Process of the 6th International Conference on Machine Learning and Cybernetics, Hong Kong, 19-22 Aug 2007
A MONEY LAUNDERING RISK ANALYSIS METHOD BASED UPON DECISION WOODS SU-NAN WANG1, 2, JIAN-GANG YANG1
College of Computer Technology and Architectural, Zhejiang University or college, Hangzhou 310027, China 2 Shanghai Pudong Development Bank, Shanghai 200002, China EMAIL: [email protected] com. cn, [email protected] zju. edu. cn
Money washing (ML) requires moving dubious funds, that could be linked to medicine trafficking or organized criminal offenses, through a number of transactions or perhaps accounts to disguise origins or control. China is facing severe problem on cash laundering with an estimated two hundred billion RMB laundered each year. Decision woods method is used in this conventional paper to create the determination rules of the cash laundering risk by consumer profiles of a commercial bank in China and tiawan. A sample of twenty-eight clients with four attributes can be used to caused and validate a decision shrub method. The result indicates the potency of decision tree in creating AML guidelines from companies' customer information. The anti-money laundering system in small , middle commerical bank in China is extremely needed.
Anti-money washing; Decision woods; Commercial lender
Criminal actions, drug trafficking, smuggling, bribing and so on, could be highly lucrative. Money generated by unlawful activities should be made to seem legitimate prior to it can be widely spent. Normally, it may be given up by the govt. Money laundering is a method that usually takes illegally acquired or filthy money and puts it by using a cycle of transactions or through different accounts in a single bank or perhaps between banks. The bicycling of the cash makes the funds appear to be by legitimate resources and the cash cannot be traced back to the illegitimate resource. Hiding legitimately acquired cash to avoid taxation also meets your criteria as cash laundering. In 2005, China Anti-Money Washing Monitoring & Analysis Middle received 283, 400 stocks and shares of the RMB suspicious transaction reports, and 1, 988, 900 stocks and shares of the foreign currency suspicious purchase reports linked to 137. 8 billions of RMB and more than one billion of US buck in 4926 accounts . The major part suspect transactions came from the state-owned commercial banking institutions 1-4244-0973-X/07/$25. 00 В©2007 IEEE
and the joint-stock commercial banking companies. Therefore industrial banks are facing extreme challenge upon money laundering in China today. The developed countries already founded some advanced online monitor systems intended for anti-money laundering (AML). For example , American Financial crime observance network Unnatural Intelligence System (FAIS)  integrated clever human and software agents to identify potential money washing on a substantial data space. Artificial intelligence computer examination system may greatly boost the work productivity and is a vital method for AML. However , the computer based AML technologies have never been used in Chinese business banks. An AML laptop automatic keep an eye on system is urgently needed. It is not appropriate that the AML man-made intelligence program from created countries immediately applied on Chinese immature economic market. Consequently , an man-made intelligence AML system should be established in line with the characteristic of Chinese economic market. The researches in the computer technology to get AML just started in China in recent years. To the best know-how, we are the first one to apply artificial intelligence technique into the AML domain in China. Decision tree learning  is one of the most widely used methods for inductive inference since the 60s. Since then, numerous researches have already been conducted to further improve the reliability, performance and so forth. ID3  is considered because the milestone in decision trees. A conclusion tree can be viewed as a dividing of the occasion space. Every single partition, called a leaf, symbolizes a number of comparable...
References: . . The People's Traditional bank of China and tiawan, China anti-money laundering report 2005[R], China Financial Publishing Residence, Beijing, 06\. 6. (In chinese) Ted E. Senator, Henry G. Goldberg, Jerry Wooton, and so forth, The financial crimes observance network AJE system (FAIS) identifying potential money washing from studies of large money transactions[J], AI Journal, Vol. 18, No . four, pp. 21-39, Winter 95. Safavin, T. R., Landgrebe, D. A survey of decision shrub classifier technique [J]. IEEE Ventures on Systems, Man and Cybernetics, Volume. 21, Number 3, pp. 660-667, The spring 1991.
Decision tree learning is a comparatively powerful means for inductive inference. An attempt is created in this daily news to examine the validity of using decision tree learning method to find judgment rules for consumer money washing risk dedication. The effects indicated that money laundering risk of a bank customer can be determined by using a method since described on this page. Out of 160 thousand current customer profiles, based on the rules generated in this conventional paper, 12% clients are considered since high AML risk and needed to be further monitored in their future deals. In future, the principles extracted by
. Quinlan, J. R. Inauguration ? introduction of decision trees, Equipment Learning 1(1): 81вЂ“106, 1986.
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