KPI
Fraud loss reduction: AI benchmark
Reduction in money lost to fraud or scams.
How to measure it
Gross fraud losses per period, normalised for volume, before and after.
Across the library
Median 40% across 5 deployments, reported range 30% to 82%.
Higher is better. Unit: percent.
Reported values by use case
AI scam intervention for instant payments
- Commonwealth Bank of Australia76% · organization claim
- Česká spořitelna40% · organization claim
- Revolut30% · organization claim
Real time fraud scoring for card and instant payments
- Commonwealth Bank of Australia76% · organization claim
- Revolut30% · organization claim
- Stripeat least 30% · organization claim
AI agent for fraud alert confirmation with cardholders
- Commonwealth Bank of Australia76% · organization claim
- Revolut30% · organization claim
AI for money mule account and network detection
- bunq82% · vendor claim
Use cases that should track fraud loss reduction
- AI agent for fraud alert confirmation with cardholders
- AI agent for payment initiation within a customer mandate
- AI for application and identity fraud detection
- AI for benefit fraud and error detection in social security
- AI for insurance claims fraud detection
- AI for money mule account and network detection
- AI for telecom fraud detection (SIM swap, IRSF and Wangiri)
- AI order fraud screening for ecommerce checkout
- AI scam intervention for instant payments
- AI spam and scam call blocking for mobile and landline subscribers
- AI underwriting and verification for auto loans
- Real time fraud scoring for card and instant payments