fraud detection

Severe class imbalance, a tight latency budget, and an adversary who keeps adapting. What interviewers watch for: treating imbalance honestly, setting the operating point from costs, and building for the feedback loop. This covers the end-to-end design.

Fraud is well under 1% of transactions, the labels land weeks late as chargebacks, and the fraudsters deliberately adapt to your model. Optimizing accuracy hands you a model that approves everything. The interview is about dollars-at-risk thresholding, label delay, and a review queue with a fixed headcount.