Graph Neural Networks for Anti-Money Laundering (AML) Detection in Global Financial Networks
Keywords:
Graph Neural Networks, anti-money laundering, financial networks, anomaly detectionAbstract
Secret transaction rings, shell corporations, and cross-border laundering schemes in complicated worldwide financial networks are detected using GNN-based AML. The research adds network topology, node embeddings, and relational structures to graph-based anomaly detection models to identify minor deviations that rule-based systems miss. Financial information sharing federation protects data and norms. GNN designs—GCNs, GATs, and heterogeneous graph models—capture synthetic and real-world transaction datasets' higher-order linkages, community structures, and temporal transaction dynamics. Precision, recall, and detection delay outperform statistics and machine learning. This project creates scalable, interpretable, and adaptable worldwide banking AML monitoring solutions.
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References
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