Abstract
The potential fraud problems, international economic crisis and the crisis of confidence in markets have affected financial institutions, which have tried to maintain customer trust in many different ways. To maintain the trust level in financial institutions, the implementation of electronic banking for customers has been considered a successful strategy. However, the parameters that define user trust have not been analysed in detail due to the lack of experience and the recent use of e-banking. This paper aims to determine which variables are relevant to user trust by applying machine learning techniques as multi-objective genetic algorithms for the preparation of business strategies to improve confidence and profitability. The algorithms have been tuned following the indications given by experts and their results have been validated by them, setting a level of reliability. There is also a comparison among different fitness functions used in the evolution process that are able to rank the subset of variables encoded by the individuals.
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Liébana-Cabanillas, F., Nogueras, R., Muñoz-Leiva, F., Rojas, I., Guillén, A. (2012). Applying Multi-objective Optimization for Variable Selection to Analyze User Trust in Electronic Banking. In: Casillas, J., Martínez-López, F., Corchado Rodríguez, J. (eds) Management Intelligent Systems. Advances in Intelligent Systems and Computing, vol 171. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-642-30864-2_14
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DOI: https://doi.org/10.1007/978-3-642-30864-2_14
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