Graph-Based Anomaly Detection in Trading and Payment Networks: A Cloud-Native Approach

Abstract

Graph-based anomaly detection leverages network theory to transform financial crime prevention by representing transactions and entities as interconnected structures rather than isolated events. The integration of graph analytics with cloud-native architectures enables financial institutions to identify sophisticated criminal activities that deliberately distribute operations across multiple accounts to evade traditional detection methods. By modeling the entire ecosystem of relationships between entities and their transactions, these systems reveal suspicious patterns through both structural anomalies and behavioral deviations, dramatically improving detection accuracy while reducing false positives. Cloud-native implementations provide the scalability, performance, and resilience required to process massive transaction volumes in real-time across global financial networks. This architectural approach fundamentally changes how financial institutions conceptualize and combat financial crime, moving from reactive investigation toward proactive prevention through earlier pattern recognition and contextual understanding of suspicious activities.