Improved NARX-ANFIS Network structure with Genetic Algorithm to optimizing Cash Flow of ATM Model

Subject Areas : Economic and Financial Time Series

Neda kiani ¹ , Ghasem Tohidi ² , Shabnam Razavyan ³ , Nosratallah Shadnoosh ⁴ , Masood Sanei ⁵

1 - Department of Industrial Management, Central Tehran Branch, Islamic Azad University, Tehran, Iran
2 - Department of Managment, Islamic Azad University, Central Tehran Branch, Tehran, Iran
3 - Department of Mathematic, South Tehran Branch, Islamic Azad University, Tehran, Iran
4 - Department of Industrial Management, Central Tehran Branch, Islamic Azad University, Tehran, Iran
5 - Department of Mathematic, Central Tehran Branch, Islamic Azad University, Tehran, Iran

Received: 2019-11-09 Accepted : 2020-06-22 Published : 2023-01-01

Keywords: Genetic Algorithm, autoregressive with exogenous input network, ATM device, Prediction model,

Abstract :

Nowadays, the rapid growth of data in organizations has caused managers to look for a way to analyze them. Extracting useful knowledge from aggregation data can lead to appropriate strategic decision-making for the organization. This paper suggests an application of hybrid network based on amount month demand in every ATM device based on transaction mean of 9 months for 1377 devices to obtain customer behavior patterns, to do so, first designed a basic model based on an auto-regressive with exogenous input network (NARX) then, the optimization of the weight and bias of the designed network is made by the genetic algorithm (GA). As a result, finding the weights of the network represents a nonlinear optimization problem that is solved by the genetic algorithm. Paper results show that the NARX-ANFIS Hybrid network using GA for the learning of rules and to optimize the network weights and weights of the network and the fixed threshold can improve the accuracy of the prediction model. Also, classic models are more efficient and increased benefits and lower financing costs and more rational inventory cash control. As well, the designed model can lead to increase benefits and decrease costs in the bank so that, exact forecast and optimal cash upload in ATMs will lead to increase funds on the bank and rise customers and popularity the brand of the bank.

References:

[1] ATMmarketplace.com, ATM software trends and analysis, Net World Alliance, Tech. Rep, 2010.

[2] Abor, J., Technological innovation and banking in Ghana: An evaluation of Customer perception, American Academy of Financial Management, 2004, P.416–421.

[3] Simutis, R., Dilijonas, D., Bastina, L., Friman, J., and Drobinov, P., Optimization of cash management for ATM network, Information Technology and Control, Kaunas, Technologija, 2007, 36(1) A, P.117 – 121.

[4] Simutis, R., Dilijonas, D., Bastina, L., and Friman, J., A Flexible Neural Network for ATM Cash Demand Forecasting, in Proc. 16th Int. Conf. on Computational intelligence, Man-Machine System and Cybernetics, Spain, 2007, Dec. 14-16, P. 162-165.

[5] Dilijonas, D., and Bastina, L., Retail Banking Optimization System Based on Multi-Agents Technology, in Proc. 16th Int. Conf. on Computational intelligence, Man-Machine System and Cybernetics, Spain, 2007, Dec. 14-16, P. 203-208.

[6] Simutis, R., Dilijonas, D., and Bastina, L., Cash demand forecasting for ATM using neural networks and support vector regression algorithms, in EurOPT 2008 - Proc. of the 20th Euro Mini Conf. on Continuous Optimization and Knowledge-Based Technologies, 2008, May 20-23.

[7] darwish, Saad M., A Methodology to Improve Cash Demand Forecasting for ATM Network, International Journal of Computer and Electrical Engineering, 2013, 5(4), August.

[8] Armenise, R., Birtolo, C., Sangianantoni, E., and Troiano, L., Optimizing ATM Cash Management by Genetic Algorithms, International Journal of Computer Information Systems and Industrial Management Applications, 2012, 4, P. 598-608.

[9] Ekinci, Y., Lu, J-C., Duman, E., Optimization of ATM Cash Replenishment with Group- Demand Forecasts, Expert Systems with Applications, 2014, Doi: 10.1016/j.eswa.2014.12.011.

[10] Venkatesh, k., Ravi, V., Prinzie, A., Poel, D., Cash demand forecasting in ATMs by clustering and neural networks, European Journal of Operational Research, 2014, 232, P.383–392. Doi:10.1016/j.ejor.2013.07.027.

[11] Arora N., Kumar, J., Saini, R., Approximating Methodology: Managing Cash in Automated Teller Machines using Fuzzy ARTMAP Network, International Journal of Enhanced Research in Science Technology & Engineering, ISSN: 2319-7463, February, 2014, 3(2), P. 318-326.

[12] Bhandari, R., Gill, J., An artificial intelligence ATM forecasting system for hybrid neural networks, International Journal of Computer Applications, 2016, 133(3), P.13-16. Doi: 10.1.1.734.7389.

[13] BİLİR, C., DÖŞEYEN, A., Optimization of ATM and Branch Cash Operations Using an Integrated Cash Requirement Forecasting and Cash Optimization Model, Business & Management Studies: An International Journal,), 2018, 6(1).  Doi:10.15295/bmij.v6i1.219.

[14] Vennila, A., and Rathnaraj, S.N., Impact on Customer Perception towards ATM Services Provided by the Banks Today: A Conceptual Study, International Journal of Scientific Research and Management, 2018, 6(01). Doi: 10.18535/ijsrm/v6i1.em06.

[15] Demuth, H., Beale, M., Hagan, M, Neural Network Toolbox™ 6-User’s Guide, Mathworks Inc, 2009.

[16] Hagan, M.T., De Jesus, O., and Schultz, R., Training Recurrent Networks for Filtering and Control, Chapter 12 in Recurrent Neural Networks: Design and Applications, L. Medsker and L.C. Jain, Eds., CRC Press, 1999, P. 311–340.

[17] Gross, G., and Galiana, F.D., short term Load Forecasting, Proc. Dec. 1987, IEEE, 75(12), P. 1558-1573.

[18] Arora, N. and Saini, J.R., Estimation and approximation using neuro-fuzzy systems, International Journal of Intelligent Systems and Applications, 2016, 8(6), P.9. Doi: 10.5815/ijisa.2016.06.02.

[19] Sugeno, M., and Kang, G.T, Structure identification of fuzzy model, Fuzzy Sets Systems, 1988, 28, P.15-33.

[20] Jang, J.S.R., Sun, C.T. and Mizutani, E, Neuro-fuzzy and soft computing, Prentice-Hall, New Jersey, 1997.

[21] Wibowo, A., Desa, M.I., Kernel Based Regression and Genetic algorithms for Estimating Cutting Conditions of Surface Roughness in End Milling Machining Process, Expert System with Applications, Elsevier, 2012, 39(14), P.11634-11641.  Doi:10.1016/j.eswa.2012.04.004.

[22] Wulandhari, L.A., Wibowo, A., and Desa M.I., Condition Diagnosis of Multiple Bearings Using Adaptive Probabilistic Based Genetic Algorithms and Back Propagation Neural Networks, Neural Computing and Applications, Springer, 2015. Doi:10.1007/s00521-014-1698-6.

[23] Rajasekaran, S., G.A. Vijayalakshmi, Neural Networks, Fuzzy Logic, and Genetic Algorithms: Synthesis and Applications, Prentice Hall of India Private Limited, New Delhi, 2007.

[24] Haykin, S. Neural Networks a Comprehensive Foundation, Second edition, Prentice Hall   International Inc, 1994.

[25] De j., Kenneth, A., Spears, W. M., Gordon, Diana F., using genetic algorithms for concept learning. In: Genetic Algorithms for Machine Learning. Springer US, 1993, P. 5-32.

[26] Rashidi Baghi, M., Taherinia, M., Prediction the Return Fluctuations with Artificial Neural Networks' Approach, Advances in Mathematical Finance and Applications, 2019, 4(2), P.103-114. Doi: 10.22034/amfa.2019.580643.1149.

[27] Holland, J., Adaptation in Natural and Artificial Systems: An Introductory Analysis with Application to Biology, University of Michigan Press: Ann Arbor, MI, USA, 1975.

[28] Hassan, R., Cohanim, B., De Weck, O., Venter, G., A comparison of particle swarm optimization and the genetic algorithm. In Proceedings of the 46th AIAA/ASME/ASCE/AHS/ASC structures, structural dynamics and materials conference, Austin, TX, USA, P.18–21 April 2005, P. 1897.

[29] Farshadfar, Z., Prokopczuk, M., Improving Stock Return Forecasting by Deep Learning Algorithm. Advances in Mathematical Finance and Applications, 2019, 4(3), P.1-13. Doi: 10.22034/amfa.2019.584494.1173