Overview
Fraud detection is an asymmetric economic problem. A false positive (blocking a legitimate customer) destroys CX and lifetime value. A false negative (approving fraud) costs chargebacks, stolen goods, and fraud-network risk. Most models optimize for accuracy — which is the wrong objective. The right objective is expected loss minimization.
The Fraud Detection Model combines supervised classifiers (XGBoost on labeled fraud history) with unsupervised anomaly detection (Isolation Forest on novel patterns) into a two-stage scoring pipeline — calibrated against the actual economic cost of false positives vs. false negatives, with review tiers and feedback loops from chargeback outcomes.
What you get: - Feature engineering for transaction fraud (velocity, device, geolocation, graph) - Supervised model (XGBoost) on labeled fraud history - Unsupervised anomaly detection (Isolation Forest) for novel fraud - Two-stage scoring: block / review / approve - Cost-calibrated decision threshold (not accuracy-optimized) - Feedback loop from chargebacks and manual reviews - Model performance by fraud type (stolen card, account takeover, friendly fraud) - A/B testing framework for threshold changes
Built for: payments teams, e-commerce fraud analysts, marketplace trust & safety, and fintech risk teams who need detection that minimizes expected loss — not just a high AUC number that ignores unit economics.