How would you approach creating a machine learning model to detect anomalies in financial transactions in real-time?

Instruction: Describe the data preprocessing steps, feature engineering, model selection, and the deployment strategy for a low-latency, high-availability system.

Context: The question tests the candidate's skills in designing real-time, high-performance anomaly detection systems, crucial for financial fraud prevention.

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I would design this as a low-latency decision system with layered risk signals. Real-time financial anomaly detection has to score events quickly, explain why something looks suspicious, and decide whether to block, step up authentication, or send the case for review.

Feature design would combine transaction attributes, recent...

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How would you approach creating a machine learning model to detect anomalies in financial transactions in real-time?

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