Open Access

ARTICLE

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. https://doi.org/10.32604/jai.2022.031843

Received 28 April 2022; Accepted 29 July 2022; Issue published 01 December 2022

Abstract

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.

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Keywords

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