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

Label Delay and Feedback Loops: The Hidden Challenges of Fraud Detection

The Label Delay Problem
Chargebacks arrive 30-90 days after the transaction, sometimes over 120 days for international cards. During this delay, you are training models on incomplete data and making decisions that determine which future labels you will see.
A transaction blocked today never gets a chargeback because it never happened. You know the model flagged it, but you do not know if it would have been fraud. Your training data contains only the outcomes of transactions you approved, not the full distribution of fraud attempts.
Feedback Loops
The feedback loop works like this: if you auto-block transactions with scores above 0.15, you never learn whether those blocked transactions were actually fraud. Your model sees 100% precision on blocked transactions (no chargebacks!) but this is artificial. You are measuring your intervention, not the true fraud rate.
⚠️ The Trap: Over time, the model learns patterns that predict "will be blocked" rather than "is fraud." It optimizes for its own decisions, not for actual fraud detection. Precision appears to improve while true detection degrades.
Mitigating Feedback Bias
Exploration traffic: Reserve 1-5% of traffic for random approval regardless of score. These transactions reveal true fraud rates in the blocked population. Expensive (you eat some fraud losses) but essential for unbiased evaluation.
Propensity weighting: When training, weight each example by the inverse of its approval probability. A transaction that was 90% likely to be blocked but got approved counts 10× more than one that was 90% likely to be approved.
Fast feedback proxies: Use signals that arrive faster than chargebacks. Network risk codes, velocity patterns, and merchant reports arrive within hours or days. These proxies are noisy but provide earlier signal for model iteration.

💡 Key Takeaways

✓Chargebacks arrive 30-90+ days after transaction; training data is incomplete during this delay

✓Blocked transactions never get chargebacks so you never learn if they were actually fraud

✓Feedback loops make model optimize for 'will be blocked' instead of 'is fraud'

✓Reserve 1-5% exploration traffic for random approval to get unbiased fraud rates

✓Use propensity weighting and fast feedback proxies (network codes) for earlier signals

📌 Interview Tips

1Explain the feedback trap: model sees 100% precision on blocks but measures intervention, not fraud

2Describe exploration traffic: 1-5% random approvals to learn true fraud rate in blocked population

3Mention propensity weighting: weight by inverse approval probability to correct for selection bias

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