Graph Neural Networks for Multi-Layered Financial Crime Network Detection: An Explainable AI Framework for Anti-Money Laundering

Abstract

Detecting multi-layered financial crime networks remains a major challenge in anti-money laundering (AML) due to high false positives, evolving adversarial behaviors, limited relational modeling, and stringent regulatory requirements for transparency and auditability. This study presents a novel, integrated Graph Neural Network–Explainable Artificial Intelligence (GNN-XAI) framework optimized for multi-layered AML detection, explicitly addressing accuracy, scalability, privacy preservation, and regulatory compliance. A heterogeneous Graph Attention Network architecture models transaction flows, entity relationships, device linkages, and temporal interactions within complex financial ecosystems. Over 400 engineered features capture velocity patterns, behavioral deviations, network centrality, and geopolitical risk. The framework was validated on benchmark datasets (IEEE-CIS Fraud Detection, Kaggle Credit Card Fraud, Elliptic Bitcoin) and large-scale synthetic transaction graphs. Results demonstrate robust performance, achieving an AUC-ROC of 0.874, precision of 89.3%, recall of 82.1%, and F1-score of 0.857, outperforming XGBoost and conventional GCN baselines by 5–6%. Relational features accounted for over 51% of predictive contribution. SHAP, LIME, and attention-based explanations enabled regulator-ready interpretability, supporting compliance, auditability, and supervisory review. Scalability experiments confirmed stable performance on networks exceeding 5.6 million transactions with sub-millisecond inference latency, while federated learning and differential privacy ensured viable privacy-utility trade-offs. The findings demonstrate that GNN-XAI architectures provide a practical, regulation-aligned pathway for next-generation AML systems.