Abstract
Credit assessment has attracted lots of researchers in financial and banking industry. Recent studies have shown that Artificial Intelligence (AI) methods are competitive to statistical methods for credit assessment. This article applies support vector machines (SVM), a relatively new machine learning technique, to the credit assessment problem for better explanatory power. The structure of SVM has many computation advantages, such as special direction at a finite sample and irrelevance between the complexity of algorithm and the sample dimension. A real credit card data experiment shows that SVM method has outstanding assessment ability. Compared with the methods that are currently used by a major Chinese bank, the SVM method has a great potential superiority in predicting accuracy.
This research has been partially supported by National Excellent Youth Fund under 70028101, and the President Fund of Chinese Academy of Sciences (CAS) (2003).
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Li, J., Liu, J., Xu, W., Shi, Y. (2004). Support Vector Machines Approach to Credit Assessment. In: Bubak, M., van Albada, G.D., Sloot, P.M.A., Dongarra, J. (eds) Computational Science - ICCS 2004. ICCS 2004. Lecture Notes in Computer Science, vol 3039. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-540-25944-2_115
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DOI: https://doi.org/10.1007/978-3-540-25944-2_115
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