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

Vijayakumar Bharathi S, Dhanya Pramod and Ramakrishnan Raman
Additional contact information
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

Data, 2022, vol. 7, issue 5, 1-15

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.

Keywords: retail banking; customer churn; machine learning; young adults; ensemble model; digital (search for similar items in EconPapers)
JEL-codes: C8 C80 C81 C82 C83 (search for similar items in EconPapers)
Date: 2022
References: View references in EconPapers View complete reference list from CitEc
Citations: View citations in EconPapers (1)

Downloads: (external link)
https://www.mdpi.com/2306-5729/7/5/61/pdf (application/pdf)
https://www.mdpi.com/2306-5729/7/5/61/ (text/html)

Related works:
This item may be available elsewhere in EconPapers: Search for items with the same title.

Export reference: BibTeX RIS (EndNote, ProCite, RefMan) HTML/Text

Persistent link: https://EconPapers.repec.org/RePEc:gam:jdataj:v:7:y:2022:i:5:p:61-:d:811572

Access Statistics for this article

Data is currently edited by Ms. Cecilia Yang

More articles in Data from MDPI
Bibliographic data for series maintained by MDPI Indexing Manager ().