Credit scoring with a data mining approach based on support vector machines

Cheng Lung Huang*, Mu-Chen Chen, Chieh Jen Wang

*Corresponding author for this work

Research output: Contribution to journalArticlepeer-review

478 Scopus citations


The credit card industry has been growing rapidly recently, and thus huge numbers of consumers' credit data are collected by the credit department of the bank. The credit scoring manager often evaluates the consumer's credit with intuitive experience. However, with the support of the credit classification model, the manager can accurately evaluate the applicant's credit score. Support Vector Machine (SVM) classification is currently an active research area and successfully solves classification problems in many domains. This study used three strategies to construct the hybrid SVM-based credit scoring models to evaluate the applicant's credit score from the applicant's input features. Two credit datasets in UCI database are selected as the experimental data to demonstrate the accuracy of the SVM classifier. Compared with neural networks, genetic programming, and decision tree classifiers, the SVM classifier achieved an identical classificatory accuracy with relatively few input features. Additionally, combining genetic algorithms with SVM classifier, the proposed hybrid GA-SVM strategy can simultaneously perform feature selection task and model parameters optimization. Experimental results show that SVM is a promising addition to the existing data mining methods.

Original languageEnglish
Pages (from-to)847-856
Number of pages10
JournalExpert Systems with Applications
Issue number4
StatePublished - 1 Nov 2007


  • Classification
  • Credit scoring
  • Data mining
  • Decision tree
  • Genetic programming
  • Neural networks
  • Support vector machine

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