Advertisement

Predicting Customer Churn in Electronic Banking

  • Marcin SzmydtEmail author
Conference paper
Part of the Lecture Notes in Business Information Processing book series (LNBIP, volume 339)

Abstract

The following paper is an outline of the current author’s research on the churn prediction in electronic banking. The research is based on real anonymised data of 4 million clients from one of the biggest Polish banks. Access to real data in such scale is a substantial strength of the study, as many researchers often do use only small data sample from a short period. Even though current research is still preliminary and ongoing, unlimited access to these data provides a great environment for further work. The study strongly connects with real business goals and trends in the banking industry as the author is also a practitioner. Described research focuses on methods for predicting customers who are likely to leave electronic banking. It contributes especially in further classification of an electronic churn and a broader definition of customer churn in general. Recommended solutions should contribute to the increase in the number of digital customers in the bank.

Keywords

Banking Churn prediction Electronic banking 

References

  1. 1.
    Benoit, D.F., Van Den Poel, D.: Improving customer retention in financial services using kinship network information. Expert Syst. Appl. 39(13), 11435–11442 (2012).  https://doi.org/10.1016/j.eswa.2012.04.016CrossRefGoogle Scholar
  2. 2.
    Cuesta, C., Ruesta, M., Tuesta, D., Urbiola, P.: The digital transformation of the banking industry. BBVA Research (2015). https://www.bbvaresearch.com/wp-content/uploads/2015/08/EN_Observatorio_Banca_Digital_vf3.pdf
  3. 3.
    Farquad, M.A., Ravi, V., Raju, S.B.: Churn prediction using comprehensible support vector machine: an analytical CRM application. Appl. Soft Comput. J. 19, 31–40 (2014).  https://doi.org/10.1016/j.asoc.2014.01.031CrossRefGoogle Scholar
  4. 4.
    Hevner, A., March, S., Park, J., Ram, S.: Design science in information systems research. MIS Q.: Manage. Inf. Syst. 28(1), 75–105 (2004)CrossRefGoogle Scholar
  5. 5.
    Hill, S., Provost, F., Volinsky, C.: Network-based marketing: identifying likely adopters via consumer networks. Stat. Sci. 21(2), 256–276 (2006).  https://doi.org/10.1214/088342306000000222. http://projecteuclid.org/euclid.ss/1154979826CrossRefGoogle Scholar
  6. 6.
    Keramati, A., Ghaneei, H., Mirmohammadi, S.M.: Developing a prediction model for customer churn from electronic banking services using data mining. Financ. Innov. 2(1), 10 (2016).  https://doi.org/10.1186/s40854-016-0029-6CrossRefGoogle Scholar
  7. 7.
    Liébana-Cabanillas, F., Nogueras, R., Herrera, L.J., Guillén, A.: Analysing user trust in electronic banking using data mining methods. Expert Syst. Appl. 40(14), 5439–5447 (2013)CrossRefGoogle Scholar
  8. 8.
    Lin, C.S., Tzeng, G.H., Chin, Y.C.: Combined rough set theory and ow network graph to predict customer churn in credit card accounts. Expert Syst. Appl. 38(1), 8–15 (2011).  https://doi.org/10.1016/j.eswa.2010.05.039CrossRefGoogle Scholar
  9. 9.
    Mutanen, T., Ahola, J., Nousiainen, S.: Customer churn prediction-a case study in retail banking. In: Proceedings of the ECML/PKDD Workshop on Practical Data Mining, pp. 13–19 (2006)Google Scholar
  10. 10.
    Oyeniyi, A.O., Adeyemo, A.B.: Customer churn analysis in banking sector using data mining techniques. Afr. J. Comput. ICT 8(3), 165–174 (2015)Google Scholar
  11. 11.
    Peppard, J.: Customer relationship management (CRM) in financial services. Eur. Manage. J. 18(3), 312–327 (2000)CrossRefGoogle Scholar
  12. 12.
    Popović, D., Banka, Z., Bašić, B.D.: Churn prediction model in retail banking using fuzzy C-means algorithm. Informatica 33, 243–247 (2009). http://wen.ijs.si/ojs-2.4.3/index.php/informatica/article/viewFile/242/239Google Scholar
  13. 13.
    Sumra, S.H., Manzoor, M.K., Sumra, H.H., Abbas, M.: The impact of e-banking on the profitability of banks: a study of Pakistani banks. J. Public Adm. Gov. 1(1), 31–38 (2011)Google Scholar
  14. 14.
    Webster, J., Watson, R.T.: Analyzing the past to prepare for the future: writing a literature review. MIS Q. 26(2), xiii–xxiii (2002)Google Scholar

Copyright information

© Springer Nature Switzerland AG 2019

Authors and Affiliations

  1. 1.Department of Information SystemsPoznan University of Economics and BusinessPoznanPoland

Personalised recommendations