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Cash stock strategies during regular and COVID-19 periods for bank branches by deep learning

Author

Listed:
  • Chattriya Jariyavajee
  • Taninnuch Lamjiak
  • San Ratanasanya
  • Suthida Fairee
  • Kreecha Puphaiboon
  • Charoenchai Khompatraporn
  • Jumpol Polvichai
  • Booncharoen Sirinaovakul

Abstract

Determining the optimal amount of cash stock reserved in each bank branch is a strategic decision. A certain level of cash stock must be kept and ready for cash withdrawal needs at a branch. However, holding too much cash not only forfeits opportunities to make profit from the exceeding amount of cash in the stock but also increases insurance cost. This paper presents cash stock strategies for bank branches by using deep learning. Deep learning models were applied to historical data collected by a retail bank to predict the cash withdrawals and deposits. Data preparation and feature selection to identify important attributes from the bank branch data were performed. In the prediction process, two Recurrent Neural Network techniques—Long Short-Term Memory and Gated Recurrent Units methods—were compared. Then prediction errors were measured and statistically tested for their probability distributions. These distributions together with the predicted values were used in determining the lower and upper bounds for holding the cash stock. These bounds were employed to recommend the cash stock level strategies by having two options for different situations. The impacts of COVID-19 were also tested and discussed. According to the bank under this study, the proposed strategies can reduce the amount of cash stock by more than 10% for which was their initial target. Hence, the costs of cash management such as insurance cost and cash transportation cost were reduced. Moreover, the excess cash could be used for other purposes of the bank.

Suggested Citation

  • Chattriya Jariyavajee & Taninnuch Lamjiak & San Ratanasanya & Suthida Fairee & Kreecha Puphaiboon & Charoenchai Khompatraporn & Jumpol Polvichai & Booncharoen Sirinaovakul, 2022. "Cash stock strategies during regular and COVID-19 periods for bank branches by deep learning," PLOS ONE, Public Library of Science, vol. 17(6), pages 1-23, June.
    • Handle: RePEc:plo:pone00:0268753
    DOI: 10.1371/journal.pone.0268753
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    References listed on IDEAS

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    3. Teddy, S.D. & Ng, S.K., 2011. "Forecasting ATM cash demands using a local learning model of cerebellar associative memory network," International Journal of Forecasting, Elsevier, vol. 27(3), pages 760-776.
    4. A S Camanho & R G Dyson, 2005. "Cost efficiency, production and value-added models in the analysis of bank branch performance," Journal of the Operational Research Society, Palgrave Macmillan;The OR Society, vol. 56(5), pages 483-494, May.
    5. García Cabello, Julia & Lobillo, F.J., 2017. "Sound branch cash management for less: A low-cost forecasting algorithm under uncertain demand," Omega, Elsevier, vol. 70(C), pages 118-134.
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