Ensemble-based customer churn prediction in banking: a voting classifier approach for improved client retention using demographic and behavioral data

Abstract Customer turnover is a crucial issue in banking since maintained profitability depends on keeping clients. This work aims to categorize consumer turnover in banks by using a new ensemble approach combining many machine learning methods, hence enhancing churn prediction models. Using a compr...

Full description

Saved in:

Bibliographic Details
Main Authors:	Ruchika Bhuria, Sheifali Gupta, Upinder Kaur, Salil Bharany, Ateeq Ur Rehman, Seada Hussen, Ghanshyam G. Tejani, Pradeep Jangir
Format:	Article
Language:	English
Published:	Springer 2025-01-01
Series:	Discover Sustainability
Subjects:	Machine learning models Ensemble methods Explainability Challenges in statistical methods Insights into customer behavior Resilience
Online Access:	https://doi.org/10.1007/s43621-025-00807-8
Tags:	Add Tag No Tags, Be the first to tag this record!

Be the first to leave a comment!

Ensemble-based customer churn prediction in banking: a voting classifier approach for improved client retention using demographic and behavioral data

Similar Items