Money Leaks in Banking ATM’s Cash-Management Systems
DOI:
https://doi.org/10.34021/ve.2020.03.02(2)Keywords:
ATMs Cash Management, Stochastic Processes, Bank Data Processing, New Methodology Tested, Cashback SitesAbstract
Some widely-accepted practices on banking ATM networks may negatively affect efficient liquidity management. This paper analyses ATM cash management in light of empirical evidence which suggests that banking ATMs tend to be overloaded beyond the customer’s needs. This, in turn, results in high opportunity costs. While this is not perceived by banks as particularly harmful, it might have a damaging impact on other business which revolves exclusively around ATM networks, such as cashback sites. A dormant money case may be solved by an appropriate tool matching the ATM’s cash to the user’s needs. Supported by a large database of banking records, this paper also provides model validation for a set of theorems previously developed by the author, resulting here in a cutting-edge, reliable forecasting system, suitable for anticipating ATMs cash demand as well as coupling with other supply chain planning processes.
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References
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Castro, J. (2009). A Stochastic Programming Approach to Cash Management in Banking. European Journal of Operational Research, 192(3), 963-974. https://doi.org/10.1016/j.ejor.2007.10.015
Darwish, S.M. (2013). A Methodology to Improve Cash Demand Forecasting for ATM Network. International Journal of Computer and Electrical Engineering, 5(4), 405-409. https://doi.org/10.7763/IJCEE.2013.V5.741
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Jadwal, P.K., Jain, S., Gupta, U., & Khanna, P. (2018). K-Means Clustering with Neural Networks for ATM Cash Repository Prediction. In S. Satapathy & A. Joshi (Eds.), Information and Communication Technology for Intelligent Systems (ICTIS 2017) - Volume 1. ICTIS 2017. Smart Innovation, Systems and Technologies, vol 83 (pp. 588-596). Cham, Switzerland: Springer International Publishing. https://doi.org/10.1007/978-3-319-63673-3_71
Kouzelis, A. (1987, June). On the determinants of ATM performance. European Journal of Operational Research, 30(1), 89-94. https://doi.org/10.1016/0377-2217(87)90015-4
Naserabadi, B., & Mirzazadeh, A., & Nodoust, S. (2014). A New Mathematical Inventory Model with Stochastic and Fuzzy Deterioration Rate under Inflation. Chinese Journal of Engineering, Article ID 347857, 1-10. http://dx.doi.org/10.1155/2014/347857
Osorio, A.F., & Toro, H.H. (2012). A MIP model to optimize a Columbian cash supply chain. International Transactions in Operational Research, 19(5), 659-673. https://doi.org/10.1111/j.1475-3995.2011.00850.x
Paradi, J.C., & Zhu, H. (2013). A survey on bank branch efficiency and performance research with data envelopment analysis. Omega, 41(1), 61-79. https://doi.org/10.1016/j.omega.2011.08.010
Teddy, S.D., & Ng, S.K. (2011, July-September). Forecasting ATM cash demands using a local learning model of cerebellar associative memory network. International Journal of Forecasting, 27(3), 760-776. https://doi.org/10.1016/j.ijforecast.2010.02.013
Valverde , S.C., & Humphrey, D.B. (2009). Technological Innovation in Banking: The Shift to ATM’s and Implicit Pricing of Network Convenience. In L. Anderloni, D. T. Llewellyn, & R. H. Schmidt (Eds.), Financial Innovation in Retail and Corporate Banking (pp. 89-110). Cheltenham, United Kingdom: Edward Elgar Publishing. https://doi.org/10.4337/9781848447189.00010
Van der Heide, L.M., Coelho, L.C., Vis, I.F.A., & Van Anholt, R.G. (2020, February). Replenishment and denomination mix of automated teller machines with dynamic forecast demands. Computers and Operations Research, 114, 104828. https://doi.org/10.1016/j.cor.2019.104828
Venkatesh, K., Ravi, V., Prinzie, A., & Van den Poel, D. (2014, January 16). Cash demand forecasting in ATMs by clustering and neural networks. European Journal of Operational Research, 232(2), 383-392. https://doi.org/10.1016/j.ejor.2013.07.027
Wagner, M. (2007). The optimal cash deployment strategy-modelling a network of ATMs. M.Sc. thesis. Finland: Swedish School of Economics and Business Administration.
Xu, Z., Elomri, A., Zhang, Q., Liu, C., & Shi, L. (2020). Status review and research strategies on product-service supply chain. Proceedings of the Institution of Mechanical Engineers, Part B: Journal of Engineering Manufacture, 234(8), 1075-1086. https://doi.org/10.1177/0954405420905199
Attanasio, O.P., Guiso, L.M., & Japelli,T. (2002). The Demand for Money, Financial Innovation, and the Welfare Cost of Inflation: An Analysis with Household Data. Journal of Political Economy, 110(2), 317-351. http://dx.doi.org/10.1086/338743
Barth, J.R., Caprio, G., & Levine, R. (2004). Bank regulation and supervision: what works best? Journal of Financial Intermediation, 13, 205-248. https://doi.org/10.1016/j.jfi.2003.06.002
Bolt, W., & Humphrey, D.B. (2010). Bank Competition Efficiency in Europe: A Frontier Approach. Journal of Banking & Finance, 34(8), 1808-1817. https://doi.org/10.1016/j.jbankfin.2009.09.019
Camanho, A.S., & Dyson, R.G. (1999, September). Efficiency, Size, Benchmarks and Targets for Bank Branches: An Application of Data Envelopment Analysis. The Journal of the Operational Research Society, 50(9), 903-915. https://doi.org/10.2307/3010188
Castro, J. (2009). A Stochastic Programming Approach to Cash Management in Banking. European Journal of Operational Research, 192(3), 963-974. https://doi.org/10.1016/j.ejor.2007.10.015
Darwish, S.M. (2013). A Methodology to Improve Cash Demand Forecasting for ATM Network. International Journal of Computer and Electrical Engineering, 5(4), 405-409. https://doi.org/10.7763/IJCEE.2013.V5.741
Diamond, D.W., & Rajan, R.G. (2011, May). Fear of Fire Sales, Illiquidity Seeking, and Credit Freezes. The Quarterly Journal of Economics, 126(2), 557-591. https://doi.org/10.1093/qje/qjr012
Ekinci, Y., Lu, J.Ch., & Duman, E. (2015, May 1). Optimization of ATM cash replenishment with group-demand forecasts. Expert Systems with Applications, 42(7), 3480-3490. https://doi.org/10.1016/j.eswa.2014.12.011
García Cabello, J. (2013a). An Efficient Liquidity Management for ATMs. Aestimatio, The IEB International Journal of Finance, 6, 50-75.
García Cabello, J. (2013b). Cash efficiency for bank branches. Springerplus, 2, 334. https://doi.org/10.1186/2193-1801-2-334
García Cabello, J., & Lobillo, F. (2017). Sound branch cash management for less: A low-cost forecasting algorithm under uncertain demand. Omega, 70, 118-134. https://doi.org/10.1016/j.omega.2016.09.005
Hill, A.V., Zhang, W., & Burch, G.F. (2015). Forecasting the forecastability quotient for inventory management. International Journal of Forecasting, 31(3), 651-663. https://doi.org/10.1016/j.ijforecast.2014.10.006
Humphrey, D.B., Willesson, M., Bergendahl, G., & Lindblom, T. (2006). Benefits from a changing payment technology in European banking. Journal of Banking & Finance, 30(6), 1631-1652. https://doi.org/10.1016/j.jbankfin.2005.09.009
Jadwal, P.K., Jain, S., Gupta, U., & Khanna, P. (2018). K-Means Clustering with Neural Networks for ATM Cash Repository Prediction. In S. Satapathy & A. Joshi (Eds.), Information and Communication Technology for Intelligent Systems (ICTIS 2017) - Volume 1. ICTIS 2017. Smart Innovation, Systems and Technologies, vol 83 (pp. 588-596). Cham, Switzerland: Springer International Publishing. https://doi.org/10.1007/978-3-319-63673-3_71
Kouzelis, A. (1987, June). On the determinants of ATM performance. European Journal of Operational Research, 30(1), 89-94. https://doi.org/10.1016/0377-2217(87)90015-4
Naserabadi, B., & Mirzazadeh, A., & Nodoust, S. (2014). A New Mathematical Inventory Model with Stochastic and Fuzzy Deterioration Rate under Inflation. Chinese Journal of Engineering, Article ID 347857, 1-10. http://dx.doi.org/10.1155/2014/347857
Osorio, A.F., & Toro, H.H. (2012). A MIP model to optimize a Columbian cash supply chain. International Transactions in Operational Research, 19(5), 659-673. https://doi.org/10.1111/j.1475-3995.2011.00850.x
Paradi, J.C., & Zhu, H. (2013). A survey on bank branch efficiency and performance research with data envelopment analysis. Omega, 41(1), 61-79. https://doi.org/10.1016/j.omega.2011.08.010
Teddy, S.D., & Ng, S.K. (2011, July-September). Forecasting ATM cash demands using a local learning model of cerebellar associative memory network. International Journal of Forecasting, 27(3), 760-776. https://doi.org/10.1016/j.ijforecast.2010.02.013
Valverde , S.C., & Humphrey, D.B. (2009). Technological Innovation in Banking: The Shift to ATM’s and Implicit Pricing of Network Convenience. In L. Anderloni, D. T. Llewellyn, & R. H. Schmidt (Eds.), Financial Innovation in Retail and Corporate Banking (pp. 89-110). Cheltenham, United Kingdom: Edward Elgar Publishing. https://doi.org/10.4337/9781848447189.00010
Van der Heide, L.M., Coelho, L.C., Vis, I.F.A., & Van Anholt, R.G. (2020, February). Replenishment and denomination mix of automated teller machines with dynamic forecast demands. Computers and Operations Research, 114, 104828. https://doi.org/10.1016/j.cor.2019.104828
Venkatesh, K., Ravi, V., Prinzie, A., & Van den Poel, D. (2014, January 16). Cash demand forecasting in ATMs by clustering and neural networks. European Journal of Operational Research, 232(2), 383-392. https://doi.org/10.1016/j.ejor.2013.07.027
Wagner, M. (2007). The optimal cash deployment strategy-modelling a network of ATMs. M.Sc. thesis. Finland: Swedish School of Economics and Business Administration.
Xu, Z., Elomri, A., Zhang, Q., Liu, C., & Shi, L. (2020). Status review and research strategies on product-service supply chain. Proceedings of the Institution of Mechanical Engineers, Part B: Journal of Engineering Manufacture, 234(8), 1075-1086. https://doi.org/10.1177/0954405420905199
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Published
2020-04-30
How to Cite
García Cabello, J. (2020). Money Leaks in Banking ATM’s Cash-Management Systems. Virtual Economics, 3(2), 25–42. https://doi.org/10.34021/ve.2020.03.02(2)
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