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Application of Support Vector Machines in Financial Literacy Modelling

Financial literacy modelling is a very complicated process, which influenced by many factors such as demographics, languages, income levels, culture, age, and sex. This paper proposes a new model based on support vector machines (SVMs) to measure financial literacy of youth in the Australian society with respect to their financial knowledge of credit cards, loans and superannuation. In order to examine the feasibility of SVM, we compared it with a multi-layer back-propagation (BP) artificial neural network (ANN) model. The experiment shows that SVMs outperform the neural network model in that SVMs results show promising results and capabilities for modelling financial literacy in an efficient and robust approach. The results of training and validation have shown that the SVMs model has higher accuracy compared with the algorithm of BP ANN model. Thus SVMs can be considered as a new financial literacy modelling technique.