Machine Learning for Core Banking System Anomaly Detection: From Batch to Stream Processing

This article examines the evolution of anomaly detection techniques in core banking systems, transitioning from traditional batch processing to modern stream processing approaches powered by machine learning. We explore how financial institutions have historically addressed fraud detection and system vulnerabilities, and detail the significant paradigm shift toward real-time analysis. The paper presents empirical evidence of increased detection efficiency, reduced false positives, and enhanced security posture in banking environments. Through case studies, technical implementations, and quantitative analysis, we demonstrate how stream processing architectures leveraging ML algorithms provide superior protection for modern banking infrastructure compared to conventional methods.