The system processes 100,000+ transactions per second with end-to-end latency under 500ms (p99). Alert generation occurs within 1 second of fraud detection.
Real-time fraud detection system processing millions of transactions using Kafka, Spark Streaming, and machine learning for instant threat detection and prevention.
Built a comprehensive real-time fraud detection system using Apache Kafka and Spark Streaming to process millions of transactions per second. The system integrates machine learning models, Change Data Capture (CDC) with Debezium, and graph analytics using Neo4j for entity resolution and fraud ring detection. This enterprise-grade solution reduced fraud losses by 40% while maintaining sub-500ms end-to-end latency.
The system leverages PySpark Structured Streaming for millisecond-level latency processing with both micro-batch and continuous processing modes. It implements stateful operations including session windows for behavioral analysis, enabling sophisticated fraud detection patterns across multiple transaction windows.
Implemented multiple layers of fraud detection including threshold-based amount anomalies, merchant blacklisting, geographic velocity checks for impossible travel detection, behavioral analysis tracking card velocity and repeat failures, and network analysis using Neo4j for identifying fraud rings through graph traversal algorithms.
The architecture consists of Kafka for distributed streaming, PySpark for real-time processing, Debezium for CDC from PostgreSQL/MySQL sources, Neo4j for entity resolution and fraud ring detection, and comprehensive observability using DataDog, Prometheus, and Grafana. The system processes 100k+ transactions per second with end-to-end latency under 500ms (p99).
PySpark Structured Streaming processing 100k+ transactions/sec with <500ms latency.
Neo4j graph database identifying fraud rings and collusion networks across accounts.
Debezium connectors for real-time sync from source databases without batch windows.
LLM integration and behavioral analysis for advanced fraud pattern recognition.
Benchmark results on 3-node cluster demonstrated impressive performance: 100k+ transactions per second throughput, end-to-end latency under 500ms (p99), alert latency under 1 second after fraud detection, and checkpointing overhead under 2%. The system successfully reduced fraud losses by 40% while maintaining high availability and reliability through comprehensive monitoring and observability.