Abstract
References
- Keramati, A., Ghaneei, H., & Mirmohammadi, S. M. (2016). Developing a prediction model for customer churn from electronic banking services using data mining. Financial Innovation, 2, 1-13. https://doi.org/10.1186/s40854-016-0029-6
- Jamjoom, A. A. (2021). The use of knowledge extraction in predicting customer churn in B2B. Journal of Big Data, 8(1), 110. https://doi.org/10.1186/s40537-021-00500-3
- Arnaldo, M. (2003). Origins and Early Development of Banking in Ethiopia. UNIMI Economics Working Paper No. 04.2003, http://dx.doi.org/10.2139/ssrn.667265
- Prabadevi, B., Shalini, R., & Kavitha, B. R. (2023). Customer churning analysis using machine learning algorithms. International Journal of Intelligent Networks, 4, 145-154. https://doi.org/10.1016/J.IJIN.2023.05.005
- Wagh, S. K., Andhale, A. A., Wagh, K. S., Pansare, J. R., Ambadekar, S. P., & Gawande, S. H. (2024). Customer churn prediction in telecom sector using machine learning techniques. Results in Control and Optimization, 14, 100342. https://doi.org/10.1016/J.RICO.2023.100342
- Gebremeskel, K. (2013). Application of data mining techniques to predict customers’ churn at Commercial Bank of Ethiopia (Master’s thesis). Addis Ababa University, School of Graduate Studies, School of Information Science.
- Gebreegziabher, B. (2022). Bank customer churn prediction model: The case of commercial bank of Ethiopia (Doctoral dissertation, St. Mary’s University).
- Kingawa, E. D., & Hailu, T. T. (2022). Customer Churn Prediction Using Machine Learning Techniques: the case of Lion Insurance. Asian Journal of Basic Science & Research, 4(4), 60-73. https://doi.org/10.38177/ajbsr.2022.4407
- Seid, M. H., & Woldeyohannis, M. M. (2022, November). Customer churn prediction using machine learning: commercial bank of Ethiopia. In 2022 International Conference on Information and Communication Technology for Development for Africa (ICT4DA) (pp. 1-6). IEEE. https://doi.org/10.1109/ICT4DA56482.2022.9971224
- Rahman, M., & Kumar, V. (2020, November). Machine learning based customer churn prediction in banking. In 2020 4th international conference on electronics, communication and aerospace technology (ICECA) (pp. 1196-1201). IEEE. https://doi.org/10.1109/ICECA49313.2020.9297529
- IEEE Communications Society. (2008). WICOM 2008: 2008 International Conference on Wireless Communications, Networking and Mobile Computing: October 12-1, 2008, Dalian, China. IEEE.
- Dalmia, H., Nikil, C. V., & Kumar, S. (2020). Churning of bank customers using supervised learning. In Innovations in Electronics and Communication Engineering: Proceedings of the 8th ICIECE 2019 (pp. 681-691). Springer Singapore. https://doi.org/10.1007/978-981-15-3172-9_64
- He, B., Shi, Y., Wan, Q., & Zhao, X. (2014). Prediction of customer attrition of commercial banks based on SVM model. Procedia computer science, 31, 423-430. https://doi.org/10.1016/j.procs.2014.05.286
Abstract
Customer churn prediction refers to the procedure of identifying customers who are highly likely to terminate their service subscription based on their utilization. Being able to predict a customer who is likely to churn is essential for solving business problems. The banking industry in Ethiopia currently has millions of users, making it challenging to analyze and anticipate customer attrition. There are diverse researches conducted in this particular domain. The primary challenges encountered in the majority of the prior investigations were associated with the selection of suitable technique for achieving data balancing, the predicaments revolving around the choice of a technique for handling missing values, the excessive dependence of the model on a singular attribute, and various others. The aim of this research is to develop a machine-learning model that can predict customer churn. The dataset utilized for this investigation comprises 50,987 entries encompassing 11 attributes, which were collected from Awash Bank Wolaita Sodo region. Among these, 31,619 represent active accounts, while the remaining 19,368 pertain to closed (churn) accounts. To achieve balance within the dataset, a SMOTE-ENN method is employed, while an extraction tree classifier is employed for important feature selection. This research used an experimental research approach, and eight model are tested, including Extreme Gradient Boosting (XGBoost), random forest, Light Gradient-Boosting Machine (LightGBM), decision tree, Convolutional Neural Network (CNN), Gradient Boosting Machine (GBM), Deep Neural Network (DNN), and Multilayer Perceptron (MLP). Model performance is evaluated using accuracy, f1-score, recall, and precision. Experimental results show random forest model outperformed other models with an overall accuracy of 99.14% and recall, precision and f1-score of 99%.
References
- Keramati, A., Ghaneei, H., & Mirmohammadi, S. M. (2016). Developing a prediction model for customer churn from electronic banking services using data mining. Financial Innovation, 2, 1-13. https://doi.org/10.1186/s40854-016-0029-6
- Jamjoom, A. A. (2021). The use of knowledge extraction in predicting customer churn in B2B. Journal of Big Data, 8(1), 110. https://doi.org/10.1186/s40537-021-00500-3
- Arnaldo, M. (2003). Origins and Early Development of Banking in Ethiopia. UNIMI Economics Working Paper No. 04.2003, http://dx.doi.org/10.2139/ssrn.667265
- Prabadevi, B., Shalini, R., & Kavitha, B. R. (2023). Customer churning analysis using machine learning algorithms. International Journal of Intelligent Networks, 4, 145-154. https://doi.org/10.1016/J.IJIN.2023.05.005
- Wagh, S. K., Andhale, A. A., Wagh, K. S., Pansare, J. R., Ambadekar, S. P., & Gawande, S. H. (2024). Customer churn prediction in telecom sector using machine learning techniques. Results in Control and Optimization, 14, 100342. https://doi.org/10.1016/J.RICO.2023.100342
- Gebremeskel, K. (2013). Application of data mining techniques to predict customers’ churn at Commercial Bank of Ethiopia (Master’s thesis). Addis Ababa University, School of Graduate Studies, School of Information Science.
- Gebreegziabher, B. (2022). Bank customer churn prediction model: The case of commercial bank of Ethiopia (Doctoral dissertation, St. Mary’s University).
- Kingawa, E. D., & Hailu, T. T. (2022). Customer Churn Prediction Using Machine Learning Techniques: the case of Lion Insurance. Asian Journal of Basic Science & Research, 4(4), 60-73. https://doi.org/10.38177/ajbsr.2022.4407
- Seid, M. H., & Woldeyohannis, M. M. (2022, November). Customer churn prediction using machine learning: commercial bank of Ethiopia. In 2022 International Conference on Information and Communication Technology for Development for Africa (ICT4DA) (pp. 1-6). IEEE. https://doi.org/10.1109/ICT4DA56482.2022.9971224
- Rahman, M., & Kumar, V. (2020, November). Machine learning based customer churn prediction in banking. In 2020 4th international conference on electronics, communication and aerospace technology (ICECA) (pp. 1196-1201). IEEE. https://doi.org/10.1109/ICECA49313.2020.9297529
- IEEE Communications Society. (2008). WICOM 2008: 2008 International Conference on Wireless Communications, Networking and Mobile Computing: October 12-1, 2008, Dalian, China. IEEE.
- Dalmia, H., Nikil, C. V., & Kumar, S. (2020). Churning of bank customers using supervised learning. In Innovations in Electronics and Communication Engineering: Proceedings of the 8th ICIECE 2019 (pp. 681-691). Springer Singapore. https://doi.org/10.1007/978-981-15-3172-9_64
- He, B., Shi, Y., Wan, Q., & Zhao, X. (2014). Prediction of customer attrition of commercial banks based on SVM model. Procedia computer science, 31, 423-430. https://doi.org/10.1016/j.procs.2014.05.286
Details
| Primary Language | English |
|---|---|
| Subjects | Computer Software |
| Journal Section | Research Articles |
| Authors | |
| Early Pub Date | June 6, 2025 |
| Publication Date | |
| Submission Date | January 21, 2025 |
| Acceptance Date | April 28, 2025 |
| Published in Issue | Year 2025 Volume: 5 Issue: 1 |
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