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Research on Early Warning of Customer Churn Based on Random Forest

Zizhen Qin, Yuxin Liu, Tianze Zhang*

Hunan University of Finance and Economics, Changsha, 410205, China

* Corresponding Author: Tianze Zhang. Email:

Journal on Artificial Intelligence 2022, 4(3), 143-154.


With the rapid development of interest rate market and big data, the banking industry has shown the obvious phenomenon of “two or eight law”, 20% of the high quality customers occupy most of the bank’s assets, how to prevent the loss of bank credit card customers has become a growing concern for banks. Therefore, it is particularly important to establish a customer churn early warning model. In this paper, we will use the random forest method to establish a customer churn early warning model, focusing on the churn of bank credit card customers and predicting the possibility of future churn of customers. Due to the large data size of banks, the complexity of their customer base, and the diversity of user characteristics, it is not easy for banks to accurately predict churned customers, and there are few customer churn early warning studies suitable for banks. Compared with the traditional bank credit risk prediction algorithm, this method is proved to be useful in the early stage of churn warning, and has the advantages of high prediction accuracy, large amount of processed data, and good model interpretability, which can help to retain valuable customers in advance and thus achieve the purpose of reducing cost and increasing efficiency.


Cite This Article

Z. Qin, Y. Liu and T. Zhang, "Research on early warning of customer churn based on random forest," Journal on Artificial Intelligence, vol. 4, no.3, pp. 143–154, 2022.

This work is licensed under a Creative Commons Attribution 4.0 International License , which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.
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