Bank Customer Churn Prediction Based on Stacking Model

Research Article
Open access

Bank Customer Churn Prediction Based on Stacking Model

Ruixuan Li 1*
  • 1 Institute of Shenzhen Audencia Financial Technology, Shenzhen University, Shenzhen, China    
  • *corresponding author liruixuan.li@audencia.com
AEMPS Vol.185
ISSN (Print): 2754-1177
ISSN (Online): 2754-1169
ISBN (Print): 978-1-80590-141-9
ISBN (Online): 978-1-80590-142-6

Abstract

With the increasingly fierce competition in the financial industry, customer churn prediction has become a key research topic. Accurate prediction of which customers are more likely to churn can help banks take timely retention measures to reduce business losses. This paper adopts a data-driven approach and uses the public bank customer churn dataset to deeply analyze the distribution of data characteristics and deal with the problem of data imbalance, and proposes a customer churn prediction method based on stacked ensemble model. In this study, random forest, XGBoost, CatBoost and LightGBM were used as the basic model, and XGBoost was used as the meta-learner to establish a two-layer stacked ensemble framework. Compared with the traditional single model and simple ensemble methods, the experimental results show that the proposed method is significantly ahead in Accuracy, Recall, AUC, F1-score and other indicators, which verifies its advanced and precise capabilities in customer churn prediction.

Keywords:

Churn prediction, Stacking model, XGBoost, Data analytics, Imbalanced data

Li,R. (2025). Bank Customer Churn Prediction Based on Stacking Model. Advances in Economics, Management and Political Sciences,185,42-51.
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References

[1]. Xu, X., & Xia, Y. (2021). Research on customer churn prediction model based on probability calibration. Statistics and Applications, 10(4), 634–641.

[2]. Sahin, E. K. (2020). Assessing the predictive capability of ensemble tree methods for landslide susceptibility mapping using XGBoost, gradient boosting machine, and random forest. SN Applied Sciences, 2(7), 1308.

[3]. Odegua, R. (2019, March). An empirical study of ensemble techniques (bagging, boosting and stacking). In Proc. conf.: deep learn. indabaXAt.

[4]. Amin, A., Anwar, S., Adnan, A., Nawaz, M., Howard, N., Qadir, J., ... & Hussain, A. (2016). Comparing oversampling techniques to handle the class imbalance problem: A customer churn prediction case study. Ieee Access, 4, 7940-7957.

[5]. Wang, Z. H. E., Wu, C., Zheng, K., Niu, X., & Wang, X. (2019). SMOTETomek-based resampling for personality recognition. IEEE access, 7, 129678-129689.

[6]. Nguyen, C., Wang, Y., & Nguyen, H. N. (2013). Random forest classifier combined with feature selection for breast cancer diagnosis and prognostic.

[7]. Aliyev, A., & Hasanova, R. (2024). Improving churn prediction in the banking sector using stacked generalization. Communications in Applied Information Technology, 12(1), 1–12.

[8]. Li, S., & Shen, Z. (2024). Explainable customer churn prediction model based on deep learning. In Proceedings of the 3rd Asia Conference on Algorithms, Computing and Machine Learning (CACML 2024) (pp. 282–287). Association for Computing Machinery.

Cite this article

Li,R. (2025). Bank Customer Churn Prediction Based on Stacking Model. Advances in Economics, Management and Political Sciences,185,42-51.

Data availability

The datasets used and/or analyzed during the current study will be available from the authors upon reasonable request.

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About volume

Volume title: Proceedings of ICEMGD 2025 Symposium: Innovating in Management and Economic Development

ISBN：978-1-80590-141-9(Print) / 978-1-80590-142-6(Online)
Editor：Florian Marcel Nuţă Nuţă, Ahsan Ali Ashraf
Conference website: https://2025.icemgd.org/Lahore.html
Conference date: 23 September 2025
Series: Advances in Economics, Management and Political Sciences
Volume number: Vol.185
ISSN：2754-1169(Print) / 2754-1177(Online)

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References

[1]. Xu, X., & Xia, Y. (2021). Research on customer churn prediction model based on probability calibration. Statistics and Applications, 10(4), 634–641.

[2]. Sahin, E. K. (2020). Assessing the predictive capability of ensemble tree methods for landslide susceptibility mapping using XGBoost, gradient boosting machine, and random forest. SN Applied Sciences, 2(7), 1308.

[3]. Odegua, R. (2019, March). An empirical study of ensemble techniques (bagging, boosting and stacking). In Proc. conf.: deep learn. indabaXAt.

[4]. Amin, A., Anwar, S., Adnan, A., Nawaz, M., Howard, N., Qadir, J., ... & Hussain, A. (2016). Comparing oversampling techniques to handle the class imbalance problem: A customer churn prediction case study. Ieee Access, 4, 7940-7957.

[5]. Wang, Z. H. E., Wu, C., Zheng, K., Niu, X., & Wang, X. (2019). SMOTETomek-based resampling for personality recognition. IEEE access, 7, 129678-129689.

[6]. Nguyen, C., Wang, Y., & Nguyen, H. N. (2013). Random forest classifier combined with feature selection for breast cancer diagnosis and prognostic.

[7]. Aliyev, A., & Hasanova, R. (2024). Improving churn prediction in the banking sector using stacked generalization. Communications in Applied Information Technology, 12(1), 1–12.

[8]. Li, S., & Shen, Z. (2024). Explainable customer churn prediction model based on deep learning. In Proceedings of the 3rd Asia Conference on Algorithms, Computing and Machine Learning (CACML 2024) (pp. 282–287). Association for Computing Machinery.

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