A Graph-Based Deep Learning Model for the Anti-Money Laundering Task of Transaction Monitoring

Nazanin Bakhshinejad, Uyen Nguyen, Shahram Ghahremani, Reza Soltani

2024

Abstract

Anti-money laundering (AML) refers to a comprehensive framework of laws, regulations, and procedures to prevent bad actors from disguising illegally obtained funds as legitimate income. The AML framework encompasses customer identity verification and risk assessment, monitoring transactions to detect suspicious money laundering activities, and reporting suspicious transactions to regulators. In this paper, we focus on the transaction monitoring task of the AML framework. We propose a graph convolutional networks (GCN) model to classify transactions as legitimate or suspicious of money laundering. We tested and evaluated the model on a publicly available large dataset to promote reproducibility. The proposed model was trained and evaluated using the classification objectives for AML transaction monitoring per industry standard. We describe in detail our solutions to the class imbalance problem typical of AML datasets. We present comprehensive experiments to demonstrate and justify how the important parameters of the model were optimized and selected. This helps to support reproducibility and comparison with future work.

Paper Citation

in Harvard Style

Bakhshinejad N., Nguyen U., Ghahremani S. and Soltani R. (2024). A Graph-Based Deep Learning Model for the Anti-Money Laundering Task of Transaction Monitoring. In Proceedings of the 16th International Joint Conference on Computational Intelligence - Volume 1: NCTA; ISBN 978-989-758-721-4, SciTePress, pages 496-507. DOI: 10.5220/0013071700003837

in Bibtex Style

@conference{ncta24,
author={Nazanin Bakhshinejad and Uyen Nguyen and Shahram Ghahremani and Reza Soltani},
title={A Graph-Based Deep Learning Model for the Anti-Money Laundering Task of Transaction Monitoring},
booktitle={Proceedings of the 16th International Joint Conference on Computational Intelligence - Volume 1: NCTA},
year={2024},
pages={496-507},
publisher={SciTePress},
organization={INSTICC},
doi={10.5220/0013071700003837},
isbn={978-989-758-721-4},
}

in EndNote Style

TY - CONF

JO - Proceedings of the 16th International Joint Conference on Computational Intelligence - Volume 1: NCTA
TI - A Graph-Based Deep Learning Model for the Anti-Money Laundering Task of Transaction Monitoring
SN - 978-989-758-721-4
AU - Bakhshinejad N.
AU - Nguyen U.
AU - Ghahremani S.
AU - Soltani R.
PY - 2024
SP - 496
EP - 507
DO - 10.5220/0013071700003837
PB - SciTePress