Learn→Fraud Detection & Anomaly Detection→Handling Imbalanced Data (SMOTE, Class Weighting, Focal Loss)→6 of 6

Fraud Detection & Anomaly Detection • Handling Imbalanced Data (SMOTE, Class Weighting, Focal Loss)Hard⏱️ ~3 min

End-to-End Production Architecture for Imbalanced Data Systems

Training Pipeline Design
1. Split data chronologically (not randomly) to prevent leakage. 2. Analyze class distribution—if imbalance is moderate (1:10 to 1:100), class weighting may suffice. 3. For severe imbalance (1:1000+), combine techniques: undersample majority to 1:10, then apply class weighting or focal loss. 4. Validate on holdout with natural distribution.
Pipeline Order: Data cleaning → Chronological split → Training set rebalancing → Model training with loss weighting → Validation on natural distribution → Threshold tuning on validation set.
Threshold Tuning
Model outputs probabilities; the decision threshold determines precision-recall trade-off. Default 0.5 threshold is rarely optimal for imbalanced data. Plot precision-recall curve, identify threshold that matches business requirements (e.g., 95% precision minimum), apply that threshold in production. Retune thresholds when class distribution shifts.
Monitoring for Distribution Shift
Class distribution changes over time—fraud rate increases during holiday seasons, decreases after fraud ring takedowns. Monitor: prediction distribution, confirmed fraud rate, model confidence distribution. Alert when these diverge significantly from training distribution. Retrain with updated class weights when necessary.
Production Insight: Log model scores for all predictions. When fraud is confirmed, you have labeled examples for continuous learning. This feedback loop is more valuable than any rebalancing technique.
Calibration Considerations
Rebalancing techniques distort predicted probabilities. A model trained on 1:1 balanced data outputs 50% probability when the true population rate is 0.1%. Recalibrate probabilities using Platt scaling or isotonic regression on a validation set with natural distribution if accurate probability estimates are needed.

💡 Key Takeaways

✓Pipeline: clean → chronological split → training rebalancing → loss weighting → validate on natural distribution → tune threshold

✓Tune decision threshold on PR curve to match business requirements—default 0.5 is rarely optimal for imbalanced data

✓Recalibrate probabilities after rebalancing if accurate estimates needed—Platt scaling or isotonic regression

📌 Interview Tips

1For severe imbalance (1:1000+), combine: undersample majority to 1:10, then apply focal loss or class weighting

2Log all predictions; confirmed fraud creates feedback loop for continuous learning—more valuable than any rebalancing

← Back to Handling Imbalanced Data (SMOTE, Class Weighting, Focal Loss) Overview