A Money Laundering Risk Evaluation Method Based on Decision Tree

Topics: Bank, Decision tree learning, Data mining Pages: 10 (2793 words) Published: July 24, 2011
Proceedings of the Sixth International Conference on Machine Learning and Cybernetics, Hong Kong, 19-22 August 2007


College of Computer Science and Engineering, Zhejiang University, Hangzhou 310027, China 2 Shanghai Pudong Development Bank, Shanghai 200002, China E-MAIL: wangsn@spdb.com.cn, yangjg@cs.zju.edu.cn

Money laundering (ML) involves moving illicit funds, which may be linked to drug trafficking or organized crime, through a series of transactions or accounts to disguise origin or ownership. China is facing severe challenge on money laundering with an estimated 200 billion RMB laundered annually. Decision tree method is used in this paper to create the determination rules of the money laundering risk by customer profiles of a commercial bank in China. A sample of twenty-eight customers with four attributes is used to induced and validate a decision tree method. The result indicates the effectiveness of decision tree in generating AML rules from companies’ customer profiles. The anti-money laundering system in small and middle commerical bank in China is highly needed.

Key Words:
Anti-money laundering; Decision tree; Commercial bank



Criminal activities, drug trafficking, smuggling, bribing and so on, can be highly profitable. Money generated by illegal activities must be made to look legitimate before it can be freely spent. Otherwise, it may be forfeited by the government. Money laundering is a process that takes illegally obtained or dirty money and puts it through a cycle of transactions or through various accounts in one bank or between banks. The cycling of the money makes the money appear to be from legitimate sources and the money cannot be traced back to its illegitimate source. Hiding legitimately acquired money to avoid taxation also qualifies as money laundering. In 2005, China Anti-Money Laundering Monitoring & Analysis Center received 283,400 shares of the RMB suspicious transaction reports, and 1,988,900 shares of the foreign currency suspicious transaction reports related to 137.8 billions of RMB and more than one billion of US dollar in 4926 accounts [1]. The major part suspicious transactions came from the state-owned commercial banks 1-4244-0973-X/07/$25.00 ©2007 IEEE

and the joint-stock commercial banks. Therefore commercial banks are facing severe challenge on money laundering in China today. The developed countries already established some advanced online monitor systems for anti-money laundering (AML). For example, American Financial crime enforcement network Artificial Intelligence System (FAIS) [2] integrated intelligent human and software agents to identify potential money laundering on a very large data space. Artificial intelligence computer analysis system can greatly enhance the work efficiency and is an essential method for AML. However, the computer based AML technologies have not been used in Chinese commercial banks. An AML computer automatic monitor system is urgently needed. It is not appropriate that an AML artificial intelligence system from developed countries directly applied on Chinese immature financial market. Therefore, an artificial intelligence AML system must be established according to the characteristic of Chinese financial market. The researches of the computer technology for AML just started in China in recent years. To our best knowledge, we are the first one to apply artificial intelligence method into the AML domain in China. Decision tree learning [3] is one of the most widely used methods for inductive inference since the 1960s. Since then, numerous researches have been conducted to improve the accuracy, performance and so on. ID3 [4] is considered as the milestone in decision trees. A decision tree can be viewed as a partitioning of the instance space. Each partition, called a leaf, represents a number of similar...

References: [1]. [2]. The People’s Bank of China, China anti-money laundering report 2005[R], China Financial Publishing House, Beijing, 2006.6. (In chinese) Ted E. Senator, Henry G. Goldberg, Jerry Wooton, etc., The financial crimes enforcement network AI system (FAIS) identifying potential money laundering from reports of large cash transactions[J], AI Magazine, Vol.16, No.4, pp. 21-39, Winter 1995. Safavin,S.R., Landgrebe,D. A survey of decision tree classifier methodology [J]. IEEE Transactions on Systems, Man and Cybernetics, Vol.21, No.3, pp.660-667, April 1991.
Decision tree learning is a comparatively powerful method for inductive inference. An attempt is made in this paper to examine the validity of using decision tree learning method to find judgment rules for customer money laundering risk determination. The results indicated that money laundering risk of a bank customer can be determined through a method as described in this article. Out of 160 thousand current customer profiles, based on the rules generated in this paper, 12% customers are considered as high AML risk and needed to be further monitored in their future transactions. In future, the rules extracted from
[4]. Quinlan, J. R. Induction of decision trees, Machine Learning 1(1): 81–106, 1986.
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