一种基于客户行为时序分析的反洗钱异常交易识别方法

可疑交易报告制度是打击洗钱活动的一项基本机制,如何有效甄别可疑交易是金融机构和金融情报中心面临的一个技术难点。为辅助反洗钱分析人员从海量金融交易信息中甄别客户异常交易,本文提出一种预测误差和统计处理综合法--CPEST,通过分析客户前后行为的一致性来发现异常。CPEST建立客户行为模型,根据预测误差对客户行为进行时点异常检验,并在此基础上构造一个窗口检验,以提高对涉嫌洗钱行为的识别能力。本文在支持向量回归和核密度估计等具体实现手段的基础上,运用CPEST对实际交易和仿真数据进行分析,结果表明该方法的有效性和可行性,具有应用推广价值。

An Approach for Unusual Transaction Detection Based on Customer Behavior Time Series Analysis

The suspicious transaction reporting system is the principle mechanism to fight against money laundering, and it is a technical problem to detect suspicious transaction for financial institutions and the financial intelligence unit. To help anti-money laundering analysts screen customers' unusual transactions and behaviors in massive financial transaction information, a new method, composition of predictive error and statistic treatment(CPEST) is presented, which can be used to detect unusual behaviors from the inconsistency of customer behaviors. CPEST models a customer's behavior, tests a customer's behavior at a particular time using estimated errors, and uses a window test to improve the ability to identify suspected of money laundering. Applying the method based on support vector regression and kernel density estimation to real data examples and simulations, the experiment results suggest that the method,which is feasible and effective, has high value in popularization and application.