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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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    1. Volker Liermann & Sangmeng Li, 2021. "Methods of Machine Learning," Springer Books, in: Volker Liermann & Claus Stegmann (ed.), The Digital Journey of Banking and Insurance, Volume III, pages 225-238, Springer.
    2. Amankwah-Amoah, Joseph & Khan, Zaheer & Wood, Geoffrey & Knight, Gary, 2021. "COVID-19 and digitalization: The great acceleration," Journal of Business Research, Elsevier, vol. 136(C), pages 602-611.
    3. Libai, Barak & Bart, Yakov & Gensler, Sonja & Hofacker, Charles F. & Kaplan, Andreas & Kötterheinrich, Kim & Kroll, Eike Benjamin, 2020. "Brave New World? On AI and the Management of Customer Relationships," Journal of Interactive Marketing, Elsevier, vol. 51(C), pages 44-56.
    4. Rughoo, Aarti & Sarantis, Nicholas, 2014. "The global financial crisis and integration in European retail banking," Journal of Banking & Finance, Elsevier, vol. 40(C), pages 28-41.
    5. Leonardo José Silveira & Plácido Rogério Pinheiro & Leopoldo Soares de Melo Junior, 2021. "A Novel Model Structured on Predictive Churn Methods in a Banking Organization," JRFM, MDPI, vol. 14(10), pages 1-24, October.
    6. Daniel Broby, 2021. "Financial technology and the future of banking," Financial Innovation, Springer;Southwestern University of Finance and Economics, vol. 7(1), pages 1-19, December.
    7. Sourav Bikash Borah & Srinivas Prakhya & Amalesh Sharma, 2020. "Leveraging service recovery strategies to reduce customer churn in an emerging market," Journal of the Academy of Marketing Science, Springer, vol. 48(5), pages 848-868, September.
    8. Carl Dahlman & Sam Mealy & Martin Wermelinger, 2016. "Harnessing the digital economy for developing countries," OECD Development Centre Working Papers 334, OECD Publishing.
    9. Van den Poel, Dirk & Lariviere, Bart, 2004. "Customer attrition analysis for financial services using proportional hazard models," European Journal of Operational Research, Elsevier, vol. 157(1), pages 196-217, August.
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