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Fraud Detection & Anomaly Detection • Supervised Anomaly Detection (Imbalanced Classification)Easy⏱️ ~2 min
Supervised Anomaly Detection: Why Accuracy Is Misleading in Imbalanced Classification
Supervised anomaly detection treats rare event detection as an imbalanced classification problem where you have labeled examples of both normal and anomalous events. The defining challenge is extreme class imbalance: anomalies typically represent 0.01% to 1% of all events in production systems. A fraud detection system processing 10,000 transactions might see only 1 to 10 actual fraud cases.
This imbalance makes accuracy a misleading metric. A model that predicts everything as normal achieves 99.5% accuracy when fraud is 0.5% of transactions, yet catches zero fraud. The real goal is maximizing business value under tight precision and recall constraints. Precision measures how many alerts are real fraud (avoiding wasted investigation costs), while recall measures what fraction of actual fraud you catch (avoiding financial losses).
Production systems score events by risk rather than making binary decisions. Stripe Radar and PayPal produce calibrated probability scores from 0 to 1, then apply business logic to convert scores into actions. Events below 0.02 might auto approve, scores from 0.02 to 0.15 route to human review, and scores above 0.15 auto block. These thresholds are tuned using cost simulations that balance chargeback fees (typically $15 to $100 per fraud), reviewer costs ($2 to $5 per case), and customer friction from false declines.
The supervised approach works when fraud patterns recur and you can collect representative labels. It excels over unsupervised methods when you have explicit cost functions and enough historical anomalies to learn from. Payment processors accumulate millions of labeled transactions over months, making supervised learning highly effective despite the severe imbalance.
💡 Key Takeaways
•Anomalies represent 0.01% to 1% of events in production, making 99% accuracy meaningless if it catches zero fraud
•Evaluation uses Precision/Recall/F1 and Precision Recall Area Under Curve (PR AUC) instead of accuracy or ROC AUC
•Systems produce calibrated risk scores (0 to 1 probabilities) rather than binary classifications
•Thresholds convert scores to actions: Stripe keeps review queues under 1 to 2% of traffic due to $2 to $5 per case cost
•Cost functions balance chargeback losses ($15 to $100), investigation costs, and false positive customer friction
•Supervised methods require representative labeled anomalies and work best when patterns recur over time
📌 Examples
Payment fraud: 0.1% base rate means 10 fraud cases per 10,000 transactions. Model achieving 80% recall with 50% precision catches 8 fraud ($800 saved) but generates 8 false alarms ($40 investigation cost)
Amazon marketplace fraud detection targets 90% precision in review band to justify $3 per case investigation cost, even if recall drops to 65%
Uber trip safety scoring uses thresholds of 0.01 for auto approve, 0.01 to 0.08 for secondary checks, and above 0.08 for blocking before trip starts