Katkat
Back

Design real-time fraud detection under 50ms

NVIDIASoftware Engineer · Technical Screen
EasySoftware EngineerTechnical ScreenML System Design

Last updated

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.

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.