Money Laundering Detection with Multi-Aggregation Custom Edge GIN

Wójcik, Filip

doi:10.6339/25-JDS1190

Journal of Data Science

Money Laundering Detection with Multi-Aggregation Custom Edge GIN

Filip Wójcik

https://doi.org/10.6339/25-JDS1190

Pub. online: 12 June 2025 Type: Data Science In Action

Open Access

Received
30 September 2024

Accepted
23 May 2025

Published
12 June 2025

Abstract

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.

Supplementary material

Supplementary Material

The source code for this study is available on GitHub: https://github.com/maddataanalyst/Graph_MAGIC_Conv. The repository includes all the necessary components to reproduce the training results. A supplementary PDF file attached to this publication provides detailed analyses of the train/validation/test splits, hyperparameter tuning results, and a comprehensive breakdown of the model architecture for each dataset.

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2025 The Author(s). Published by the School of Statistics and the Center for Applied Statistics, Renmin University of China.

Open access article under the CC BY license.

Keywords

deep learning financial fraud detection graph neural networks graph representation learning

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