Graph Neural Networks for Financial Fraud Detection: A Detailed Literature Review

In contemporary digital economies, financial fraud has become a significant problem that threatens the integrity of financial institutions and causes large financial losses. Owing to their incapacity to represent interactions between entities, traditional fraud detection techniques, such as rule-based systems and conventional machine learning models, frequently fail to identify intricate and changing fraud patterns. Graph Neural Networks (GNNs), which represent financial data as interconnected graphs with nodes and edges encoding entities and their interactions, have become a potent solution in recent years. This study presents an extensive evaluation of the literature on GNN-based methods for financial fraud detection. It examines important designs and evaluates how well they capture relational relationships in transaction networks, including Graph Convolutional Networks (GCN), Graph Attention Networks (GAT), and GraphSAGE. The assessment identifies key application areas, including anti-money laundering systems, transaction risk scoring, and fraud ring identification. The advantages of GNNs over conventional methods are also covered in this study, especially regarding accuracy and the capacity to find hidden patterns. It also highlights important issues, such as handling unbalanced datasets, scalability, and interpretability. The report concludes by summarizing current research trends and suggesting future approaches, such as the integration of XAI with real-time fraud detection systems.