| 基于行政处罚案例的洗钱风险评估建模与实证研究 |
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| 引用本文： | 谢晓金,宁阳雪,施兴森,罗康洋,张怡,王国强.基于行政处罚案例的洗钱风险评估建模与实证研究[J].上海工程技术大学学报,2022,36(2):205-211. |
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| 作者姓名： | 谢晓金 宁阳雪 施兴森 罗康洋 张怡 王国强 |
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| 作者单位： | 1.上海工程技术大学 数理与统计学院, 上海 2016202.上海金仕达软件科技有限公司, 上海 2012033.华东师范大学 数据科学与工程学院, 上海 200062 |
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| 基金项目： | 国家自然科学基金面上项目资助(11971302)；全国统计科学研究项目一般项目资助(2020LY098)；浦东新区科技发展基金产学研专项资金（人工智能）项目资助(PKX2020-R02) |
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| 摘 要： | 基于中国人民银行行政处罚案例构建度量洗钱风险危害程度的评估模型并进行实证分析.基于1 717例中国人民银行行政处罚案例，构建5个一级风险等级指标.利用层次分析法和熵权法对风险等级指标进行权重赋值，构建基于主客观方法相结合的行政处罚洗钱风险危害程度的评估模型.利用随机森林模型检验指标权重的有效性和模型的准确性.试验结果表明，测试集样本整体的F-score达到94%，研究成果可为行政管理部门从大量反洗钱行政处罚案例中发现典型案例和突出问题提供参考，推动和促进我国反洗钱制度建设.
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| 关 键 词： | 行政处罚 反洗钱 风险评估 层次分析法 熵权法 随机森林 |
| 收稿时间： | 2021-05-10 |
Modeling and empirical study on money laundering risk assessment based on administrative punishment cases |
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| 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. |
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| Authors: | XIE Xiaojin NING Yangxue SHI Xingsen LUO Kangyang ZHANG Yi WANG Guoqiang |
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| Affiliation: | 1.School of Mathematics, Physics and Statistics, Shanghai University of Engineering Science, Shanghai 201620, China2.Shanghai Kingstar Fintech Company Limited, Shanghai 201203, China3.School of Data Science & Engineering, East China Normal University, Shanghai 200062, China |
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| 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. |
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