Predicting the Success of Bank Telemarketing Using Artificial Neural Network

Authors: Mokrane Selma

Abstract:

The shift towards decision making (DM) based on artificial intelligence (AI) techniques will change the way in which consumer markets and our societies function. Through AI, predictive analytics is being used by businesses to identify these patterns and major trends with the objective to improve the DM and influence future business outcomes. This paper proposes an Artificial Neural Network (ANN) approach to predict the success of telemarketing calls for selling bank long-term deposits. To validate the proposed model, we uses the bank marketing data of 41188 phone calls. The ANN attains 98.93% of accuracy which outperforms other conventional classifiers and confirms that it is credible and valuable approach for telemarketing campaign managers.

Keywords: Bank telemarketing, prediction, decision making, artificial intelligence, artificial neural network.

Digital Object Identifier (DOI): doi.org/10.5281/zenodo.3607876

References:

[1] P. Kotler, and K. L. Keller. “Marketing Management”.14th Edition, New Jersey: Prentice Hall, 2012.
[2] S. Moro, P.Cortez, and P. Rita. “A data-driven approach to predict the success of bank telemarketing”. Decision Support Systems, 2014, 62, 22–31. http://dx.doi.org/10.1016/j.dss.2014.03.001.
[3] S. Moro, R. M. S. Laureano and P. Cortez. "Using Data Mining for Bank Direct Marketing: an Application of The CRISP-DM Methodology," in Proceedings of the European Simulation and Modelling Conference - ESM' 2011, Guimarães, 2011.
[4] K. H. Kim, et al., “Predicting the success of bank telemarketing using deep convolutional neural network,” in Proc. 7th International Conference of Soft Computing and Pattern Recognition (SoCPaR), Fukuoka, 2015.
[5] F. J. L. Iturriaga, and I. P. Sanz. “Bankruptcy visualization and prediction using neural networks: A study of US commercial banks”. Expert Systems with Applications, 2015, 42 (6), p. 2857-2869.
[6] Kwon, H.-B., & Lee, J. “Two-stage production modeling of large U.S. banks: A DEA-neural network approach”. Expert Systems with Applications, 2015, 42(19), 6758–6766. http://dx.doi.org/10.1016/j.eswa.2015.04.062.
[7] L. G. Brock III, and L. B. Davis. “Estimating available supermarket commodities for food bank collection in the absence of information”. Expert Systems with Applications, 2015, 42(7), 3450-3461.
[8] C. Jeong, J. H. Min, and M. S. Kim. “A tuning method for the architecture of neural network models incorporating GAM and GA as applied to bankruptcy prediction”. Expert Systems with Applications, 2012, 39 (3), pp. 3650-3658.
[9] P. Du Jardin. “Predicting bankruptcy using neural networks and other classification methods: The influence of variable selection techniques on model accuracy”. Neurocomputing, 2010, 73 (10), pp .2047-2060.
[10] A. Asuncion, and D. Newman, CA: University of California, School of Information and Computer Science, UCI machine learning repository. Irvine, 2012.
[11] S. Moro, P. Cortez and P. Rita. “A Data-Driven Approach to Predict the Success of Bank Telemarketing”. Decision Support Systems, Elsevier, 2014, 62:22-31.
[12] S. Bajpai, K. Jain, and J. Neeti, “Artificial Neural Networks,” the International Journal of Soft Computing and Engineering, vol. 1, no. 2011, pp. 2231-2307, 2011.