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On the Use of Intelligent Systems for the Modelling of Financial Literacy Parameters

On the Use of Intelligent Systems for the Modelling of Financial Literacy Parameters

H. Tawfik, R. Huang, M. Samy, A.K. Nagar
Copyright: © 2009 |Volume: 2 |Issue: 4 |Pages: 19
ISSN: 1938-7857|EISSN: 1938-7865|ISSN: 1938-7857|EISBN13: 9781616920722|EISSN: 1938-7865|DOI: 10.4018/jitr.2009062902
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MLA

Tawfik, H., et al. "On the Use of Intelligent Systems for the Modelling of Financial Literacy Parameters." JITR vol.2, no.4 2009: pp.17-35. http://doi.org/10.4018/jitr.2009062902

APA

Tawfik, H., Huang, R., Samy, M., & Nagar, A. (2009). On the Use of Intelligent Systems for the Modelling of Financial Literacy Parameters. Journal of Information Technology Research (JITR), 2(4), 17-35. http://doi.org/10.4018/jitr.2009062902

Chicago

Tawfik, H., et al. "On the Use of Intelligent Systems for the Modelling of Financial Literacy Parameters," Journal of Information Technology Research (JITR) 2, no.4: 17-35. http://doi.org/10.4018/jitr.2009062902

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Abstract

Research has shown that more young people lack good financial literacy and make poor financial decisions. Financial literacy is not only important for individuals, but also for families, financial institutions, and the entire economy. In this paper, artificial neural networks (ANNs) and support vector machines (SVMs) are used as tools to model the financial literacy levels of young university students across Australia and three Western European countries. The goal was to ascertain the students’ level of financial knowledge in relation to the use of credit card and loan facilities based on a number of input parameters such as age, gender and educational level. Sensitivity analysis is applied to determine the relative contribution of each input parameter to the overall financial literacy model. The experiments show that ANNs and SVMs exhibit promising results and capabilities for effectively modeling financial literacy. Our findings indicate that the main determinants of young people’s level of financial literacy include educational level, length of employment, age, and credit card status – in terms of the use of credit card facilities, and gender, living status and credit card status – in terms of the use of loan facilities.

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