On the potential of a graph attention network in money laundering detection
Journal of Money Laundering Control
ISSN: 1368-5201
Article publication date: 4 October 2021
Issue publication date: 8 June 2022
Abstract
Purpose
In a classroom, a support vector machines model with a linear kernel, a neural network and the k-nearest neighbors algorithm failed to detect simulated money laundering accounts generated from the Panama papers data set of the offshore leak database. This study aims to resolve this failure.
Design/methodology/approach
Build a graph attention network having three modules as a new money laundering detection tool. A feature extraction module encodes these input data to create a weighted graph structure. In it, directed edges and their end vertices denote financial transactions. Each directed edge has weights for storing the frequency of money transactions and other significant features. Social network metrics are features of nodes for characterizing an account’s roles in a money laundering typology. A graph attention module implements a self-attention mechanism for highlighting target nodes. A classification module further filters out such targets using the biased rectified linear unit function.
Findings
Resulted from the highlighting of nodes using a self-attention mechanism, the proposed graph attention network outperforms a Naïve Bayes classifier, the random forest method and a support vector machines model with a radial kernel in detecting money laundering accounts. The Naïve Bayes classifier produces second accurate classifications.
Originality/value
This paper develops a new money laundering detection tool, which outperforms existing methods. This new tool produces more accurate detections of money laundering, perfects warns of money laundering accounts or links and provides sharp efficiency in processing financial transaction records without being afraid of their amount.
Keywords
Acknowledgements
The author Chang-Yu Li has received a funding about NT$ 42000. It serves as a scholarship to him. This scholarship has a Grant No. 109-2813-C-309-012-H No other fundings!
Citation
Sheu, G.-Y. and Li, C.-Y. (2022), "On the potential of a graph attention network in money laundering detection", Journal of Money Laundering Control, Vol. 25 No. 3, pp. 594-608. https://doi.org/10.1108/JMLC-07-2021-0076
Publisher
:Emerald Publishing Limited
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