Real-Time Fraud Detection: Latency, Features & Scale

This post elaborates on the real-time fraud detection capability by detailing the critical role of latency constraints in the scoring pipeline, the challenges of feature retrieval for fraud models, and the training-serving skew problem. It specifically highlights the use of Redis sorted sets for exact velocity counts and probabilistic data structures (Bloom filters, HyperLogLog, Count-Min Sketch) for memory-efficient counting. The post also emphasizes the architectural separation of event ingestion, stream processing for state maintenance, and low-latency feature stores like Redis for online inference.