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中文摘要：
针对跨行洗钱犯罪的复杂性和协作性,在中央银行大额支付系统（HVPS）框架内,综合有限信息管理新方法,构建了新型的反洗钱管理模型.该模型采用分布式检测点采集排队队列中的洗钱信息,从而对大额支付系统中的协作洗钱犯罪进行整合的动态跟踪.它采用了基于事件的描述方法记录洗钱犯罪过程,应用灰色关联度算法实现大额支付系统中的多检测点信息融合,通过有限信息发掘出大额支付系统中的异常操作行为,最终应用功率谱估计算法实现洗钱犯罪的快速分析与识别.仿真测试结果证明,该模型与传统的反洗钱管理模型相比,洗钱客户覆盖率和发现精确度超过12％以上,而洗钱事件召回率提高了5％以上.从总体来看,该模型具有较高的信息处理效率和处理精度.
英文摘要：
To deal with the problem of inter-bank money laundering, combined with limited information management methods, a new anti-money laundering model was presented with central bank High-Value Payment System （HVPS） architecture. The proposed model utilized distributed monitor nodes to trace money laundering crimes. And it used event description method to record the crime procedures and so on. A new grey relational information fusion algorithm was invented to integrate multi-monitor information. And an improved power spectral algorithm was proposed to deal with fast data analysis and money laundering recognition operations. The simulation results show that the model has better processing performance and anti-money laundering recognition accuracy than others do. In detail, the model does well in money-laundering client coverage （by 12%）, discovery rates （by 12%） and recall rates （by 5%）.
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