A Novel Java-Based Framework for Real-Time Financial Risk Assessment and Anomaly Detection Using Apache Kafka and Apache Flink

Abstract

Modern financial systems create massive amounts of real-time data from high-frequency trading platforms, market feeds, and transactional systems. To address these issues, this paper proposes a novel integrated framework for real-time financial risk assessment and anomaly detection. It is built upon the high-throughput fault-tolerant messaging system Apache Kafka and low-latency stateful stream processor Apache Flink. The framework is built in Java, which guarantees performance, painless integration with enterprise systems, and scalability to meet the future development of market conditions. Extremely low latency, high throughput, and good detection accuracy have been demonstrated in extensive experiments operated over synthetically generated datasets that aim to approximate realistic market conditions, as well as with injected anomalies. The system’s modular architecture and innovative statistical techniques it implements serve as a solid basis for the future refinement with methods such as deep learning integration and adaptive thresholding.