and institutional records, each with its licence class. Sources by agreement are used only under their terms and never republished.
| Source | What it gives | Licence class |
|---|---|---|
| ECB and EBA report on payment fraud | Payment fraud rates by instrument in the European Economic Area | Free, EU reuse terms |
| UK Finance annual fraud report | Unauthorised and authorised push payment fraud statistics | Free to read, UK Finance terms |
| Payment Systems Regulator APP fraud reimbursement data | Reimbursement under the UK requirement | Free, PSR terms |
| FTC Consumer Sentinel data | Reported fraud by payment method in the United States | Public domain |
| Card scheme and central bank payment security reports | Card fraud trends | Free to read, terms per publisher |
Financial Crime and Fraud Risk prepares evidence. The decisions below are taken by the authorities named, under their own rules, and never by the platform.
| Who decides | The question | Evidence this page serves |
|---|---|---|
| Payment regulators and legislators | Who reimburses victims and what prevention duties apply | Loss data, reimbursement outcomes and comparative rules |
| Bank and payment firm fraud functions | Which controls to apply before releasing payments | Fraud patterns, friction cost and detection performance |
| Payment scheme operators | Whether to mandate verification of payee or data sharing | Scheme data and fraud trends |
| Law enforcement | How to target mule networks and organisers | Intelligence from reports and industry data |
Payments fraud researchers ask how to stop fraud without stopping legitimate payments.
Instant payments are valuable because they are fast and final, but those features also make fraud harder to stop and reverse. As controls on card fraud and account takeover improved, fraud shifted towards deceiving customers into authorising payments, which bypasses authentication. Fraudsters use social engineering, impersonation of banks, police or family members, and fake investments or purchases.
Proceeds move quickly through money mule accounts, often at several institutions. Detection relies on monitoring behaviour, devices and payment patterns, and on intelligence sharing. Rules on reimbursement shape incentives: where receiving institutions share liability, they have stronger reasons to prevent mule accounts.
Machine learning models scoring payments for fraud risk, using artificial intelligence and machine learning. False positives create friction; models degrade as fraud adapts.
Detecting mule networks across accounts with network science. Requires data across institutions.
Describing fraud methods to inform controls. Must avoid enabling crime.
Industry and official fraud data. Definitions and reporting vary.
The current service level is R0: information and enquiries. An enquiry receives a reference and names the next step; applications, payments, compute allocations and registrations open only when their team, terms and review capacity exist.