ATM Cash Flow Prediction and Replenishment Optimization with ANN

Year 2019, Volume: 11 Issue: 1, 402 - 408, 31.01.2019

Sefik İlkin Serengil , Alper Ozpinar

https://doi.org/10.29137/umagd.484670

Cited By: 5

Abstract

ATMs are physical interaction points between
financial institutions and real customers. Storing physical cash causes
renouncing to get interested. On the other hand, customer satisfaction requires
to store the necessary cash amount. This concern becomes even more critical for
countries having high-interest rate and overnight interest rates are higher. In
this paper, we will show that daily cash withdrawals are predictable and we
will propose a cost function for replenishment optimization. Experiments show
that proposed model decrease idle balance dramatically.

Keywords

ATM Replenishment , Cash Optimization

References

• Armenise, R., Birtolo, C., Sangianantoni, E., & Troiano, L. (2010). A generative solution for ATM CashManagement. Soft Computing and Pattern Recognition. Paris, France.
• Chen, W., Xie, X., Wang, J., Pradhan, B., Hong, H., Bui, D., . . . Ma, J. (2017). A comparative study of logistic model tree, random forest, and classification and regression tree models for spatial prediction of landslide susceptibility. Catena, 151, 147-160.
• Ekinci, Y., Lu, J.-C., & Duman, E. (2015). Optimization of ATM cash replenishment with group-demand forecasts. Expert Systems with Applications, 42, 3480–3490.
• Heaton, J. (2008). Introduction to Neural Networks for Java. Heaton Research, Inc.
• Kumar, P., & Walia, E. (2006). Cash Forecasting: An Application of Artificial Neural Networks in Finance. International Journal of Computer Science and Applications, 3(1), 61-77.
• Ozpinar, A. (2007). Modeling and Planning of Energy Production in Renewable Energy Stations with Artificial Neural Networks. PhD Thesis Submitted to Yildiz Technical University.
• Sculley, D., Holt, G., Golovin, D., Davydov, E., Phillips, T., Ebner, D., Dennison, D. (2015). Hidden Technical Debt in Machine Learning Systems. Advances in neural information processing systems.
• Serengil, S., & Ozpinar, A. (2016). Planning Workforce Management for Bank Operation Centers with Neural Networks. Proceedings of the 15th WSEAS International Conference on Artificial Intelligence, Knowledge Engineering and Databases. Venice.
• Serengil, S., & Ozpinar, A. (2017). Workforce Optimization for Bank Operation Centers: A Machine Learning Approach. International Journal of Interactive Multimedia and Artificial Intelligence, 4(6), 81-87,.
• Simutis, R., Dilijonas, D., & Bastina, L. (2008). Cash Demand Forecasting for ATM using Neural Networks. Continuous Optimization and Knowledge-Based Technologies EurOPT-2008. Lithuania.
• Simutis, R., Dilijonas, D., Bastina, L., Friman, J., & Drobinov, P. (2007). Optimization of Cash Management for ATM Network. Information technology and control, 36(1), 117-121.
• Zapranis , A., & Alexandridis , A. (2009). Forecasting cash money withdrawals using wavelet analysis and wavelet neural networks. International Journal of Financial Economics and Econometrics.