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Machine Learning Techniques Applied to Profile Mobile Banking Users in India

Author

Listed:
  • M. Carr

    (Institute for Development and Research in Banking Technology (IDRBT), Hyderabad, India)

  • V. Ravi

    (Institute for Development and Research in Banking Technology (IDRBT), Hyderabad, India)

  • G. Sridharan Reddy

    (Institute for Development and Research in Banking Technology (IDRBT), Hyderabad, India)

  • D. Veranna

    (Institute for Development and Research in Banking Technology (IDRBT), Hyderabad, India)

Abstract

This paper profiles mobile banking users using machine learning techniques viz. Decision Tree, Logistic Regression, Multilayer Perceptron, and SVM to test a research model with fourteen independent variables and a dependent variable (adoption). A survey was conducted and the results were analysed using these techniques. Using Decision Trees the profile of the mobile banking adopter’s profile was identified. Comparing different machine learning techniques it was found that Decision Trees outperformed the Logistic Regression and Multilayer Perceptron and SVM. Out of all the techniques, Decision Tree is recommended for profiling studies because apart from obtaining high accurate results, it also yields ‘if–then’ classification rules. The classification rules provided here can be used to target potential customers to adopt mobile banking by offering them appropriate incentives.

Suggested Citation

  • M. Carr & V. Ravi & G. Sridharan Reddy & D. Veranna, 2013. "Machine Learning Techniques Applied to Profile Mobile Banking Users in India," International Journal of Information Systems in the Service Sector (IJISSS), IGI Global, vol. 5(1), pages 82-92, January.
    • Handle: RePEc:igg:jisss0:v:5:y:2013:i:1:p:82-92
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    Cited by:

    1. Hossein Hassani & Xu Huang & Emmanuel Silva & Mansi Ghodsi, 2020. "Deep Learning and Implementations in Banking," Annals of Data Science, Springer, vol. 7(3), pages 433-446, September.

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