Design real-time fraud detection under 50ms

Q: Design real-time fraud detection under 50ms

This question evaluates expertise in designing low-latency, high-throughput ML-powered systems for online decisioning, covering competencies in real-time inference, feature pipelines, model serving, scaling, and operational reliability within the ML System Design domain.

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Question

Design a real-time fraud detection system for a payments company that processes millions of transactions per day.

Requirements:

For each incoming transaction, the system must decide Approve / Flag / Block .
End-to-end decision latency must be ≤ 50 ms per transaction.
Sustain 10,000+ requests/second (RPS) and tolerate promotional spikes (e.g., Black Friday) with high transaction success rate.
The ML model(s) must be updatable without downtime (no service interruption during model rollout).

Describe the architecture, data/feature flow, model serving strategy, scaling and reliability approach, and how you would operate/monitor the system in production.

Design real-time fraud detection under 50ms

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