UNVEILING CHURN PREDICTION AT BANK IVORY

Raymond Sunardi Oetama

doi:10.23960/jitet.v11i3s1.3394

Raymond Sunardi Oetama
Universitas Multimedia Nusantara

DOI: https://doi.org/10.23960/jitet.v11i3s1.3394

Abstract Views (Last 12 Months)

312 Abstract Views

343 Downloads

Abstract

The banking industry faces significant challenges in tackling customer churn within its credit card services. Customer churn refers to the situation where customers discontinue using a bank's services and migrate to another financial institution. To proactively address this critical issue, the present research endeavors to predict customer attrition in credit card services. To achieve this goal, the study extensively employs the CRISP-DM framework and diligently compares the performance of two predictive models, namely Gradient Boosting and Random Forest. The research endeavors to identify potential churn customers by analyzing crucial variables, including customer age, marital status, gender, income category, credit limit, and total transactions. The preferred modeling approach, determined based on the lowest misclassification rate, serves as a vital component of the research's analytical process. Remarkably, the research findings unequivocally demonstrate the superior performance of the Gradient Boosting model, which attains a misclassification rate of 0.1118 in predicting customer attrition.

Downloads

Download data is not yet available.

References

S. Nurjannah, “Digital Transformation In The Banking Industry Challenges And Opportunities,” Iternational J. Accounting, Manag. Econ., vol. 1, no. 01, 2023.

A. Palandurkara, D. Bawankule, A. Bagde, F. Deshpande, S. Kamble, and S. Shende, “Customer Churn Prediction in Banking Environment using Data Science”.

H. Jain, G. Yadav, and R. Manoov, “Churn prediction and retention in banking, telecom and IT sectors using machine learning techniques,” in Advances in Machine Learning and Computational Intelligence: Proceedings of ICMLCI 2019, Springer, 2020, pp. 137–156.

H. Hardjadinata, R. S. Oetama, and I. Prasetiawan, “Facial Expression Recognition Using Xception And DenseNet Architecture,” in 2021 6th International Conference on New Media Studies (CONMEDIA), 2021, pp. 60–65.

N. T. Msweli, T. Mawela, and H. Twinomurinzi, “Massifying Data Science Education through Immersive Datathons,” 2023.

W. H. Wijaya, R. S. Oetama, and F. A. Halim, “Implementation of Backpropagation Method with MLPClassifier to Face Mask Detection Model,” IJNMT (International J. New Media Technol., vol. 9, no. 2, pp. 48–55, 2022.

S. J. Haddadi, M. O. Mohammadi, M. Bahrami, E. Khoeini, M. Beygi, and M. H. Khoshkar, “Customer churn prediction in the iranian banking sector,” in 2022 International Conference on Applied Artificial Intelligence (ICAPAI), 2022, pp. 1–6.

A. Kinge, Y. Oswal, T. Khangal, N. Kulkarni, and P. Jha, “Comparative study on different classification models for customer churn problem,” in Machine Intelligence and Smart Systems: Proceedings of MISS 2021, Springer, 2022, pp. 153–164.

R. A. de Lima Lemos, T. C. Silva, and B. M. Tabak, “Propension to customer churn in a financial institution: A machine learning approach,” Neural Comput. Appl., vol. 34, no. 14, pp. 11751–11768, 2022.

A. Qutub, A. Al-Mehmadi, M. Al-Hssan, R. Aljohani, and H. S. Alghamdi, “Prediction of employee attrition using machine learning and ensemble methods,” Int. J. Mach. Learn. Comput, vol. 11, no. 2, pp. 110–114, 2021.

V. A. Dev and M. R. Eden, “Formation lithology classification using scalable gradient boosted decision trees,” Comput. Chem. Eng., vol. 128, pp. 392–404, 2019.

J. L. M. Arruda, R. B. C. Prudêncio, and A. C. Lorena, “Measuring instance hardness using data complexity measures,” in Intelligent Systems: 9th Brazilian Conference, BRACIS 2020, Rio Grande, Brazil, October 20–23, 2020, Proceedings, Part II 9, 2020, pp. 483–497.