An adaptive resource allocation model in anti-money laundering system

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• 作者：Xintao Hong ; Hongbin Liang ; Zengan Gao…
• 关键词：Anti ; money laundering (AML) ; Semi ; markov decision process (SMDP) ; Adaptive AML resource allocation model (AAMLRAM)
• 刊名：Peer-to-Peer Networking and Applications
• 出版年：2017
• 出版时间：March 2017
• 年：2017
• 卷：10
• 期：2
• 页码：315-331
• 全文大小：
• 刊物类别：Engineering
• 刊物主题：Communications Engineering, Networks; Information Systems and Communication Service; Computer Communication Networks; Signal,Image and Speech Processing;
• 出版者：Springer US
• ISSN：1936-6450
• 卷排序：10

文摘

The importance of the global combating of money laundering and financing of terrorism has increased significantly over the past decade. One of the most fundamental research issues in Anti-Money Laundering (AML) area is how to efficiently and adaptively allocate the limited AML resource to analyze suspicious transactions to achieve the maximal AML rewards. In this paper, a novel Adaptive AML Resource Allocation Model (AAMLRAM) based on Semi-Markov Decision Process (SMDP) is proposed to allocate AML resources optimally in AML resource allocation domain to analyze the suspicious transaction report sent from Financial Institutions (FIs). Based on our proposed AAMLRAM, AML resource allocation domain can achieve the maximal AML rewards, taking into account not only the incomes of identifying the suspicious transaction but also the cost resulted from AML resource occupation as well. Extensive simulations are conducted to demonstrate that our proposed model can achieve the higher system reward compared to traditional approaches based on the greedy resource allocation algorithms.