I went in expecting something Meta-flavored and completely underestimated how deep the payments domain knowledge would matter here.
Start by clarifying requirements and scale, then design a layered system with real-time scoring and offline analysis. Focus on trade-offs between latency, accuracy, and cost, and explain how you'd measure success with metrics like precision/recall and fraud loss rate.
Pro tip: Emphasize that fraud detection is an adversarial problem: attackers adapt, so you need continuous monitoring, feedback loops, and the ability to quickly deploy new rules/models without downtime.
Ask about transaction volume, latency requirements, fraud types, and business impact. Establish metrics like false positive rate and detection rate.
Propose a streaming pipeline for real-time scoring and a batch pipeline for model training and historical analysis. Include data stores, feature engineering, and decision engine.
Design how to evaluate transactions in <100ms using a combination of rules, ML models, and velocity checks. Discuss feature computation and caching.
Explain how to train models on labeled data, handle imbalanced classes, and incorporate analyst feedback and chargebacks to continuously improve.
Define key metrics (precision, recall, F1, fraud loss, false positive rate) and describe dashboards, alerting, and A/B testing for new models.
AI-generated suggestions, not part of the candidate's original notes. May be inaccurate — verify before relying on them.