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LSTM Based Reserve Prediction for Bank Outlets
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Abstract
Reserve allocation is a significant problem faced by commercial banking businesses every day. To satisfy the cash requirement of customers and abate the vault cash pressure, commercial banks need to appropriately allocate reserves for each bank outlet. Excessive reserve would impact the revenue of bank outlets. Low reserves cannot guarantee the successful operation of bank outlets. Considering the reserve requirement is effected by the past cash balance, we deal the reserve allocation problem as a time series prediction problem, and the Long Short Time Memory (LSTM) network is adapted to solve it. In addition, the proposed LSTM prediction model regards date property, which can affect the cash balance, as a primary factor. The experiment results show that our method outperforms some existing traditional methods.
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Yu Liu is a master student in computer science at Central South University. She received the bachelor degree from Central South University, China, in 2014. Her main research interests include big data progressing.
Jianxin Wang received the BS and MS degrees from Central South University in 1992 and 1996, respectively, and received the PhD degree from Central South University in 2001. He is a vice dean and a professor in School of Information Science and Engineering at Central South University, China. His current research interests include algorithm analysis and optimization, parameterized algorithm, bioinformatics, and computer network. He has published more than 150 papers in various international journals and refereed conferences. He is a senior member of IEEE.
Shuting Dong is a master student in computer science at Central South University. Her main research interests include deep learning and big data analysis. She received the bachelor degree from Jiangxi University of Finance and Economics, China, in 2016.
Mingming Lu received the PhD degree in computer science from Florida Atlantic University, US, in 2008. He is currently an associate professor at the School of Information Science and Engineering, Central South University, China. His main research interests include deep learning and big data analysis.