Anti-money laundering method based on hierarchical risk control knowledge graph

Zichen Tang; Haihong E; Mingzhi Sun; Lamei Zhao; Rongrong Wang; Meina Song

doi:10.1117/12.3009288

16 October 2023 Anti-money laundering method based on hierarchical risk control knowledge graph

Zichen Tang, Haihong E, Mingzhi Sun, Lamei Zhao, Rongrong Wang, Meina Song

Proceedings Volume 12803, Fifth International Conference on Artificial Intelligence and Computer Science (AICS 2023); 128032Z (2023) https://doi.org/10.1117/12.3009288
Event: 2023 5th International Conference on Artificial Intelligence and Computer Science (AICS 2023), 2023, Wuhan, China

Abstract

With the development of the Internet and advances in technology, financial transactions have become more convenient, but money laundering has also become more concealed, complex, and can be quickly adjusted according to regulatory policies, making traditional methods based on rule libraries and blacklists difficult to cope with the current Anti-Money Laundering (AML) challenges. To improve the efficiency and accuracy of AML work, various machine learning methods have been proposed. However, these methods are often difficult to be widely used due to their complex models and poor interpretability. To overcome the shortcomings of existing methods and adapt to the changing trends in money laundering while ensuring good interpretability, this paper proposes an AML method based on Hierarchical Risk Control Knowledge Graph (HRCKG). Firstly, a simulated AML dataset is generated based on a small amount of real data and money laundering patterns. Next, various transaction features are extracted to construct graded risk control indicators, which are used to automatically search for and filter risk control rules to form a rule library, and then construct an HRCKG. Finally, integrating rule-based reasoning and graph-based reasoning, this paper uses rule matching, node importance measurement, and community discovery algorithms to evaluate the money laundering risks of accounts, identify money laundering accounts, and discover money laundering groups. Through comparison with other machine learning methods and analysis of experimental results, the effectiveness and application value of the proposed method is demonstrated.

(2023) Published by SPIE. Downloading of the abstract is permitted for personal use only.

Citation Download Citation

Zichen Tang, Haihong E, Mingzhi Sun, Lamei Zhao, Rongrong Wang, and Meina Song "Anti-money laundering method based on hierarchical risk control knowledge graph", Proc. SPIE 12803, Fifth International Conference on Artificial Intelligence and Computer Science (AICS 2023), 128032Z (16 October 2023); https://doi.org/10.1117/12.3009288

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KEYWORDS

Machine learning

Statistical modeling

Risk assessment

Education and training

Mining

Detection and tracking algorithms

Data modeling

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