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Bank Customer Churn Prediction with Machine Learning Methods
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Abstract
This paper examines and analyses customer churn prediction in the banking sector using the data from ABC Bank. The analysis conducted will document the determinants of bank customer churn and provide insights to the most important factors which influence the customers decision to quit utilizing the services of a bank. The investigation is based on the results of two machine learning algorithms with k-fold-cross-validation and same boosting methods. The result of the analysis reveals that out of logistic regression and random forests algorithms, the random forest methods show a higher accuracy score which corresponds with the literature review studied. Furthermore, the statistic of the research indicates that customer’s age has the highest association with the likelihood of customer churning, while whether the customer has a credit card at the bank has the lowest interconnection. The results of this research may provide valid explanations to customer churn in the banking sector and bring further intuitions of the advantages which machine learning methods may provide to future financial analysis.
Keywords
Bank Customer, Prediction, Machine Learning
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Cite this article
Zhu,H. (2024). Bank Customer Churn Prediction with Machine Learning Methods. Advances in Economics, Management and Political Sciences,69,23-29.
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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Volume title: Proceedings of the 2nd International Conference on Financial Technology and Business Analysis
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