Senior interview questionAsked at Capital One · interview write-up
Design a real-time card fraud detector with a 0.2% fraud rate, 14-day label delay, a 50 ms p95 latency budget and an explicit cost matrix for false positives and negatives.
Model answer
Score every authorisation inside the 50 ms budget: precompute velocity features, like recent spend per card, in a streaming feature store, and serve a gradient-boosted model in-process.
Labels lag 14 days, so train only on transactions old enough to be labelled, and monitor with faster signals like customer disputes. With 0.2% fraud, keep probabilities calibrated and set the threshold from the cost matrix: decline when the expected fraud loss exceeds the expected cost of blocking a good customer. Add rules, a review queue for the grey zone, and shadow-test new models.
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