Work out fraud detection savings instantly with clear inputs, formula shown and shareable results.
Fraud exposure is transaction volume times fraud rate times average loss, and a detection system converts a share of that into prevented loss. The residual is what still gets through. What this calculation deliberately omits is the cost of false positives — declined legitimate transactions and manual review effort — which often exceeds the marginal fraud saved once detection is already above 80 percent.
Fraud economics
fraudulent = transactions x fraud rate; exposure = fraudulent x average loss; prevented = exposure x detection rate
Raising recall on a rare class costs precision fast. Each extra percentage point of fraud caught can decline hundreds of good transactions, and lost customer lifetime value dwarfs the recovered amount.
Chargeback fees, manual review labour, and the cost of customer friction such as step-up authentication. Prevented loss alone overstates the net benefit.