Start by clarifying requirements and defining the Fraud Event schema, then design the ingestion and detection logic. Walk through the data model, storage, and algorithms for identifying fraud, emphasizing scalability and real-time processing.
Pro tip: Discuss trade-offs between rule-based and ML-based detection, and how you'd handle false positives and evolving fraud patterns. Mention idempotency and exactly-once processing to show production maturity.
Ask about data volume, velocity, sources, and latency requirements. Determine if detection is real-time, batch, or both, and what constitutes a fraud case.
Define fields like event_id, timestamp, user_id, transaction_amount, merchant, location, device_info, and fraud_label. Choose a schema (e.g., Avro, Protobuf) and storage (e.g., Kafka, Cassandra) that supports fast writes and queries.
Describe how to record events: use a message queue for buffering, validate and enrich data, then persist to a scalable store. Ensure idempotency and exactly-once semantics.
Outline algorithms: rule-based (velocity checks, blacklists), statistical (anomaly detection), and ML models. Explain how to identify fraud cases from stored events, possibly using batch jobs or stream processing.
Discuss partitioning, indexing, and caching for performance. Include monitoring for false positives/negatives, feedback loops, and model retraining.
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