A Classification Based Model to Assess Customer Behavior in Banking Sector


  • A. Rahman Department of Computer Science, Shaheed Zulfikar Ali Bhutto Institute of Science and Technology, Islamabad, Pakistan
  • M. N. A. Khan Department of Computer Science, Shaheed Zulfikar Ali Bhutto Institute of Science and Technology, Islamabad, Pakistan


A customer relationship management system is used to manage company relationships with current and possible customers. Following a thorough review of contemporary literature, different data mining techniques employed in different types of business, corporate sectors and organizations are analyzed. A model that would be helpful to identify customers’ behavior in the banking sector is then proposed. Three classifiers, k-NN, decision tree and artificial neural networks are used to predict customer behavior and are assessed in order to determine which classifier performs better for predicting customer behavior in the banking sector.


customer, relationship, management, profitability, behavior, data mining, prediction


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T. F. Bahari, M. S. Elayidom, “An Efficient CRM-Data Mining Framework for the Prediction of Customer Behaviour”, Procedia Computer Science, Vol. 46, pp. 725-731, 2015 DOI: https://doi.org/10.1016/j.procs.2015.02.136

A. Shrivastava, B. Kumari, “Implementation of classifiers and their performance evaluation”, International Journal of Engineering Research Online, Vol. 3, No. 2, pp. 71-78, 2015

N. Khan, F. Khan, “Fuzzy based decision making for promotional marketing campaigns”, International Journal of Fuzzy Logic Systems, Vol. 3, No. 1, pp. 64-77, 2013 DOI: https://doi.org/10.5121/ijfls.2013.3107

H. A. Elsalamony, “Bank Direct Marketing Analysis of Data Mining Techniques”, International Journal of Computer Applications, Vol. 85, No. 7, pp. 12-22, 2014 DOI: https://doi.org/10.5120/14852-3218

A. Nachev, “Application of Data Mining Techniques for Direct Marketing”, in: Computational Models for Business and Engineering Domains, pp.86-95, ITHEA, Rzeszow – Sofia, 2014 DOI: https://doi.org/10.1057/9781137406194_12

M. Karim, R. M. Rahman, “Decision Tree and Naïve Bayes Algorithm for Classification and Generation of Actionable Knowledge for Direct Marketing”, Journal of Software Engineering and Applications, Vol. 6, pp. 196-206, 2013 DOI: https://doi.org/10.4236/jsea.2013.64025

S. Emtiyaz, M. Keyvanpour, “Customers behavior modeling by semi-supervised learning in customer relationship management”, arXiv preprint arXiv:1201.1670, 2012

H. Ahn, J. J. Ahn, K. J. Oh, D. H. Kim, “Facilitating cross-selling in a mobile telecom market to develop customer classification model based on hybrid data mining techniques”, Expert Systems with Applications, Vol. 38, No. 5, pp. 5005-5012, 2011 DOI: https://doi.org/10.1016/j.eswa.2010.09.150

I. Bose, X. Chen, “Exploring business opportunities from mobile services data of customers: An inter-cluster analysis approach”, Electronic Commerce Research and Applications, Vol. 9, No. 3, pp. 197-208, 2010 DOI: https://doi.org/10.1016/j.elerap.2009.07.006

Y. L. Chen, M. H. Kuo, S. Y. Wu, K. Tang, “Discovering recency, frequency, and monetary (RFM) sequential patterns from customers’ purchasing data”, Electronic Commerce Research and Applications, Vol. 8, No. 5, pp. 241-251, 2009 DOI: https://doi.org/10.1016/j.elerap.2009.03.002

J. D’Haen, D. Van den Poel, D. Thorleuchter, “Predicting customer profitability during acquisition: Finding the optimal combination of data source and data mining technique”, Expert Systems with Applications, Vol. 40, No. 6, pp. 2007-2012, 2013 DOI: https://doi.org/10.1016/j.eswa.2012.10.023

P. Duchessi, E. J. Lauria, “Decision tree models for profiling ski resorts’ promotional and advertising strategies and the impact on sales”, Expert Systems with Applications, Vol. 40, No. 15, pp. 5822-5829, 2013 DOI: https://doi.org/10.1016/j.eswa.2013.05.017

A. Griva, C. Bardaki, S. Panagiotis, D. Papakiriakopoulos, “A Data Mining Based Framework to Identify Shopping Missions”, Mediterranean Conference on Information Systems, August 4, 2014

S. M. S. Hosseini, A. Maleki, M. R. Gholamian, “Cluster analysis using data mining approach to develop CRM methodology to assess the customer loyalty”, Expert Systems with Applications, Vol. 37, No. 7, pp. 5259-5264, 2010 DOI: https://doi.org/10.1016/j.eswa.2009.12.070

M. Khajvand, K. Zolfaghar, S. Ashoori, S. Alizadeh, “Estimating customer lifetime value based on RFM analysis of customer purchase behavior: Case study”, Procedia Computer Science, Vol. 3, pp. 57-63, 2011 DOI: https://doi.org/10.1016/j.procs.2010.12.011

S. H. Liao, Y. J. Chen, M. Y. Deng, “Mining customer knowledge for tourism new product development and customer relationship management”, Expert Systems with Applications, Vol. 37, No. 6, pp. 4212-4223, 2010 DOI: https://doi.org/10.1016/j.eswa.2009.11.081

V. L. Migueis, A. S. Camanho, J. F. Cunha, “Customer data mining for lifestyle segmentation”, Expert Systems with Applications, Vol. 39, No. 10, pp. 9359-9366, 2012 DOI: https://doi.org/10.1016/j.eswa.2012.02.133

B. Shim, K. Choi, Y. Suh, “CRM strategies for a small-sized online shopping mall based on association rules and sequential patterns”, Expert Systems with Applications, Vol. 39, No. 9, pp. 7736-7742, 2012 DOI: https://doi.org/10.1016/j.eswa.2012.01.080

A. Rahman, M. N. A. Khan,”An Assessment of Data Mining Based CRM Techniques for Enhancing Profitability”, International Journal of Education and Management Engineering, Vol. 7, No. 2, pp.30-40, 2017 DOI: https://doi.org/10.5815/ijeme.2017.02.04


How to Cite

A. Rahman and M. N. A. Khan, “A Classification Based Model to Assess Customer Behavior in Banking Sector”, Eng. Technol. Appl. Sci. Res., vol. 8, no. 3, pp. 2949–2953, Jun. 2018.


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