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Fraud Detection & Anomaly Detection • Supervised Anomaly Detection (Imbalanced Classification)Medium⏱️ ~3 min

Threshold Tuning and Cost Sensitive Decision Making

From Scores to Decisions
Fraud models output probability scores between 0 and 1. Business value comes from converting scores into actions through thresholds. The key insight: different errors have vastly different costs.
Missing a ,000 fraud costs ,000 plus a  chargeback fee. Blocking a legitimate  transaction costs customer frustration and potential churn. Sending a transaction to human review costs -5 in analyst time. Optimal thresholds balance these costs.
Multi-Threshold Approach
Rather than a single approve/decline threshold, production systems use multiple thresholds creating decision bands. Below 0.05: auto-approve. 0.05 to 0.30: approve but flag for post-transaction review. 0.30 to 0.70: route to human analyst for real-time decision. Above 0.70: auto-decline.
Each band has different cost structures. Auto-decisions cost nothing in labor. Human review costs -5 per transaction but catches errors before they become chargebacks. The middle band width depends on analyst capacity and transaction volume.
Dynamic Thresholds
Optimal thresholds vary by context. High-value transactions warrant more caution: lower the auto-approve threshold. New accounts without history need stricter thresholds. Peak traffic periods might raise auto-approve threshold to maintain analyst queue depth.
💡 Threshold Math: If fraud loss = , false positive cost =  (lost sale), then optimal threshold = false_positive_cost / (fraud_loss + false_positive_cost) = 50/5050 ≈ 0.01. Block when P(fraud) > 1%.
Calibration Matters
Threshold math assumes calibrated probabilities. If the model says 10% fraud probability, 10% of those transactions should actually be fraud. Uncalibrated models break threshold logic. Validate calibration with reliability diagrams plotting predicted vs actual fraud rates in score buckets.

💡 Key Takeaways

✓Different errors have different costs: missing K fraud ≠ blocking legitimate transaction

✓Use multiple thresholds: auto-approve, flag for review, human decision, auto-decline bands

✓Dynamic thresholds vary by context: stricter for new accounts, high-value transactions

✓Optimal threshold = false_positive_cost / (fraud_loss + false_positive_cost)

✓Threshold math requires calibrated probabilities; validate with reliability diagrams

📌 Interview Tips

1Show multi-threshold bands: <0.05 auto-approve, 0.05-0.30 flag, 0.30-0.70 human, >0.70 decline

2Calculate optimal threshold: FP cost, fraud loss → threshold = 50/5050 ≈ 1%

3Mention calibration: if model says 10% fraud, 10% of those should actually be fraud

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