Anti-Money Laundering by Group-Aware Deep Graph Learning
Rattachement africain : cn, au. Niveau de preuve : code pays fourni par la source.
Le résumé fourni par la source
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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Le contrôle bibliographique ouvert
DOI retrouvé dans Crossref DOI retrouvé ; titre concordant.
- Titre Crossref
- Anti-Money Laundering by Group-Aware Deep Graph Learning
- Date Crossref
- 01/12/2023
- Éditeur
- Institute of Electrical and Electronics Engineers (IEEE)
- Type
- journal-article
Ce recoupement confirme des métadonnées liées au DOI. Il ne confirme ni la méthode ni les conclusions de l’étude, et il ne compte pas comme une seconde source scientifique indépendante.
Où se fait cette recherche
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Tongji University Department of Computer Science and Technology pays non établi dans la noticeUniversité ou école supérieure
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Beijing Academy of Artificial Intelligence pays non établi dans la noticeInstitution
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Shanghai Artificial Intelligence Laboratory pays non établi dans la noticeStructure de recherche
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University of Technology Sydney Australian Artificial Intelligence Institute pays non établi dans la noticeUniversité ou école supérieure
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Zhejiang Gongshang University pays non établi dans la noticeUniversité ou école supérieure
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UnionPay (China) pays non établi dans la noticeEntreprise
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School of Computer and Information Engineering pays non établi dans la noticeUniversité ou école supérieure
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China UnionPay Company Research and Development Center of Financial Safety pays non établi dans la noticeEntreprise
Department of Computer Science and Technology — Tongji University, Beijing Academy of Artificial Intelligence et Shanghai Artificial Intelligence Laboratory, avec 5 autres affiliations.
Une affiliation ne permet pas de déduire la nationalité d’un auteur.