Analysing financial literacy determinants with computational intelligence models

This paper reports on the use of neural networks (NNs) and support vector machines (SVMs) to model financial literacy of youth in the Australian society with respect to their financial knowledge of Credit Cards, Loans and Pension. Sensitivity analysis is applied to determine the relative contribution of each determinant to the overall financial literacy output. The experiment which is based on a sample of youth from an Australian university shows that NNs & SVMs give promising results and capabilities for modelling financial literacy problem efficiently. The findings indicate that the main determinants of the level of credit card literacy are the student´s level of study, credit card status and daily routine. While for knowledge related to loans, the main determinants are the credit card status, gender and living status. In the case of pensions, work status, year of study, and living status have strong relevance to participants´ knowledge in this area.