How would you manage precision/recall for fraud detection?

Scenario

You own (or significantly contribute to) a production fraud detection system that flags transactions/users as fraud vs legit.

• The model outputs a fraud probability score $p(\text{fraud})$ .
• A decision threshold determines whether to block , step-up verify , or send to manual review .
• Labels may be delayed (chargebacks) and the data is highly imbalanced .

Questions

1. Precision/Recall management: What concrete methods have you used (or would you use) to measure, manage, and optimize precision and recall in a real fraud system?
2. False positives: How would you diagnose and reduce false positives (legit users being flagged) without letting fraud through?
3. Sudden fraud spike: If you suddenly observe many more fraud cases than usual, what changes would you make (model, thresholding, monitoring, operations), and how would you validate them quickly?
4. Specific fraud pattern: If fraud shows a very specific规律/pattern (e.g., a new attack vector with clear signatures), what would you do—rules, model features, segmentation, retraining—and how would you prevent overfitting to a short-lived pattern?

Please be explicit about:

• The primary metric vs diagnostic metrics vs guardrails you would use.
• How you handle cost asymmetry (FP vs FN), label delay , and distribution shift/adversarial adaptation .
• The trade-off between product/user experience and fraud loss .