Abstract
Anti-money laundering(AML) compliance is having deeply effects on the culture, organization, and technology of Fiancial institutions(FIs). One of the most critical research issues in AML is how to efficiently and adaptively allocate limited AML resource to analyze suspicious transactions to achieve 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 FIs. Based on our proposed AAMLRAM, AML resource allocation domain can achieve 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 higher AML resource allocation domain system reward compared to traditional approach based on the Greedy resource allocation algorithms. Performance comparisons with various AML resource allocation schemes are also provided.
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Acknowledgement
This work was supported in part by the National High Technology Research and Development Program of China(863 Program, Grant No: 2015AA01A705), the National Social Science Foundation of China (Grant NO. 12XJY028) and China Postdoctoral Science Foundation (Grant No. 2013M541014). Hongbin Liang is the corresponding author.
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Hong, X., Liang, H., Gao, Z. (2015). Adaptive Resource Allocation for Anti-money Laundering Based on SMDP. In: Xu, K., Zhu, H. (eds) Wireless Algorithms, Systems, and Applications. WASA 2015. Lecture Notes in Computer Science(), vol 9204. Springer, Cham. https://doi.org/10.1007/978-3-319-21837-3_19
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DOI: https://doi.org/10.1007/978-3-319-21837-3_19
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