Abstract
Credit scoring has attracted lots of research interests in the literature. The credit scoring manager often evaluates the consumer’s credit with intuitive experience. However, with the support of the credit classification model, the manager can accurately evaluate the applicant’s credit score. Support Vector Machine (SVM) classification is currently an active research area and successfully solves classification problems in many domains. This article introduces support vector machines (SVM), to the problem in attempt to provide a model with better explanatory power. We used backpropagation neural network (BNN) as a benchmark and obtained prediction accuracy around 80% for both BNN and SVM methods for the Australian and German credit datasets from UCI.
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© 2009 Springer-Verlag Berlin Heidelberg
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Zhang, L., Hui, X. (2009). Application of Support Vector Machines Method in Credit Scoring. In: Wang, H., Shen, Y., Huang, T., Zeng, Z. (eds) The Sixth International Symposium on Neural Networks (ISNN 2009). Advances in Intelligent and Soft Computing, vol 56. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-642-01216-7_30
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DOI: https://doi.org/10.1007/978-3-642-01216-7_30
Publisher Name: Springer, Berlin, Heidelberg
Print ISBN: 978-3-642-01215-0
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