Real-Time Fraud Detection Engine
Impact95% fraud catch rate
Throughput10K TPS
Latency<100ms
Catch Rate95%
Overview
Designed and implemented a real-time fraud detection system for a fintech startup processing high-volume payment transactions. The system uses ML models and rule-based filters to flag suspicious transactions instantly.
The Challenge
The startup was experiencing growing fraud losses as transaction volume scaled, and existing batch processing was too slow to catch fraud in real-time.
The Solution
Built a streaming pipeline using Apache Kafka and Flink, with ML models deployed on Kubernetes for low-latency inference. Implemented adaptive thresholds that learn from analyst feedback.
Key Results
Processing 10K+ transactions/second
<100ms end-to-end latency
95% fraud catch rate with 2% false positive rate
Tech Stack
Apache KafkaApache FlinkPythonTensorFlowKubernetesRedisPostgreSQL