International Journal of Nonlinear Analysis and Applications

Providing a forecasting model and optimization of the cash balance of bank branches and ATMs with the approach of social responsibilities

Document Type : Research Paper

Authors

1 Department of Industrial Engineering, Faculty of Engineering, Kharazmi University, Tehran, Iran

2 Department of Electrical and Computer Engineering, Faculty of Engineering, Kharazmi University, Tehran, Iran

Abstract

Providing cash reserves and providing cash services is one of the main missions of banks, and optimal management of cash to meet the needs of customers while complying with social responsibilities is one of the important challenges in the banking industry. In order to provide a solution, in the current research, we have presented a two-objective mathematical model with a combined approach of cost minimization and action maximization to the social responsibilities of the sample branches of a commercial bank in cash logistics. In order to get better results, first, withdrawal and receipt of customers' cash were predicted using the SARIMAX statistical method and LSTM recurrent neural network, and by comparing the results, the LSTM method was chosen as the superior method with an acceptable level of accuracy. Then, by placing the predicted values in the optimization model, the optimal values of the inventory variables and the amount and time of cash movement of the branches were determined in the cash logistics, which resulted in a significant reduction of the bank's costs in providing cash services and accountability. Complete with the demand for cash from customers, the social responsibilities of the bank were optimized in the cash transfer mission.

Keywords

[1] C. Bilir and A. Doseyen, Optimization of ATM and branch cash operations using an integrated cash requirement forecasting and cash optimization model, Bus. Manag. Stud. 6 (2018), no. 1, 237––255.
[2] Y. Ekinci, N. Serban, and E. Duman, Optimal ATM replenishment policies under demand uncertainty, Oper. Res. 21 (2021), 999–1029.
[3] A. Fallahtafti, M. Aghaaminiha, S. Akbarghanadia, and G.R. Weckman, Forecasting ATM cash demand before and during the COVID-19 pandemic using an extensive evaluation of statistical and machine learning models, Sn. Comput. Sci. 3 (2022), no. 164.
[4] J. Garcıa Cabello and F.J. Lobillo, Sound branch cash management for less: A low-cost forecasting algorithm under uncertain demand, Omega 70 (2017), 118–134.
[5] O. Gorodetskaya, Y. Gobareva, and M. Koroteev, A machine learning pipeline for forecasting time series in the banking sector, Economies 9 (2021), no. 4.
[6] S. Gray, Central bank balances and reserve requirements, Tech. report, International Monetary Fund, Washington, DC, USA, 2011.
[7] H. Gurgul and M. Suder, Modeling of withdrawals from selected ATMs of the “Euronet” network, Manag. Econ. 13 (2013), 65.
[8] R.I.D. Harris, Testing for unit roots using the augmented dickey-fuller test: Some issues relating to the size, power and the lag structure of the test, Econ. Lett. 38 (1992), no. 4, 381–386.
[9] D. Heller and Y. Lengwiler, Payment obligations, reserve requirements, and the demand for central bank balances, J. Monetary Econ. 50 (2003), no. 2, 419–432.
[10] S. Hochreiter and J. Schmidhuber, Long short-term memory, Neural Comput. 9 (1997), no. 8, 1735–1780.
[11] C. Jariyavajee, T. Lamjiak, S. Ratanasanya, S. Fairee, K. Puphaiboon, and C. Khompatraporn, Cash stock strategies during regular and COVID-19 periods for bank branches by deep learning, PLOS ONE 17 (2022), no. 06, 1–23.
[12] N. Kkiani, G. Tohidi, S. Razavyan, N. Shadnoosh, and M. Sanei, Improved NARX-ANFIS network structure with genetic algorithm to optimize cash flow of ATM model, Adv. Math. Finance Appl. 8 (2023), no. 1, 241–254.
[13] A. Kumar, Modelling the importance of social responsibility standards (ISO 26000:2010) practices adoption in freight transport industry, J. Cleaner Prod. 367 (2022), 132861.
[14] H. Larrain, L.C. Coelho, and A. Cataldo, A variable MIP neighborhood descent algorithm for managing inventory and distribution of cash in automated teller machines, Comput. Oper. Res. 85 (2017), 22–31.
[15] Y. Liu, S. Dong, M. Lu, and J. Wang, LSTM based reserve prediction for bank outlets, Tsinghua Sci. Technol. 24 (2019), no. 1, 77–85.
[16] J. Lopez Lazaro, A. Barbero Jimenez, and A. Takeda, Improving cash logistics in bank branches by coupling machine learning and robust optimization, Expert Syst. Appl. 92 (2018), 236–255.
[17] S. Meisenbacher, M. Turowski, K. Phipps, M. Ratz, D. Muller, V. Hagenmeyer, and R. Mikut, Review of automated time series forecasting pipelines, WIREs Data Min. Knowledge Discov. 12 (2022), no. 6, 1475.
[18] N. Nazari Ganje, A. Mirzapour, and S. Mohammad J, An integrated location-inventory routing problem for ATMs in banking industry: A green approach, pp. 27–52, Springer International Publishing, 2020.
[19] S. Pasandideh, S. Akhavan Niaki, and K. Asadi, Optimizing a bi-objective multi-product multi-period three echelon supply chain network with warehouse reliability, Expert Syst. Appl. 42 (2015), no. 5, 2615–2623.
[20] S. Song, Y. Tian, and D. Zhou, Reverse logistics network design and simulation for automatic teller machines based on carbon emission and economic benefits: A study of the Anhui Province ATMs Industry, Sustainability 13 (2021), no. 20.
[21] A. Suharsono, A. Masyitha, and A. Anuravega, Time series regression and ARIMAX for forecasting currency flow at Bank Indonesia in Sulawesi region, AIP Conf. Proc. 1691 (2015), no. 1, 050025.
[22] R.G. van Anholt, L.C. Coelho, G. Laporte, and I.F.A. Vis, An inventory-routing problem with pickups and deliveries arising in the replenishment of automated teller machines, Transport. Sci. 50 (2016), no. 3, 1077–1091.
[23] T. Van Nguyen, H.T.T. Bui, and C.H.D. Le, The impacts of corporate social responsibility to corporate financial performance: A case study of Vietnamese commercial banks, Cogent Econ. Finance 10 (2022), no. 1, 2132642.
[24] W.W.S. Wei, Time Series Analysis: Univariate and Multivariate Methods, Time Series Analysis: Univariate and Multivariate Methods, Pearson Addison Wesley, 2006.
[25] H. Weytjens, E. Lohmann, and M. Kleinsteuber, Cash flow prediction: MLP and LSTM compared to ARIMA and prophet, Electron. Commerce Res. 21 (2019), 371–391.
[26] Y. Zhu, S. Kumar, S. Rodriguez-Sanchez, and C. Sriskandarajah, Managing logistics in regional banknote supply chain under security concerns, Prod. Oper. Manag. 24 (2015), no. 12, 1966–1983.
International Journal of Nonlinear Analysis and Applications
Volume 15, Issue 10
October 2024
Pages 211-224
  • Receive Date: 22 July 2023
  • Accept Date: 01 December 2023