Unveiling Hidden Money Laundering Networks: The Application of Graph Neural Networks in Financial Transaction Analysis

Abstract

Money laundering poses a persistent threat to global financial systems, enabling activities such as terrorism financing, corruption, and tax evasion. Traditional rule-based and machine learning approaches often fall short in detecting hidden laundering schemes due to their inability to capture complex multi-hop relationships and dynamic behaviors across financial networks. This paper investigates the application of Graph Neural Networks (GNNs) to detect concealed money laundering networks by modeling financial transactions as heterogeneous graphs, where accounts, customers, and institutions form nodes, and transactions, ownership, and associations define edges. Several advanced GNN architectures including Graph Convolutional Networks, Graph Attention Networks, and Heterogeneous GNNs are evaluated for their ability to identify suspicious activities, uncover intricate relational patterns, and adapt to evolving laundering tactics. Using evaluation metrics such as precision, recall, F1-score, AUROC, and AUPRC, the results demonstrate that GNNs significantly outperform traditional detection methods by reducing false positives and revealing camouflaged illicit activities across multiple accounts. Case studies further highlight the capacity of GNNs to expose complex laundering chains, offering both technical insights and practical implications. Beyond detection accuracy, this study addresses challenges related to scalability, interpretability, privacy, and adversarial evasion, while proposing mitigation strategies. Overall, the findings underscore the transformative potential of graph-based deep learning techniques for strengthening anti-money laundering frameworks, enhancing compliance infrastructures, and safeguarding trust in the global financial system. [1][2][3].