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Abstract
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.
Original language | English |
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Pages (from-to) | 3302-3308 |
Number of pages | 7 |
Journal | Expert Systems with Applications |
Volume | 36 |
Early online date | 8 Feb 2008 |
DOIs | |
Publication status | Published - Mar 2009 |
Keywords / Materials (for Non-textual outputs)
- SVM; Credit scoring; Feature selection
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