Abstract
ATM cash flow management is a challenging task which involves both machine learning predictions and encashment planning. Banks employ these systems to optimize their costs and improve the overall device availability via reducing the number of device failures. Although cash flow prediction is a common task, complete design of the cost optimization system is a complex design problem. In this article we present our complete encashment strategy methodology. We evaluate the proposed system design on real world data from one of the Russian banks. We show that one can effectively achieve \(18\,\%\) cost reduction by employing such strategy.
This is a preview of subscription content, log in via an institution to check access.
Access this chapter
Tax calculation will be finalised at checkout
Purchases are for personal use only
References
Westland, C.J.: Preference ordering cash, near cash and electronic cash. J. Organ. Comput. Electron. Commer. 12(3), 223–242 (2012)
Wagner, M.: The optimal cash deployment strategy – modeling a network of ATMs. Master thesis, Swedish School of Economics and Business Administration, Finland (2007)
Gubar, E., Zubareva, M., Merzljakova, J.: Cash flow optimization in ATM network model. Contrib. Game Theory Manag. 4, 213–222 (2011)
Wagner, M.: Forecasting daily demand in cash supply chains. Am. J. Econ. Bus. Adm. 2(4), 377–383 (2010)
PremChand Kumar, P., Walia, E.: Cash forecasting: an application of artificial neural networks in finance. Int. J. Comput. Sci. Appl. 3(1), 61–77 (2006)
Simutis, R., Dilijonas, D., Bastina, L.: Cash demand forecasting for ATM using neural networks and support vector regression algorithms. In: 20th EURO Mini Conference “Continuous Optimization and Knowledge-Based Technologies” (EurOPT- 2008), pp. 416–421 (2008)
Simutis, R., Dilijonas, D., Bastina, L., Friman, J.: A flexible neural network for ATM cash demand forecasting. In: 6th WSEAS International Conference on Computational Intelligence, Man-Machine Systems and Cybernetics, pp. 162–167 (2007)
Simutis, R., Dilijonas, D., Bastina, L., Friman, J., Drobinov, P: Optimization of cash management for ATM network. Inf. Technol. Control 36(1A), 117–212 (2007)
Kamini, V., Ravi, V., Prinzie, A., Van den Poel, D.: Cash demand forecasting in ATMs by clustering and neural networks. Eur. J. Oper. Res. 232(2), 383–392 (2013)
Zapranis, A., Alexandridis, A.: Forecasting cash money withdrawals using wavelet analysis and wavelet neural networks. Int. J. Financ. Econ. Econometrics (2009) (to appear)
Darwish, S.M.: A methodology to improve cash demand forecasting for ATM network. Int. J. Comput. Electr. Eng. 5(4), 405–409 (2013)
Castro, J.: A stochastic programming approach to cash management in banking. Eur. J. Oper. Res. 192(3), 963–974 (2009)
Esteves, P.S., Rodriguesm, P.M.M.: Calendar effects in daily ATM withdrawals. Econ. Bull. 30(4), 2587–2597 (2010)
Author information
Authors and Affiliations
Corresponding author
Editor information
Editors and Affiliations
Rights and permissions
Copyright information
© 2015 Springer International Publishing Switzerland
About this paper
Cite this paper
Grozin, V., Natekin, A., Knoll, A. (2015). ATM Service Cost Optimization Using Predictive Encashment Strategy. In: Khachay, M., Konstantinova, N., Panchenko, A., Ignatov, D., Labunets, V. (eds) Analysis of Images, Social Networks and Texts. AIST 2015. Communications in Computer and Information Science, vol 542. Springer, Cham. https://doi.org/10.1007/978-3-319-26123-2_37
Download citation
DOI: https://doi.org/10.1007/978-3-319-26123-2_37
Published:
Publisher Name: Springer, Cham
Print ISBN: 978-3-319-26122-5
Online ISBN: 978-3-319-26123-2
eBook Packages: Computer ScienceComputer Science (R0)