Real-time fraud scoring for a payments platform
A machine learning pipeline that scores transactions in real time, replacing a static rules engine.
The challenge
A payments platform relied on a static rules engine for fraud detection, generating a high false-positive rate that frustrated legitimate customers while missing evolving fraud patterns.
Our approach
We built a feature pipeline over transaction and behavioral data, trained and evaluated gradient-boosted and anomaly-detection models, and deployed a scoring service with human-in-the-loop review for borderline cases.
Results
- Meaningful reduction in false-positive declines
- Faster detection of new fraud patterns via continuous retraining
- A monitored, explainable scoring pipeline replacing static rules