Context
A growing e-commerce marketplace was experiencing increasing fraud losses as sophisticated actors found ways around their rule-based detection system. Chargebacks were climbing while legitimate customers faced friction from overly aggressive blocking.
The Problem
Traditional rules couldn't keep pace with evolving fraud patterns. Fraudsters adapted faster than the team could write new rules. Meanwhile, false positives were damaging customer relationships and conversion rates. The team needed a system that could learn and adapt.
Why generic AI wouldn't work
Generic fraud detection services are trained on broad patterns. Marketplace fraud has unique dynamics — seller fraud, buyer fraud, and collusion patterns specific to the category and price points. The optimal sensitivity/specificity balance depends heavily on margins and customer lifetime value.
The System We Designed
- Real-time feature computation from transaction, device, and behavioral signals
- Ensemble model combining supervised fraud detection and anomaly detection
- Network analysis to identify coordinated fraud rings
- Adaptive thresholds based on transaction risk and customer value
- Feedback loop from chargebacks and manual reviews
Human-in-the-Loop & Explainability
Medium-confidence transactions routed to fraud analysts for review. Analyst decisions feed back into model training. High-value customers receive expedited review rather than automatic blocking. Fraud team maintains override capability for edge cases.
Outcomes
- 65% reduction in fraud losses
- False positive rate reduced from 4% to under 2%
- Customer complaints about blocked transactions down 50%
- Fraud analyst productivity up 3x with better case prioritization
Reference available upon request. Some details have been generalized to protect client confidentiality.
Facing a similar challenge?
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