Fraud detection rate

Fraud detection rate quantifies how well your fraud prevention systems identify and stop illegitimate transactions. Effective fraud detection protects revenue from chargebacks and fines while maintaining customer trust. However, overly aggressive rules create false positives that block legitimate customers and reduce charge success rate.

The metric should always be tracked alongside the false positive rate. A fraud detection rate of 99% is meaningless if it also blocks 10% of legitimate transactions. The goal is to maximise the detection rate while keeping false positives below a threshold that your business can tolerate.

Fraud patterns evolve constantly, so detection models require ongoing tuning. Analysing detection rate by fraud type (card testing, account takeover, friendly fraud) helps identify which vectors are well covered and which need additional rules or model training.

Fraud Detection Rate = (Blocked Fraudulent Transactions / Total Fraudulent Attempts) x 100

Calculating the denominator requires combining blocked attempts with fraudulent transactions that slipped through (identified via chargebacks or manual review). For example, if your system blocks 950 fraudulent attempts but 50 get through and result in chargebacks, the total fraudulent attempts are 1,000 and the detection rate is 95%.

1. 1

   Layer multiple detection signals

   Combine device fingerprinting, IP geolocation, velocity checks, and behavioural analysis. No single signal catches all fraud, but layered signals create a comprehensive defence.

2. 2

   Review and tune rules regularly

   Analyse blocked transactions monthly to identify false positives. Adjust thresholds and rules based on actual fraud outcomes rather than theoretical risk. Remove rules that generate more false positives than genuine catches.

3. 3

   Use machine learning models alongside rules

   ML models adapt to evolving fraud patterns faster than static rules. Use rules for known patterns and ML for emerging threats. Feed chargeback outcomes back into the model as training data.

4. 4

   Implement 3D Secure for high-risk transactions

   Apply strong customer authentication to transactions flagged as elevated risk. This shifts liability to the card issuer and provides an additional verification layer without applying friction to low-risk payments.