API · Machine Learning · Fintech
A real-time transaction fraud detection API that scores each payment across eight independent risk signals — returning a fraud probability, risk tier, and plain-language explanation in under 80ms.
80ms
Response Time
8
Risk Signals
4
Risk Tiers
ML
Weighted Model
Most fraud detection happens through post-hoc chargeback analysis — by which point the loss has already occurred. Payment processors and fintechs need a way to score transactions before they clear, in real time, without adding latency that kills conversion rates. Building this in-house requires data science infrastructure most teams don't have.
Each transaction is evaluated across eight independent risk dimensions: spending pattern deviation, device fingerprint recognition, geographic anomaly, transaction frequency, IP reputation history, time-of-day patterns, amount threshold analysis, and transaction category risk. A weighted ML model combines these signals into a single fraud probability score (0–1).
The API returns not just a score but a four-tier classification (LOW / MEDIUM / HIGH / CRITICAL) and a plain-language explanation of the primary signals that drove the decision — giving operations teams actionable context, not just a number. HMAC-SHA256-signed webhooks fire on critical-tier events. A chargeback feedback loop feeds confirmed fraud cases back into model retraining.
FraudDetect API integrates in a single API call with no ML infrastructure required. Deployed on Railway with RapidAPI marketplace distribution and a full OpenAPI spec. The freemium tier covers prototyping; Pro unlocks fraud network detection across devices and IPs, custom rule engine, and the merchant analytics dashboard — everything a payment processor needs to run fraud ops at scale.