A Money Laundering Risk Evaluation Method Based on Decision Tree

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.