Money Laundering Detection with Multi-Aggregation Custom Edge GIN

Detecting illicit transactions in Anti-Money Laundering (AML) systems remains a significant challenge due to class imbalances and the complexity of financial networks. This study introduces the Multiple Aggregations for Graph Isomorphism Network with Custom Edges (MAGIC) convolution, an enhancement of the Graph Isomorphism Network (GIN) designed to improve the detection of illicit transactions in AML systems. MAGIC integrates edge convolution (GINE Conv) and multiple learnable aggregations, allowing for varied embedding sizes and increased generalization capabilities. Experiments were conducted using synthetic datasets, which simulate real-world transactions, following the experimental setup of previous studies to ensure comparability. MAGIC, when combined with XGBoost as a link predictor, outperformed existing models in 16 out of 24 metrics, with notable improvements in F1 scores and precision. In the most imbalanced dataset, MAGIC achieved an F1 score of 82.6% and a precision of 90.4% for the illicit class. While MAGIC demonstrated high precision, its recall was lower or comparable to the other models, indicating potential areas for future enhancement. Overall, MAGIC presents a robust approach to AML detection, particularly in scenarios where precision and overall quality are critical. Future research should focus on optimizing the model’s recall, potentially by incorporating additional regularization techniques or advanced sampling methods. Additionally, exploring the integration of foundation models like GraphAny could further enhance the model’s applicability in diverse AML environments.