Mid-level interview questionAsked at Shopify · interview write-up
Design an end-to-end fraud detection model, covering label definition, severe class imbalance, label delay, metrics and the thresholds for action.
Model answer
Labels: chargebacks and confirmed fraud are positives, but they arrive weeks later, so train only on orders whose labels have matured. Imbalance: keep every positive, downsample negatives, then re-weight or recalibrate. Features: payment, device, address mismatches and velocities, in gradient-boosted trees. Metrics: precision-recall AUC, not accuracy, and recall at your review capacity. Thresholds come from costs: approve, review or block, set by expected fraud loss against lost good orders.
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