Kafka Event Sourcing for Real-Time Risk Analysis

Abstract

In the age of hyperconnected systems and increasing regulatory scrutiny, real-time risk analysis has become a cornerstone of modern enterprise operations. This paper introduces a novel architecture combining Apache Kafka and event sourcing to facilitate dynamic, resilient, and scalable risk analytics. By leveraging Kafka's distributed log capabilities with immutable event streams, the system enables instant state reconstruction, auditability, and fault tolerance. We propose a domain-specific event model optimized for risk evaluation and demonstrate its efficacy in high-throughput environments, such as financial fraud detection and cybersecurity.