基于银行机构客户账户的可疑洗钱交易行为识别研究
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摘要
洗钱既可以是独立的犯罪行为，也可以是其他犯罪(如：贩毒)的下游犯罪，甚至还可以是其他犯罪行为(如：恐怖活动)的上游犯罪。洗钱不仅影响国家的经济发展，同时还会助长社会犯罪风气、动摇国家机器、损害国家形象。随着经济全球化的发展和国际金融市场的完善，反洗钱逐渐成为国际金融领域的重要课题。而数据报告制度是反洗钱的基础。不管是美国的FinCen还是中国人民银行的反洗钱监测分析中心，分析和监测的基础数据都来自于数据报告制度提交的大额和可疑交易报告。我国现行的数据报告制度存在标准模糊、易规避、自适应能力低下、数据海量等问题。本文的主要目的就是要在现行数据报告制度的基础上，提出一种基于银行账户交易数据的不易规避、标准明确且动态变化、人工智能化的可疑交易行为识别方法，这对完善我国反洗钱数据报告制度具有相当重要的意义，同时也符合国际反洗钱研究的发展方向。
本文的研究力图吸收和发展国内外有关可疑洗钱交易行为识别的最新研究成果，在分析我国现行数据报告制度应用现状和研究成果的基础上，以银行机构客户账户为基础，利用数据挖掘技术进行可疑交易行为识别研究。
论文首先分析了我国现行反洗钱工作的基本程序，并指出数据报告制度是反洗钱工作的基础和灵魂，从而确定本文的研究意义和研究价值；接着对我国现行的数据报告制度进行了较为详细的分析，主要是“弊”方面的分析，提出本研究的必要性；文章的重点在于在详细分析比较多种欺诈研究算法的基础上，提出最适合可疑交易行为识别的聚类算法和孤立点探测算法，并利用基于统计的孤立点探测技术、LOF算法和改进的网格聚类算法对银行机构客户账户进行偶然可疑行为和惯常可疑行为的识别研究。论文最后通过C++编程对采自银行的真实交易数据进行实验分析。
本研究最主要的贡献在于：基于改进的网格聚类算法和交易金额离散系数、交易频率、现金／入账金额比率、现金／出账金额比率等四个变量，对银行机构客户账户洗钱的可疑性程度进行排序，为反洗钱监测分析中心选择重点监测对象提供理论依据。
The crime of Money Laundering (ML) can be an independent criminal behavior, and can also be predicate crime and lower crime of other criminal activities. Therefore, ML will not only have an impact on the country's economic development, but also will encourage criminals, threaten national stability and do harm to nations' images. With the development of economic globalization and international financial markets, Anti-money Laundering (AML) becomes an important topic of international financial field. Meanwhile, Suspicious Activity Report (SAR) system is considered as the foundation of Anti-money Laundering. Both in FinCen and China Anti-Money Laundering Monitoring & Analyzing Center, the Large and Suspicious Transactions used for analyzing are supplied by SAR system. The current SAR system adopted by China has such limitations as fuzzy criterions, less of adaptive abilities, easy to avoid and massive data, etc. The main purpose of this paper is to put forward an automatic and dynamic suspicious transactional activities recognition method which is difficult to avoid and easy to measure. All of these are based on current SAR system and bank accounts. This is rather significant for the development of SAR system and accord with the future research direction of AML.
The up-to-minute research results of suspicious transaction activities recognition at home and abroad are greatly accepted and developed in this thesis. Base on the analysis of current SAR system in China, it does research on suspicious activities recognition via data mining. In this process, Outlier Detection Technology, Grid-based clustering algorithm and LOF algorithm have been applied.
Firstly, the current AML procedures in China are analyzed. At the same
本文的研究力图吸收和发展国内外有关可疑洗钱交易行为识别的最新研究成果，在分析我国现行数据报告制度应用现状和研究成果的基础上，以银行机构客户账户为基础，利用数据挖掘技术进行可疑交易行为识别研究。
论文首先分析了我国现行反洗钱工作的基本程序，并指出数据报告制度是反洗钱工作的基础和灵魂，从而确定本文的研究意义和研究价值；接着对我国现行的数据报告制度进行了较为详细的分析，主要是“弊”方面的分析，提出本研究的必要性；文章的重点在于在详细分析比较多种欺诈研究算法的基础上，提出最适合可疑交易行为识别的聚类算法和孤立点探测算法，并利用基于统计的孤立点探测技术、LOF算法和改进的网格聚类算法对银行机构客户账户进行偶然可疑行为和惯常可疑行为的识别研究。论文最后通过C++编程对采自银行的真实交易数据进行实验分析。
本研究最主要的贡献在于：基于改进的网格聚类算法和交易金额离散系数、交易频率、现金／入账金额比率、现金／出账金额比率等四个变量，对银行机构客户账户洗钱的可疑性程度进行排序，为反洗钱监测分析中心选择重点监测对象提供理论依据。
The crime of Money Laundering (ML) can be an independent criminal behavior, and can also be predicate crime and lower crime of other criminal activities. Therefore, ML will not only have an impact on the country's economic development, but also will encourage criminals, threaten national stability and do harm to nations' images. With the development of economic globalization and international financial markets, Anti-money Laundering (AML) becomes an important topic of international financial field. Meanwhile, Suspicious Activity Report (SAR) system is considered as the foundation of Anti-money Laundering. Both in FinCen and China Anti-Money Laundering Monitoring & Analyzing Center, the Large and Suspicious Transactions used for analyzing are supplied by SAR system. The current SAR system adopted by China has such limitations as fuzzy criterions, less of adaptive abilities, easy to avoid and massive data, etc. The main purpose of this paper is to put forward an automatic and dynamic suspicious transactional activities recognition method which is difficult to avoid and easy to measure. All of these are based on current SAR system and bank accounts. This is rather significant for the development of SAR system and accord with the future research direction of AML.
The up-to-minute research results of suspicious transaction activities recognition at home and abroad are greatly accepted and developed in this thesis. Base on the analysis of current SAR system in China, it does research on suspicious activities recognition via data mining. In this process, Outlier Detection Technology, Grid-based clustering algorithm and LOF algorithm have been applied.
Firstly, the current AML procedures in China are analyzed. At the same
引文
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