GraphGuard: A Graph Neural Network Framework for Financial Fraud Detection

This paper introduces GraphGuard, a novel approachto detecting fraudulent financial transactions using graphneural networks (GNNs). the framework constructs transactiongraphs from raw financial data, capturing the intricaterelationships between entities. By applying graph convolutionaltechniques, GraphGuard identifies anomalous patterns that traditionalmethods often overlook. Extensive experiments on realworlddatasets reveal that the method significantly improves bothprecision and recall over conventional fraud detection systems.The results underscore the potential of graph-based learning tooffer a scalable and interpretable solution for real-time financialfraud monitoring.