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An Ensemble Model for Predicting Retail Banking Churn in the Youth Segment of Customers

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  • Vijayakumar Bharathi S

    (Symbiosis Centre for Information Technology, Symbiosis International, Deemed University, Pune 411057, India)

  • Dhanya Pramod

    (Symbiosis Centre for Information Technology, Symbiosis International, Deemed University, Pune 411057, India)

  • Ramakrishnan Raman

    (Symbiosis Institute of Business Management, Pune, Symbiosis International, Deemed University, Pune 412115, India)

Abstract

(1) This study aims to predict the youth customers’ defection in retail banking. The sample comprised 602 young adult bank customers. (2) The study applied Machine learning techniques, including ensembles, to predict the possibility of churn. (3) The absence of mobile banking, zero-interest personal loans, access to ATMs, and customer care and support were critical driving factors to churn. The ExtraTreeClassifier model resulted in an accuracy rate of 92%, and an AUC of 91.88% validated the findings. (4) Customer retention is one of the critical success factors for organizations so as to enhance the business value. It is imperative for banks to predict the drivers of churn among their young adult customers so as to create and deliver proactive enable quality services.

Suggested Citation

  • Vijayakumar Bharathi S & Dhanya Pramod & Ramakrishnan Raman, 2022. "An Ensemble Model for Predicting Retail Banking Churn in the Youth Segment of Customers," Data, MDPI, vol. 7(5), pages 1-15, May.
    • Handle: RePEc:gam:jdataj:v:7:y:2022:i:5:p:61-:d:811572
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    References listed on IDEAS

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