Abstract
One of the most critical challenges facing banking institutions is customer churn, as it dramatically affects a bank’s profits and reputation. Therefore, banks use customer churn forecasting methods when selecting the necessary measures to reduce the impact of this problem. This study applied data mining techniques to predict customer churn in the banking sector using three different classification algorithms, namely: decision tree (J48), random forest (RF), and neural network (MLP) using WEKA. The Results showed that J48 had an overall superior performance over the five performance measures, compared to other algorithms using the 10-fold cross-validation. Additionally, the InfoGain and correlation features selection method was used to identify significant features to predict customer churn. The experiment revealed that both algorithms work better when all features are utilized. In short, the results obtained help in predicting which customer is likely to leave the bank. Furthermore, identifying these essential features will help banks keep customers from churning out and compete with rival banks.
This is a preview of subscription content, log in via an institution to check access.
Access this chapter
Tax calculation will be finalised at checkout
Purchases are for personal use only
Similar content being viewed by others
References
Agarwal, S.: Data mining: data mining concepts and techniques. In: 2013 International Conference on Machine Intelligence and Research Advancement, pp. 203–207 (2013). https://doi.org/10.1109/ICMIRA.2013.45
Dai, T.: International trade e-commerce based on data mining. In: Proceedings - 2014 IEEE Workshop on Advanced Research and Technology in Industry Applications, WARTIA 2014, pp. 703–705 (2014). https://doi.org/10.1109/WARTIA.2014.6976362
Bilal Zoric, A.: Predicting customer churn in banking industry using neural networks. Interdiscip. Descr. Complex Syst. 14(2), 116–124 (2016). https://doi.org/10.7906/indecs.14.2.1
Keramati, A., Ghaneei, H., Mirmohammadi, S.M.: Developing a prediction model for customer churn from electronic banking services using data mining. Financ. Innov. 2(1), 1–13 (2016). https://doi.org/10.1186/s40854-016-0029-6
Lazarov, V., Capota, M.: Churn prediction: a comparative study using KNN and decision trees. In: 2019 Sixth HCT Information Technology Trends, no. 1, pp. 182–186 (2019)
Kim, K., Lee, J.H.: Bayesian optimization of customer churn predictive model. In: Joint 10th International Conference on Soft Computing and Intelligent Systems and 19th International Symposium on Advanced Intelligent System, pp. 85–88 (2018). https://doi.org/10.1109/SCIS-ISIS.2018.00024
Ge, Y., He, S., Xiong, J., Brown, D.E.: Customer churn analysis for a software-as-a-service company. In: 2017 Systems and Information Engineering Design Symposium, SIEDS 2017, pp. 106–111 (2017). https://doi.org/10.1109/SIEDS.2017.7937698
Subramanya, K.B., Somani, A.K.: Enhanced feature mining and classifier models to predict customer churn for an E-retailer. Big Data Anal. Tools Technol. Eff. Plan., 293–309 (2017). https://doi.org/10.1201/b21822
Han, J., Kamber, M., Pei, J.: Data Mining Concepts and Techniques, 3rd edn. Morgan Kaufmann Publishers Inc., San Francisco (2011)
López-Gil, J.M., Virgili-Gomá, J., Gil, R., García, R.: Method for improving EEG based emotion recognition by combining it with synchronized biometric and eye tracking technologies in a non-invasive and low cost way. Front. Comput. Neurosci. 10 (2016). https://doi.org/10.3389/fncom.2016.00085
Pavlov, Y.L.: Random forests. In: Random Forest, pp. 1–122 (2019). https://doi.org/10.1201/9780429469275-8
What Is a Good Credit Score? - Experian. https://www.experian.com/blogs/ask-experian/credit-education/score-basics/what-is-a-good-credit-score/. Accessed 08 Jan 2021
Daw, S., Basak, R.: Machine learning applications using Waikato environment for knowledge analysis. In: Proceedings of the 4th Fourth International Conference on Computing Methodologies and Communication ICCMC 2020, pp. 346–351, February 2020. https://doi.org/10.1109/ICCMC48092.2020.ICCMC-00065
Tufi, D.: Proceedings of the 10th International Conference ‘Linguistic Resources and Tools for Processing the Romanian Language’ 18–19 September 2014, September 2014
Author information
Authors and Affiliations
Corresponding author
Editor information
Editors and Affiliations
Rights and permissions
Copyright information
© 2021 IFIP International Federation for Information Processing
About this paper
Cite this paper
Alsubaie, W.A., Albishi, H.Z., Aljoufi, K.A., Alghamdi, W.S., Alyahyan, E.A. (2021). Predicting Customer Churn in Banking Based on Data Mining Techniques. In: Krishnamurthy, V., Jaganathan, S., Rajaram, K., Shunmuganathan, S. (eds) Computational Intelligence in Data Science. ICCIDS 2021. IFIP Advances in Information and Communication Technology, vol 611. Springer, Cham. https://doi.org/10.1007/978-3-030-92600-7_3
Download citation
DOI: https://doi.org/10.1007/978-3-030-92600-7_3
Published:
Publisher Name: Springer, Cham
Print ISBN: 978-3-030-92599-4
Online ISBN: 978-3-030-92600-7
eBook Packages: Computer ScienceComputer Science (R0)
