Title: On the build and application of bank customer churn warning model

Authors: Wangdong Jiang; Yushan Luo; Ying Cao; Guang Sun; Chunhong Gong

Addresses: Institute of Big Data, Hunan University of Finance and Economics, Changsha, Hunan, China ' School of Information Management and Technology, Institute of Big Data, Hunan University of Finance and Economics, Changsha, Hunan, China ' Housheng School of International Education, Hunan University of Finance and Economics, Changsha, Hunan, China ' School of Information Management and Technology, Institute of Big Data, Hunan University of Finance and Economics, Changsha, Hunan, China ' School of Information Management and Technology, Hunan University of Finance and Economics, Changsha, Hunan, China

Abstract: In view of the customer churn problem faced by banks, this paper will use the Python language to clean and select the original dataset based on real bank customer data, and gradually condense the 626 customer features in the original dataset to 77 customer features. Then, based on the pre-processed bank data, this paper uses logistic regression, decision tree and neural network to establish three bank customer churn warning models and compares them. The results show that the accuracy of the three models in predicting bank loss customers is above 92%. Finally, based on the logistic regression model with better evaluation results, this paper analyses the characteristics of the lost customers for the bank, and gives the bank management suggestions for the lost customers.

Keywords: bank customer; churn warning model; logistic regression; customer churn.

DOI: 10.1504/IJCSE.2020.109400

International Journal of Computational Science and Engineering, 2020 Vol.22 No.4, pp.404 - 419

Received: 27 Jul 2019
Accepted: 02 Sep 2019

Published online: 26 Aug 2020 *

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