客户洗钱风险评级多层次量化模型设计与实践
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摘要
客户洗钱风险评级是金融监管部门对所有金融机构履行反洗钱义务的具体要求,体现风险为本的反洗钱工作原则,避免缺少重点低效的反洗钱工作。论文在沃尔夫斯堡集团反洗钱指引以及中国人民银行客户风险评估指引的基础上,提出了客户洗钱风险评级多层次量化模型,利用客户基本信息、地域信息、业务信息、行业及关联信息四个维度多层次风险子项,通过多种方式量化映射了客户的洗钱风险程度,并提出了根据金融机构客户数据分布进行模型调校的方法,每个层级之间可以加权计算评分,增加了模型调整的稳定性。相比国内外其他客户洗钱风险评级模型在准确性、可调控性上有显著提升。此模型已经在国内50多家金融机构的反洗钱系统中得到使用。
The customer money laundering risk rating is the financial supervision department's guidelines and requirementsfor all financial institutions to implement their anti-money laundering obligations. It embodies risk-based anti-money launderingwork principles and avoids the lack of key and inefficient anti-money laundering work. It is a long-term and arduous task for the financial institutions to quantify and correct the risk rating of customer money laundering. Based on the Wolfsburg Group's anti-money laundering guidelines and PBOC's customer risk assessment guidelines,this paper proposes a multi-level quantitative model forcustomer laundering risk assessment,which includes customer characteristics,geography,business,industry related information.The multi-level risk sub-items of the dimensions have quantitatively mapped the level of customer money laundering risk throughmultiple ways. This paper proposes a method of model calibration based on the distribution of customer data of financial institutions.The scores can be weighted between each level to increase the stability of the model adjustment. Compared with other models,thecustomer money laundering risk rating model has a significant improvement in accuracy and controllability. This model has beenused in the anti-money laundering system of more than 50 China financial institutions.
The customer money laundering risk rating is the financial supervision department's guidelines and requirementsfor all financial institutions to implement their anti-money laundering obligations. It embodies risk-based anti-money launderingwork principles and avoids the lack of key and inefficient anti-money laundering work. It is a long-term and arduous task for the financial institutions to quantify and correct the risk rating of customer money laundering. Based on the Wolfsburg Group's anti-money laundering guidelines and PBOC's customer risk assessment guidelines,this paper proposes a multi-level quantitative model forcustomer laundering risk assessment,which includes customer characteristics,geography,business,industry related information.The multi-level risk sub-items of the dimensions have quantitatively mapped the level of customer money laundering risk throughmultiple ways. This paper proposes a method of model calibration based on the distribution of customer data of financial institutions.The scores can be weighted between each level to increase the stability of the model adjustment. Compared with other models,thecustomer money laundering risk rating model has a significant improvement in accuracy and controllability. This model has beenused in the anti-money laundering system of more than 50 China financial institutions.
引文
[1]FTAF. The FATF Recommendations,International Standards On Combating Money Laundering And The Financing Of Terrorism&Proliferation[EB/OL].[2018-06-14]. http://www.fatf-gafi.org/publications/fatfrecommendations/documents/fatf-recommendations.html.
[2]中国人民银行.银发〔2013〕2号,中国人民银行关于印发《金融机构洗钱和恐怖融资风险评估及客户分类管理指引》的通知[Z]. 2013-1-5.PBOC. Yinfa[2013]No. 2,Notice of the People's Bankof China on issuing the guidelines on risk assessment andcustomer classification management of money launderingand terrorist financing of financial institutions[Z].2013-1-5.(in Chinese)
[3]FFIEC. Bank Secrecy Act/Anti-Money Laundering Examination Manual[EB/OL].[2018-06-14]. https://www.ffiec.gov/bsa_aml_infobase/documents/BSA_AML_Man_2014_v2.pdf.
[4] Wolfsberg Group. Wolfsberg Statement Guidance on aRisk Based Approach for Managing Money LaunderingRisks[EB/OL].[2018-06-14]. https://www.wolfsberg-principles.com/sites/default/files/wb/pdfs/wolfsberg-standards/15.%20Wolfsberg_RBA_Guidance_%282006%29.pdf.
[5]Rick Small. Risk Based Approach to Customer Due Diligence[EB/OL].[2018-06-14]. https://www.bankersonline.com/sites/default/files/tools/kb_riskratingsystem.pdf.
[6] Karima Touil. Risk-BasedApproach Understanding andImplementation[EB/OL].[2018-06-14]. http://files.acams.org/pdfs/2016/Risk-Based_Approach_Understanding_and_Implementation_K_Touil.pdf.
[7]中国人民人寿保险股份有限公司.中国人民人寿保险股份有限公司客户洗钱风险等级划分标准[EB/OL].[2018-06-14]. https://wenku.baidu.com/view/cfb71433852458fb770b56e4.html.EPICC. Customer money laundering risk classificationstandard of EPICC[EB/OL].[2018-06-14]. https://wenku.baidu.com/view/cfb71433852458fb770b56e4.html.
[8]张燕华,薛耀文.金融机构客户洗钱风险评估——基于中国洗钱案例实证研究[J].金融理论与实践,2015(3):24-29.ZHANG Yanhua,XUE Yaowen. Customer Money Laundering Risk Assessment of FI—Research Based on ChineseMoney Laundering Cases[J]. Financial Theory&Practice,2015(3):24-29.
[9]张成虎,李霖魁.基于信息融合的多层次多因素客户洗钱风险综合评估研究[J].湖南社会科学,2015(1):116-121.ZHANG Chenghu,LI Linkui. Research on ComprehensiveEvaluation of Multi-level and Multi-factor Customer Money Laundering Risk Based on Information Fusion[J]. Social Sciences in Hunan,2015(1):116-121.
[10]孙娟.基于BP神经网络的客户洗钱风险等级划分模型[J].金融电子化,2013(10):72-73.SUN Juan. Customer Money Laundering Risk Classification Model Based on BP Neural Network[J]. FinancialComputerizing,2013(10):72-73.
[11]尚微.关于在国别风险管理中嵌入反洗钱制裁风险因子的实务探析[J].华北金融,2018(5):39-41.SHANG Wei. Practical analysis of embedding risk factors in Anti-money laundering sanctions in country riskmanagement[J]. Huabei Finance,2018(5):39-41.
[12]赵东明等.寿险业客户洗钱风险等级分类管理工作研究——基于分类模型和系统架构视角[J].福建金融,2014(1):25-29.ZHAO Dongming,et al. Research on Classification Management of Customer Money Laundering Risk Level inLife Insurance Industry:Based on Classification Modeland System Architecture Perspective[J]. Fujian Finance,2014(1):25-29.
[13]杨扬,文良旭.基层银行业金融机构客户风险等级划分问题研究[J].西部金融,2013(12):78-80.YANG Yang,WEN Liangxu. Research on customer riskclassification of grass roots banking financial institutions[J]. West China Finance,2013(12):78-80.
[14]黎文利,罗桦.科学评定反洗钱客户风险等级[J].农业发展与金融,2013(10):69-70.LI Wenli,LUO Hua. Scientifically assess the risk level ofanti money laundering customers[J]. The AgriculturalDevelopment and Finance,2013(10):69-70.
[15]贾昌峰.基于风险为本的银行业洗钱风险评估体系研究[J].北方金融,2016(5):61-64.JIA Changfeng. Research on Risk-based Banking MoneyLaundering Risk Assessment System[J]. Northern Finance Journal,2016(5):61-64.
[2]中国人民银行.银发〔2013〕2号,中国人民银行关于印发《金融机构洗钱和恐怖融资风险评估及客户分类管理指引》的通知[Z]. 2013-1-5.PBOC. Yinfa[2013]No. 2,Notice of the People's Bankof China on issuing the guidelines on risk assessment andcustomer classification management of money launderingand terrorist financing of financial institutions[Z].2013-1-5.(in Chinese)
[3]FFIEC. Bank Secrecy Act/Anti-Money Laundering Examination Manual[EB/OL].[2018-06-14]. https://www.ffiec.gov/bsa_aml_infobase/documents/BSA_AML_Man_2014_v2.pdf.
[4] Wolfsberg Group. Wolfsberg Statement Guidance on aRisk Based Approach for Managing Money LaunderingRisks[EB/OL].[2018-06-14]. https://www.wolfsberg-principles.com/sites/default/files/wb/pdfs/wolfsberg-standards/15.%20Wolfsberg_RBA_Guidance_%282006%29.pdf.
[5]Rick Small. Risk Based Approach to Customer Due Diligence[EB/OL].[2018-06-14]. https://www.bankersonline.com/sites/default/files/tools/kb_riskratingsystem.pdf.
[6] Karima Touil. Risk-BasedApproach Understanding andImplementation[EB/OL].[2018-06-14]. http://files.acams.org/pdfs/2016/Risk-Based_Approach_Understanding_and_Implementation_K_Touil.pdf.
[7]中国人民人寿保险股份有限公司.中国人民人寿保险股份有限公司客户洗钱风险等级划分标准[EB/OL].[2018-06-14]. https://wenku.baidu.com/view/cfb71433852458fb770b56e4.html.EPICC. Customer money laundering risk classificationstandard of EPICC[EB/OL].[2018-06-14]. https://wenku.baidu.com/view/cfb71433852458fb770b56e4.html.
[8]张燕华,薛耀文.金融机构客户洗钱风险评估——基于中国洗钱案例实证研究[J].金融理论与实践,2015(3):24-29.ZHANG Yanhua,XUE Yaowen. Customer Money Laundering Risk Assessment of FI—Research Based on ChineseMoney Laundering Cases[J]. Financial Theory&Practice,2015(3):24-29.
[9]张成虎,李霖魁.基于信息融合的多层次多因素客户洗钱风险综合评估研究[J].湖南社会科学,2015(1):116-121.ZHANG Chenghu,LI Linkui. Research on ComprehensiveEvaluation of Multi-level and Multi-factor Customer Money Laundering Risk Based on Information Fusion[J]. Social Sciences in Hunan,2015(1):116-121.
[10]孙娟.基于BP神经网络的客户洗钱风险等级划分模型[J].金融电子化,2013(10):72-73.SUN Juan. Customer Money Laundering Risk Classification Model Based on BP Neural Network[J]. FinancialComputerizing,2013(10):72-73.
[11]尚微.关于在国别风险管理中嵌入反洗钱制裁风险因子的实务探析[J].华北金融,2018(5):39-41.SHANG Wei. Practical analysis of embedding risk factors in Anti-money laundering sanctions in country riskmanagement[J]. Huabei Finance,2018(5):39-41.
[12]赵东明等.寿险业客户洗钱风险等级分类管理工作研究——基于分类模型和系统架构视角[J].福建金融,2014(1):25-29.ZHAO Dongming,et al. Research on Classification Management of Customer Money Laundering Risk Level inLife Insurance Industry:Based on Classification Modeland System Architecture Perspective[J]. Fujian Finance,2014(1):25-29.
[13]杨扬,文良旭.基层银行业金融机构客户风险等级划分问题研究[J].西部金融,2013(12):78-80.YANG Yang,WEN Liangxu. Research on customer riskclassification of grass roots banking financial institutions[J]. West China Finance,2013(12):78-80.
[14]黎文利,罗桦.科学评定反洗钱客户风险等级[J].农业发展与金融,2013(10):69-70.LI Wenli,LUO Hua. Scientifically assess the risk level ofanti money laundering customers[J]. The AgriculturalDevelopment and Finance,2013(10):69-70.
[15]贾昌峰.基于风险为本的银行业洗钱风险评估体系研究[J].北方金融,2016(5):61-64.JIA Changfeng. Research on Risk-based Banking MoneyLaundering Risk Assessment System[J]. Northern Finance Journal,2016(5):61-64.
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