Real-Time Fraud Detection — ML System Design Interviews

Description: How do global payment platforms like Stripe, Visa, and PayPal process millions of transactions per second while instantly blocking fraudsters? In this video, we break down the end-to-end system design of a production-ready, real-time fraud detection platform built for sub-100ms latency and maximum accuracy. We will walk through the core technical challenges—balancing strict latency limits against high model precision—and how to construct a resilient infrastructure that handles transaction velocity, device fingerprinting, and risk scoring without failing. What you will learn in this video: • Core Architecture: Real-time stream processing pipelines vs. asynchronous batch layers. • The Dual Decision Engine: How to perfectly combine deterministic business rules with Machine Learning model scoring (XGBoost/LightGBM). • Feature Engineering at Scale: Storing and retrieving dynamic rolling windows (velocity, geo-distance) using Redis and Feature Stores. • Closing the Feedback Loop: Active case management queues for human analysts and feeding chargeback data back to retrain models.