Support vector machines approach to credit assessment
Li, JP; Liu, JL; Xu, WX; Shi, Y
AbstractCredit 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.
KeywordCredit Assessment Classification Support Vector Machines
Subject AreaComputer Science, Interdisciplinary Applications ; Computer Science, Theory & Methods
Indexed BySCI
WOS IDWOS:000223079700115
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Cited Times:13[WOS]   [WOS Record]     [Related Records in WOS]
Document Type期刊论文
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GB/T 7714
Li, JP,Liu, JL,Xu, WX,et al. Support vector machines approach to credit assessment[J]. COMPUTATIONAL SCIENCE - ICCS 2004, PROCEEDINGS,2004,3039:8,892-899.
APA Li, JP,Liu, JL,Xu, WX,&Shi, Y.(2004).Support vector machines approach to credit assessment.COMPUTATIONAL SCIENCE - ICCS 2004, PROCEEDINGS,3039,8,892-899.
MLA Li, JP,et al."Support vector machines approach to credit assessment".COMPUTATIONAL SCIENCE - ICCS 2004, PROCEEDINGS 3039(2004):8,892-899.
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