The interviewer dropped hints along the way which made it more of a conversation than an interrogation.
Start by clarifying requirements and constraints, such as transaction volume, latency needs, and fraud types. Then design a scalable, real-time system with a rules engine and machine learning models, covering data ingestion, feature computation, scoring, and feedback loops. Discuss trade-offs between accuracy, latency, and cost, and how to handle false positives/negatives.
Pro tip: Emphasize the importance of a feedback loop where confirmed fraud cases are used to retrain models, and discuss how to handle imbalanced data and concept drift. Also, mention the need for explainability to support manual review and compliance.
Ask about scale (transactions per second), latency requirements (e.g., <100ms), fraud types (stolen cards, account takeover), and regulatory constraints. Define success metrics like precision, recall, and false positive rate.
Outline components: data ingestion (Kafka), feature store, rules engine, ML models, decision engine, and feedback loop. Ensure scalability and fault tolerance.
Discuss key features: user behavior, transaction patterns, device info, geolocation. Explain how to compute features in real-time and store them for training.
Describe how rules and ML models combine to produce a risk score. Set thresholds for approve, review, or decline. Discuss trade-offs between latency and accuracy.
Explain how to monitor model performance, detect drift, and incorporate feedback. Discuss A/B testing and continuous improvement.
AI-generated suggestions, not part of the candidate's original notes. May be inaccurate — verify before relying on them.