Abstract
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.
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This work was supported in part by the National High-Tech Research and Development Program of China(863 Program, Grant No: 2015AA01A705), the National Natural Science Foundation of China (Grant No. 61571375), the Science and Technology Support Program of Sicuan Province (Grant No. 2015GZ0088) and the National Social Science Foundation of China (Grant No. 12XJY028).
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Hong, X., Liang, H., Gao, Z. et al. An adaptive resource allocation model in anti-money laundering system. Peer-to-Peer Netw. Appl. 10, 315–331 (2017). https://doi.org/10.1007/s12083-016-0430-y
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DOI: https://doi.org/10.1007/s12083-016-0430-y