Support vector machines for credit scoring and discovery of significant features

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摘要

The assessment of risk of default on credit is important for financial institutions. Logistic regression and discriminant analysis are techniques traditionally used in credit scoring for determining likelihood to default based on consumer application and credit reference agency data. We test support vector machines against these traditional methods on a large credit card database. We find that they are competitive and can be used as the basis of a feature selection method to discover those features that are most significant in determining risk of default.

论文关键词:SVM,Credit scoring,Feature selection

论文评审过程:Available online 8 February 2008.

论文官网地址:https://doi.org/10.1016/j.eswa.2008.01.005