Research on Integrated Learning Algorithm Model of Bank Customer Churn Prediction

 Research on Integrated Learning Algorithm Model of Bank Customer Churn Prediction

Shang Xinping and Wang Yi, Dongguan City University, China

Abstract

With the rapid growth of Internet finance, competition within the banking industry has intensified significantly. To better understand customer needs and enhance customer loyalty, it has become crucial to develop a customer churn prediction model. Such a model enables banks to identify customers at risk of leaving, support data-driven business decisions, and implement strategies to retain valuable clients, thereby safeguarding the bank's interests. In this context, this paper presents a customer churn prediction model based on an ensemble learning algorithm. Experimental results demonstrate that the model effectively predicts and analyzes potential customer churn, providing valuable insights for retention efforts.


Keywords

customer churn; data preprocessing; XGBoost.


Abstract URL: https://aircconline.com/abstract/ijdms/v16n5/16524ijdms01.html

Full Article: https://aircconline.com/ijdms/V16N5/16524ijdms01.pdf

http://airccse.org/journal/ijdms/index.html

#customerchurn #datapreprocessing #xgboost



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