Start by clarifying the business context: transaction volume, latency requirements, fraud cost trade-offs, and existing infrastructure. Then design a layered system: real-time feature computation, a low-latency model for scoring, and a decision engine that maps scores to actions (approve, block, challenge, manual review). Finally, discuss how to handle feedback loops, model updates, and monitoring for drift and adversarial adaptation.
Pro tip: Emphasize the importance of separating the fraud score from the decision policy—the score is a model output, while the decision thresholds should be configurable and based on business costs. Also, mention that you'd start with a simple model (e.g., logistic regression) and iterate, rather than jumping to deep learning.
Ask about transaction volume, latency SLA (e.g., <100ms), acceptable false positive/negative rates, and integration with existing systems. Understand the cost of different errors and the need for explainability.
Outline real-time feature computation (e.g., velocity checks, user history, device fingerprinting) using a stream processing framework. Discuss feature store for consistency between training and serving.
Choose a model balancing accuracy and latency (e.g., gradient boosted trees or logistic regression). Address class imbalance, concept drift, and the need for periodic retraining with labeled data.
Map model scores to actions using thresholds optimized for business costs. Explain how to handle challenge (e.g., 3DS) and manual review queues, and how to incorporate rules for known fraud patterns.
Set up monitoring for latency, score distribution, and fraud rate. Use feedback from manual reviews and chargebacks to retrain models. Discuss A/B testing for policy changes.
AI-generated suggestions, not part of the candidate's original notes. May be inaccurate — verify before relying on them.