KPI
False positive reduction: AI benchmark
Reduction in alerts that turn out not to be real risk.
How to measure it
False positive alerts per period, or false positive rate, before and after, at an equal or better detection rate.
Across the library
Median 60% across 7 deployments, reported range 40% to 95%.
Higher is better. Unit: percent.
Reported values by use case
AI agent for fraud alert confirmation with cardholders
- Macquarie Bank40% · vendor claim
AI for merchant underwriting and risk monitoring
- Airwallex50% · organization claim
AI for PEP and adverse media screening
- Scotiabank95% · vendor claim
AI for sanctions screening alert adjudication
- United Overseas Bank (UOB)60% · organization claim
Real time fraud scoring for card and instant payments
- NatWest Group75% · vendor claim
Use cases that should track false positive reduction
- AI agent for fraud alert confirmation with cardholders
- AI agent for fraud alert triage
- AI early warning and covenant monitoring for loan portfolios
- AI for AML transaction monitoring alert triage
- 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 market abuse surveillance alert triage
- AI for merchant underwriting and risk monitoring
- AI for money mule account and network detection
- AI for PEP and adverse media screening
- AI for sanctions screening alert adjudication
- AI for security alert triage and investigation in the SOC
- AI for software vulnerability triage and remediation
- AI for tax compliance risk scoring and audit selection
- AI for telecom fraud detection (SIM swap, IRSF and Wangiri)
- AI predictive maintenance for freight rail rolling stock
- AI predictive maintenance for industrial and energy assets
- AI quality inspection on the production line
- AI scam intervention for instant payments
- AI screening of trade finance transactions for trade based money laundering
- Real time fraud scoring for card and instant payments