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

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...

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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
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