Modeling and empirical study on money laundering risk assessment based on administrative punishment cases

XIE Xiaojin; NING Yangxue; SHI Xingsen; LUO Kangyang; ZHANG Yi; WANG Guoqiang

doi:10.12299/jsues.21-0090

Volume 36 Issue 2

Jun. 2022

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Article Navigation > Journal of Shanghai University of Engineering Science > 2022 > 36(2): 205-211

XIE Xiaojin, NING Yangxue, SHI Xingsen, LUO Kangyang, ZHANG Yi, WANG Guoqiang. Modeling and empirical study on money laundering risk assessment based on administrative punishment cases[J]. Journal of Shanghai University of Engineering Science, 2022, 36(2): 205-211. doi: 10.12299/jsues.21-0090

Citation:

XIE Xiaojin, NING Yangxue, SHI Xingsen, LUO Kangyang, ZHANG Yi, WANG Guoqiang. Modeling and empirical study on money laundering risk assessment based on administrative punishment cases[J]. Journal of Shanghai University of Engineering Science, 2022, 36(2): 205-211. doi: 10.12299/jsues.21-0090

Citation:

XIE Xiaojin, NING Yangxue, SHI Xingsen, LUO Kangyang, ZHANG Yi, WANG Guoqiang. Modeling and empirical study on money laundering risk assessment based on administrative punishment cases[J]. Journal of Shanghai University of Engineering Science, 2022, 36(2): 205-211. doi: 10.12299/jsues.21-0090

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Modeling and empirical study on money laundering risk assessment based on administrative punishment cases

doi: 10.12299/jsues.21-0090

1.
School of Mathematics, Physics and Statistics, Shanghai University of Engineering Science, Shanghai 201620, China
2.
Shanghai Kingstar Fintech Company Limited, Shanghai 201203, China
3.
School of Data Science & Engineering, East China Normal University, Shanghai 200062, China

Received Date: 2021-05-10
Available Online: 2022-11-16
Publish Date: 2022-06-30

Abstract

Abstract

An assessment model based on the administrative punishment cases of the People’s Bank of China was built to measure the degree of money laundering and conduct an empirical analysis. Based on 1717 administrative punishment cases by the People’s Bank of China, five first-level risk level indexes were constructed. The AHP and entropy method were used to assign the weights of the risk level indexes, and an evaluation model was built based on above methods. The random forest model was used to test the validity of index weights and the accuracy of the model. The results showed that the F−score of the testing set was up to 94%. The research results can provide preferences for finding typical cases and prominent problems from the large number of anti-money laundering administrative punishment cases, and then promote the construction of China's anti-money laundering system.
- administrative punishment,
- anti-money laundering,
- risk assessment,
- AHP,
- entropy method,
- random forest

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References(14)

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