Support vector machines for credit scoring and discovery of significant features

Tony Bellotti, Jonathan Crook

Research output: Journal PublicationArticlepeer-review

205 Citations (Scopus)

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 languageEnglish
Pages (from-to)3302-3308
Number of pages7
JournalExpert Systems with Applications
Volume36
Issue number2 PART 2
DOIs
Publication statusPublished - Mar 2009
Externally publishedYes

Keywords

  • Credit scoring
  • Feature selection
  • SVM

ASJC Scopus subject areas

  • Engineering (all)
  • Computer Science Applications
  • Artificial Intelligence

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