I went straight into talking about flagging anomalies in transaction volume and velocity, which felt right at the time.
Start by clarifying requirements such as scale, latency, and accuracy targets, then outline a high-level pipeline from data ingestion to real-time scoring and feedback loops. Focus on the trade-offs between rule-based and ML-based detection, and how to handle imbalanced data and evolving fraud patterns.
Pro tip: Emphasize the importance of a feedback loop where flagged transactions are reviewed and labels are fed back to retrain models, and discuss how to handle false positives to avoid customer friction.
Ask about transaction volume, latency requirements (real-time vs batch), acceptable false positive rate, and data available (e.g., user history, device info).
Propose a pipeline: data collection -> feature engineering -> model training -> real-time scoring -> alerting/review -> feedback loop. Mention components like Kafka, Flink, and a feature store.
Discuss using a combination of rules (for known fraud patterns) and machine learning models (e.g., gradient boosted trees, neural networks) to detect anomalies. Address class imbalance with techniques like SMOTE or class weights.
Explain how to serve models with low latency using a model server (e.g., TensorFlow Serving) and scale horizontally. Mention caching features and precomputing aggregates.
Describe monitoring for model drift, fraud pattern changes, and system performance. Set up A/B testing and continuous retraining with new labeled data.
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