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Anti-Money Laundering by Group-Aware Deep Graph Learning

  • Published:2023-12-01 Issue:12 Volume:35 Page:12444-12457
  • ISSN:1041-4347
  • Container-title:IEEE Transactions on Knowledge and Data Engineering
  • language:
  • Short-container-title:IEEE Trans. Knowl. Data Eng.

Author:

Cheng Dawei1ORCID,Ye Yujia1,Xiang Sheng2ORCID,Ma Zhenwei3,Zhang Ying2ORCID,Jiang Changjun1ORCID

Affiliation:

1. Department of Computer Science and Technology, Tongji University, Shanghai, China

2. School of Computer and Information Engineering, Zhejiang Gongshang University, Hangzhou, China

3. Research and Development Center of Financial Safety, China UnionPay Company, Ltd., Shanghai, China

Funder

National Key R&D Program of China

National Natural Science Foundation of China

Shanghai Science and Technology Innovation Action Plan

Publisher

Institute of Electrical and Electronics Engineers (IEEE)

Subject

Computational Theory and Mathematics,Computer Science Applications,Information Systems

Reference92 articles.

1. Money Laundering

2. Money Laundering and its Regulation

3. Estimating illicit financial flows resulting from drug trafficking and other transnational organized crimes: Research report;Walker,2011

4. Detecting money laundering transactions with machine learning

5. Money laundering policy;Council;Policy,1910

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1. P2P credit risk management with KG-GNN: a knowledge graph and graph neural network-based approach;Journal of the Operational Research Society;2024-09-14

2. Weakly Supervised Anomaly Detection via Knowledge-Data Alignment;Proceedings of the ACM Web Conference 2024;2024-05-13

3. Explainable deep learning model for predicting money laundering transactions;International Journal on Smart Sensing and Intelligent Systems;2024-04-01

4. Credit Card Fraud Detection via Intelligent Sampling and Self-supervised Learning;ACM Transactions on Intelligent Systems and Technology;2024-03-28

5. Contrastive Learning for Money Laundering Detection: Node-Subgraph-Node Method with Context Aggregation and Enhancement Strategy;Lecture Notes in Computer Science;2024
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