International Journal of Engineering Research in Computer Science and Engineering (IJERCSE)

Churn Prediction in Financial Institutions Using Machine Learning

Author : Malik Mubasher Hassan ¹ Tabasum Mirza ²

Date of Publication :20th May 2021

Abstract: Customer churn prevention is one of the important components of CRM(Customer Relationship Management) in financial institutions like banks and predictive modeling of customer churn can help in preventing the churn from actually occurring thus saving banks from losses. In this research study we are presenting a comparative analysis of different popular machine learning algorithms for the challenging problem of churn prediction by cross-validation on the basis of performance metrics like accuracy and kappa coefficients. Our results determined the Random Forest algorithm as the best possible classifier for prediction of customer churn in financial institutions with almost 86% accuracy in predictions.

Reference :

1. I. J. Chen and K. Popovich, “Understanding customer relationship management (CRM): People, process and technology,” Bus. Process Manag. J., vol. 9, no. 5, pp. 672–688, 2003, doi: 10.1108/14637150310496758.
2. A. Ghavami, “MA S T ER â€Ÿ S TH E SI S The Impact of CRM The Impact of CRM,” 2006.
3. M. Tabasum, “IMPROVING CUSTOMER RELATIONSHIP MANAGEMENT ( CRM ),” no. 9, pp. 70–75, 2018.
4. M. M. Hassan and T. Mirza, “Churn Prediction in Banking Sector using Bayesian Neural Networks,” vol. 6, no. 12, pp. 3343–3346, 2018.
5. D. Wadikar, “Customer churn prediction,” Masters Diss. Technol. Univ. Dublin., 2020, doi: 10.21427/kpsz-x829. [
6. N. Derby, “Reducing Customer Attrition with Machine Learning for Financial Institutions,” pp. 1769– 2018, 2018.
7. A. Bilal Zoric, “Predicting Customer Churn in Banking Industry using Neural Networks,” Interdiscip. Descr. Complex Syst., vol. 14, no. 2, pp. 116–124, 2016, doi: 10.7906/indecs.14.2.1. [
8. F. Li, J. Lei, Y. Tian, S. Punyapatthanakul, and Y. J. Wang, “Model selection strategy for customer attrition risk prediction in retail banking,” Conf. Res. Pract. Inf. Technol. Ser., vol. 121, pp. 119–124, 2010.
9. B. He, Y. Shi, Q. Wan, and X. Zhao, “Prediction of customer attrition of commercial banks based on SVM model,” Procedia Comput. Sci., vol. 31, pp. 423–430, 2014, doi: 10.1016/j.procs.2014.05.286.
10. T. Vafeiadis, K. I. Diamantaras, G. Sarigiannidis, and K. C. Chatzisavvas, “A comparison of machine learning techniques for customer churn prediction,” Simul. Model. Pract. Theory, vol. 55, no. 10, pp. 1–9, 2015, doi: 10.1016/j.simpat.2015.03.003.
11. S. A. Neslin, S. Gupta, W. Kamakura, J. Lu, and C. H. Mason, “Defection Detection: Measuring and Understanding the Predictive Accuracy of Customer Churn Models,” J. Mark. Res., vol. 43, no. 2, pp. 204– 211, 2006, doi: 10.1509/jmkr.43.2.204.
12. S. F. Sabbeh, “Machine-learning techniques for customer retention: A comparative study,” Int. J. Adv. Comput. Sci. Appl., vol. 9, no. 2, pp. 273–281, 2018, doi: 10.14569/IJACSA.2018.090238.
13. Y. Xie, X. Li, E. W. T. Ngai, and W. Ying, “Customer churn prediction using improved balanced random forests,” Expert Syst. Appl., vol. 36, no. 3 PART 1, pp. 5445–5449, 2009, doi: 10.1016/j.eswa.2008.06.121.
14. C.-F. Tsai and Y.-H. Lu, “Data Mining Techniques in Customer Churn Prediction,” Recent Patents Comput. Sci., vol. 3, no. 1, pp. 28–32, 2010, doi: 10.2174/1874479611003010028.
15. K. A. Amuda and A. B. Adeyemo, “Customers Churn Prediction in Financial Institution Using Artificial Neural Network,” 2019, [Online]. Available: http://arxiv.org/abs/1912.11346.
16. S. H. Iranmanesh, M. Hamid, M. Bastan, G. Hamed Shakouri, and M. M. Nasiri, “Customer churn prediction using artificial neural network: An analytical CRM application,” Proc. Int. Conf. Ind. Eng. Oper. Manag., no. July, pp. 2214–2226, 2019.
17. H. Sayed, M. A. Abdel-Fattah, and S. Kholief, “Predicting potential banking customer churn using Apache Spark ML and MLlib packages: A comparative study,” Int. J. Adv. Comput. Sci. Appl., vol. 9, no. 11, pp. 674–677, 2018, doi: 10.14569/ijacsa.2018.091196.
18. C. F. Tsai and Y. H. Lu, “Customer churn prediction by hybrid neural networks,” Expert Syst. Appl., vol. 36, no. 10, pp. 12547–12553, 2009, doi: 10.1016/j.eswa.2009.05.032.
19. A. T. Jahromi, “Predicting customer churn in telecommunications service providers,” p. 88, 2009, [Online]. Available: http://epubl.ltu.se/1653- 0187/2009/052/.
20. A. Soofi and A. Awan, “Classification Techniques in Machine Learning: Applications and Issues,” J. Basic Appl. Sci., vol. 13, pp. 459–465, 2017, doi: 10.6000/1927-5129.2017.13.76.
21. O. F.Y, A. J.E.T, A. O, H. J. O, O. O, and A. J, “Supervised Machine Learning Algorithms: Classification and Comparison,” Int. J. Comput. Trends Technol., vol. 48, no. 3, pp. 128–138, 2017, doi: 10.14445/22312803/ijctt-v48p126.
22. A. J. Viera and J. M. Garrett, “Understanding Interobserver Agreementâ€¯: The Kappa Statistic,” no. May, pp. 360–363, 2005.
23. J. González Alonso and M. Pazmiño Santacruz, “Cálculo e interpretación del Alfa de Cronbach para el caso de validación de la consistencia interna de un cuestionario, con dos posibles escalas tipo Likert,” Rev. Publicando, vol. 2, no. 2, pp. 62–7, 2015.
24. K. Boggs, Liam, “Performance Measures for Machine Learning 1,” pp. 1–32, 2017.
25. K. J. Danjuma, “Performance Evaluation of Machine Learning Algorithms in Post-operative Life Expectancy in the Lung Cancer Patients,” 2015, [Online]. Available: http://arxiv.org/abs/1504.04646.
26. P. Baldi, S. Brunak, Y. Chauvin, C. A. F. Andersen, and H. Nielsen, “Assessing the accuracy of prediction algorithms for classification: An overview,” Bioinformatics, vol. 16, no. 5, pp. 412–424, 2000, doi: 10.1093/bioinformatics/16.5.412.