Fraud Signal Calibration Audit (QA)

Fraud Signal Calibration Audit (QA)

1. 01

   Inventory all fraud signal sources feeding the client's risk stack

   List every device fingerprint, IP, email, and behavioral signal currently in use, including those from Fingerprint and IPQS, and map them to the specific transaction or login decision they inform.

2. 02

   Benchmark current signal accuracy against a 30-day baseline

   Pull historical true positive, false positive, and chargeback rates for each signal source to establish a performance floor before any tuning begins.

3. 03

   Test signal resilience against spoofing and traffic shifts

   Run controlled simulations with VPNs, incognito browsers, and emulated devices to see which signals degrade, and compare results across peak and off-peak traffic periods.

4. 04

   Review vendor documentation for known limitations and update cadence

   Check release notes and support pages from Fingerprint and IPQS for recent changes to signal algorithms or data sources that could affect accuracy.

5. 05

   Cross-validate high-risk flags with manual review on a sample set

   Manually inspect 50 to 100 flagged transactions or logins per week to confirm whether automated decisions align with actual fraud patterns.

6. 06

   Adjust risk thresholds based on client chargeback tolerance

   Raise or lower score cutoffs to match the client's acceptable chargeback rate, typically between 0.5% and 1% of monthly revenue, and document the rationale.

7. 07

   Document calibration results and share a risk posture summary with the client

   Produce a one-page report showing signal accuracy, threshold changes, and residual fraud exposure so the client understands the trade-offs in the delivery process.