Event-driven fraud detection system: A cloud-native architecture for real-time transaction analysis

This article presents event-driven fraud detection architectures implemented on cloud-native streaming platforms within financial services. It explores the evolution from traditional batch-oriented fraud detection methods to real-time, event-driven approaches that significantly reduce detection latency and improve prevention capabilities. The article explores the core architectural components of modern fraud detection systems, including data ingestion layers, stream processing engines, event sourcing patterns, and command-query responsibility segregation. It further shows implementation considerations such as platform selection criteria, integration patterns, containerization strategies, and auto-scaling mechanisms essential for handling variable transaction volumes. By synthesizing findings from recent industry research, this paper demonstrates how event-driven architectures on cloud-native platforms enable financial institutions to detect fraudulent activities with substantially improved accuracy and speed, while simultaneously reducing infrastructure costs and enhancing operational resilience.