Machine Learning Techniques Applied to Profile Mobile Banking Users in India

Machine Learning Techniques Applied to Profile Mobile Banking Users in India

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) and D. Veranna (Institute for Development and Research in Banking Technology (IDRBT), Hyderabad, India)
Copyright: © 2013 |Pages: 11
DOI: 10.4018/jisss.2013010105
OnDemand PDF Download:
Available
$37.50
Current Special Offers
No Current Special Offers
TOTAL SAVINGS: $37.50

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.
Article Preview
Top

Literature Review

Fundamentally variables were drawn from adoption and diffusion theoretical models. Diffusion of Innovations Theory (Rogers, 1995), Theory of Planned Behaviour (Fishbein & Azjen, 1975; Azjen, 1985), Technology Acceptance Model (Davis, 1989) and other studies related to technology adoption (Chan & Lu, 2004; Shih & Fang, 2004; Tan & Teo, 2000; Williamson, Kirsty, Lichtenstein, & Sharman, 2006; Davis, 1989; Gefen, & Straub, 2000). The variables are explained the subsequent section. Variables were also cross checked with recent studies in technology adoption studies. Table 1 illustrates the number of respondents from more recent studies (Gebauer & Shaw, 2004; Hung, Ku, & Chang, 2003; Pavlov, 2003; Eastin, 2002; Lederer, Maupin, Sena, & Zhuang, 2000).

Complete Article List

Search this Journal:
Reset
Open Access Articles: Forthcoming
Volume 13: 4 Issues (2021): 1 Released, 3 Forthcoming
Volume 12: 4 Issues (2020)
Volume 11: 4 Issues (2019)
Volume 10: 4 Issues (2018)
Volume 9: 4 Issues (2017)
Volume 8: 4 Issues (2016)
Volume 7: 4 Issues (2015)
Volume 6: 4 Issues (2014)
Volume 5: 4 Issues (2013)
Volume 4: 4 Issues (2012)
Volume 3: 4 Issues (2011)
Volume 2: 4 Issues (2010)
Volume 1: 4 Issues (2009)
View Complete Journal Contents Listing