Design a traditional fraud detection system
PayPalMachine Learning Engineer · Onsite
Last updated
This question evaluates a Machine Learning Engineer's competency in end-to-end ML system design for real-time payments fraud detection, including labeling under delayed confirmations, handling extreme class imbalance and sampling, feature engineering across behavioral, graph, device and merchant signals, model selection for latency and scale, and production scoring and monitoring architecture. It is commonly asked in the ML System Design category to assess how an engineer balances low-latency decision-making with delayed sparse labels, calibration and threshold trade-offs, operational scalability and resiliency, and drift/adversarial detection, testing both conceptual understanding and practical application.
Design an end-to-end fraud detection system. Specify positive/negative labeling strategy given delayed and scarce fraud confirmations, sampling to address extreme class imbalance, feature sets (behavioral, graph, device, merchant), model choices and justification, real-time scoring architecture and latency constraints, thresholding and precision/recall trade-offs, evaluation metrics (PR-AUC, precision@k, cost-sensitive metrics), and monitoring for drift and adversarial adaptation.
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