Proceedings of the 3rd International Conference on Bigdata Blockchain and Economy Management, ICBBEM 2024, March 29–31, 2024, Wuhan, China

Research Article

Bank Customer Loss Forecast Analysis

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  • @INPROCEEDINGS{10.4108/eai.29-3-2024.2347318,
        author={Shengqian  Zhou},
        title={Bank Customer Loss Forecast Analysis},
        proceedings={Proceedings of the 3rd International Conference on Bigdata Blockchain and Economy Management, ICBBEM 2024, March 29--31, 2024, Wuhan, China},
        publisher={EAI},
        proceedings_a={ICBBEM},
        year={2024},
        month={6},
        keywords={bank customer loss cross table factor analysis binary logistic regression},
        doi={10.4108/eai.29-3-2024.2347318}
    }
    
  • Shengqian Zhou
    Year: 2024
    Bank Customer Loss Forecast Analysis
    ICBBEM
    EAI
    DOI: 10.4108/eai.29-3-2024.2347318
Shengqian Zhou1,*
  • 1: University of Sydney
*Contact email: shengqianzhou1225@gmail.com

Abstract

With the development of banks, the competition is increasing among banks, and the loss of customers is a serious problem, which affects the profitability of banks. Therefore, it is necessary to judge the reasons for customer loss through a series of indicators. In order to explore which factors are related to customer churn, this paper uses Kaggle’s open data set and the methods of contrastive analysis, factor analysis and binary logistic regression. Through comparative analysis, it is found that age, customer activity, the number of products owned by the customer in ABC Bank, the credit score of the customer and the account balance has an impact on the loss of customers. The accuracy of prediction by logistic regression is 67.3% and 68.5% respectively.