Graph Neural Network-Based Anomaly Detection in Financial Transaction Networks

Abstract

Modern financial ecosystems face unprecedented challenges in detecting sophisticated fraud schemes that exploit complex transaction networks. This paper presents a comprehensive approach utilizing Graph Neural Networks (GNN) to identify anomalous patterns in financial transaction networks. Our methodology constructs heterogeneous graph representations of financial transactions, incorporating temporal dynamics and multi-entity relationships. The proposed adaptive GNN architecture integrates attention mechanisms for suspicious pattern identification and handles dynamic graph structures effectively. Experimental validation demonstrates superior performance compared to traditional machine learning approaches, achieving 94.7% precision and 92.3% recall in fraud detection tasks. The framework addresses scalability concerns while maintaining interpretability requirements for regulatory compliance. Our approach successfully identifies complex fraud networks and money laundering schemes that evade conventional detection methods. The research contributes novel graph construction techniques, adaptive neural network architectures, and comprehensive evaluation methodologies for financial anomaly detection. Results indicate significant improvements in both accuracy and computational efficiency, making real-time deployment feasible for large-scale financial institutions.