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International Conference on Computational Science and Its Applications

ICCSA 2020: Computational Science and Its Applications – ICCSA 2020 pp 576–589Cite as

Machine Learning for Customer Churn Prediction in Retail Banking

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Part of the book series: Lecture Notes in Computer Science ((LNTCS,volume 12251))

Abstract

Predicting in advance whether a given customer will end his relationship with a company has an undeniable added value for all organizations, since targeted campaigns can be prepared to promote customer retention. In this work, six different methods using machine learning have been investigated on the retail banking customer churn prediction problem, considering predictions up to 6 months in advance. Different approaches are tested and compared using real data. Out of sample results are very good, even with very challenging out-of-sample sets composed only of churners, that truly test the ability to predict when a customer will churn. The best results are obtained by stochastic boosting, and the most important variables for predicting churn in a 1–2 months horizon are the total value of bank products held in recent months and the existence of debit or credit cards in another bank. For a 3–4 months horizon, the number of transactions in recent months and the existence of a mortgage loan outside the bank are the most important variables.

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Acknowledgments

This study has been funded by national funds, through FCT, Portuguese Science Foundation, under project UIDB/00308/2020.

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Authors and Affiliations

  1. INESC-Coimbra, University of Coimbra, Coimbra, Portugal

    Joana Dias

  2. CeBER, Faculty of Economics, University of Coimbra, Coimbra, Portugal

    Joana Dias, Pedro Godinho & Pedro Torres

Authors
  1. Joana Dias
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  2. Pedro Godinho
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  3. Pedro Torres
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Corresponding author

Correspondence to Pedro Godinho .

Editor information

Editors and Affiliations

  1. University of Perugia, Perugia, Italy

    Osvaldo Gervasi

  2. University of Basilicata, Potenza, Potenza, Italy

    Beniamino Murgante

  3. Chair- Center of ICT/ICE, Covenant University, Ota, Nigeria

    Sanjay Misra

  4. University of Cagliari, Cagliari, Italy

    Chiara Garau

  5. University of Cagliari, Cagliari, Italy

    Ivan Blečić

  6. Clayton School of Information Technology, Monash University, Clayton, VIC, Australia

    David Taniar

  7. Department of Information Science, Kyushu Sangyo University, Fukuoka, Japan

    Bernady O. Apduhan

  8. University of Minho, Braga, Portugal

    Ana Maria A. C. Rocha

  9. Polytechnic University of Bari, Bari, Italy

    Eufemia Tarantino

  10. Polytechnic University of Bari, Bari, Italy

    Carmelo Maria Torre

  11. Department of Neurology, University of Massachusetts Medical School, Worcester, MA, USA

    Yeliz Karaca

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Dias, J., Godinho, P., Torres, P. (2020). Machine Learning for Customer Churn Prediction in Retail Banking. In: Gervasi, O., et al. Computational Science and Its Applications – ICCSA 2020. ICCSA 2020. Lecture Notes in Computer Science(), vol 12251. Springer, Cham. https://doi.org/10.1007/978-3-030-58808-3_42

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  • DOI: https://doi.org/10.1007/978-3-030-58808-3_42

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  • Publisher Name: Springer, Cham

  • Print ISBN: 978-3-030-58807-6

  • Online ISBN: 978-3-030-58808-3

  • eBook Packages: Computer ScienceComputer Science (R0)

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