Anti-Money Laundering by Group-Aware Deep Graph Learning

Cheng, D; Ye, Y; Xiang, S; Ma, Z; Zhang, Y; Jiang, C

Anti-Money Laundering by Group-Aware Deep Graph Learning

Cheng, D Ye, Y Xiang, S

Ma, Z Zhang, Y

Jiang, C

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Publisher:: Institute of Electrical and Electronics Engineers (IEEE)
Publication Type:: Journal Article
Citation:: IEEE Transactions on Knowledge and Data Engineering, 2023, 35, (12), pp. 12444-12457
Issue Date:: 2023-12-01

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Field	Value	Language
dc.contributor.author	Cheng, D
dc.contributor.author	Ye, Y
dc.contributor.author	Xiang, S https://orcid.org/0000-0002-1454-8240
dc.contributor.author	Ma, Z
dc.contributor.author	Zhang, Y https://orcid.org/0000-0002-2674-1638
dc.contributor.author	Jiang, C
dc.date.accessioned	2024-02-15T06:23:27Z
dc.date.available	2024-02-15T06:23:27Z
dc.date.issued	2023-12-01
dc.identifier.citation	IEEE Transactions on Knowledge and Data Engineering, 2023, 35, (12), pp. 12444-12457
dc.identifier.issn	1041-4347
dc.identifier.issn	1558-2191
dc.identifier.uri	http://hdl.handle.net/10453/175688
dc.description.abstract	Anti-money laundering (AML) is a classical data mining problem in finance applications. As well known, money laundering (ML) is critical to the effective operation of transnational and organized crime, which affects a country's economy, government, and social wellbeings. Financial services organizations facilitate the movement of money and have been enlisted by governments to assist with the detection and prevention of money laundering, which is a key tool in the fight to reduce crime and create sustainable economic development. In the application of AML, user identity and financial behavior data are widely used to detect laundering transactions. In recent years, an increasing number of money laundering activities have been conducted by organized criminal gangs while most existing works still treat the actions of each account as independent identity behavior without considering the group-level conspired interactions. Therefore, in this paper, we propose a group-aware deep graph learning-based approach for organized money-laundering detection. In particular, we design a community-centric encoder to represent the nodes and attributes in user transaction graphs and derive the adjacent gang behaviors. Then, we devise a scheme of local enhancement to accommodate nodes with similar transaction features, which are aggregated into gangs for downstream detection. Extensive experiments on the real-world dataset from one of the largest bank card alliances worldwide show that our proposed method outperforms state-of-the-art methods in both offline and online modes, showing the effectiveness of money laundering detection with group-aware deep graph learning.
dc.language	en
dc.publisher	Institute of Electrical and Electronics Engineers (IEEE)
dc.relation.ispartof	IEEE Transactions on Knowledge and Data Engineering
dc.relation.isbasedon	10.1109/TKDE.2023.3272396
dc.rights	info:eu-repo/semantics/closedAccess
dc.subject	08 Information and Computing Sciences
dc.subject.classification	Information Systems
dc.subject.classification	46 Information and computing sciences
dc.title	Anti-Money Laundering by Group-Aware Deep Graph Learning
dc.type	Journal Article
utslib.citation.volume	35
utslib.for	08 Information and Computing Sciences
pubs.organisational-group	University of Technology Sydney
pubs.organisational-group	University of Technology Sydney/Faculty of Engineering and Information Technology
pubs.organisational-group	University of Technology Sydney/Strength - AAII - Australian Artificial Intelligence Institute
utslib.copyright.status	closed_access	*
dc.date.updated	2024-02-15T06:23:26Z
pubs.issue	12
pubs.publication-status	Published
pubs.volume	35
utslib.citation.issue	12

Abstract:

Anti-money laundering (AML) is a classical data mining problem in finance applications. As well known, money laundering (ML) is critical to the effective operation of transnational and organized crime, which affects a country's economy, government, and social wellbeings. Financial services organizations facilitate the movement of money and have been enlisted by governments to assist with the detection and prevention of money laundering, which is a key tool in the fight to reduce crime and create sustainable economic development. In the application of AML, user identity and financial behavior data are widely used to detect laundering transactions. In recent years, an increasing number of money laundering activities have been conducted by organized criminal gangs while most existing works still treat the actions of each account as independent identity behavior without considering the group-level conspired interactions. Therefore, in this paper, we propose a group-aware deep graph learning-based approach for organized money-laundering detection. In particular, we design a community-centric encoder to represent the nodes and attributes in user transaction graphs and derive the adjacent gang behaviors. Then, we devise a scheme of local enhancement to accommodate nodes with similar transaction features, which are aggregated into gangs for downstream detection. Extensive experiments on the real-world dataset from one of the largest bank card alliances worldwide show that our proposed method outperforms state-of-the-art methods in both offline and online modes, showing the effectiveness of money laundering detection with group-aware deep graph learning.

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http://hdl.handle.net/10453/175688