Self-Adaptive Graph Analytics for Real-Time Fraud Detection in Transaction Networks

Financial fraud in large scale transaction networks has
become more sophisticated, and traditional static and rule-based systems are not anymore adequate to deal with dynamic and evolving fraudulent behaviors. The issue is that conventional graph-based and machine learning models struggle with the changing topology in real-time financial data streams which must be adaptable to fast changes in topology and concept drift. This study proposes a Self-Adaptive Graph Analytics (SAGA)
framework that is able to dynamically model transaction
networks with temporal graph learning, reinforcement-driven
topology adaptation and continuous online feedback.