Graph Neural Networks for Financial Fraud Detection: A Comprehensive Review

Abstract

Financial fraud detection has evolved from traditional rule-based systems to sophisticated machine learning approaches, with Graph Neural Networks (GNNs) emerging as a powerful paradigm for modeling complex relational patterns in financial data. This comprehensive review examines recent advances in GNN-based fraud detection systems, analyzing ten state-of-the-art methods published between 2023-2025. We systematically categorize GNN architectures into memory-augmented, heterogeneous, temporal-aware, and community-detection frameworks. Key innovations include adaptive sampling mechanisms, risk diffusion models, attention-based aggregation, and semi-supervised learning approaches. Our review identifies critical research gaps including model interpretability, real-time processing constraints, adversarial robustness, and cross-domain generalization. We conclude with future directions emphasizing federated learning, explainable AI, and hybrid architectures that balance accuracy with computational efficiency.

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