A hybrid feature selection method for credit scoring

Authors

  • Sang Ha Van Academy Of Finance image/svg+xml
  • Nam Nguyen Ha VNU-University of Engineering and Technology
  • Hien Nguyen Thi Bao Academy Of Finance image/svg+xml

DOI:

https://doi.org/10.4108/eai.6-3-2017.152335

Keywords:

Credit risk, Credit scoring, Hybrid Feature selection, GBM, RFE, Information Values, Machine learning

Abstract

Reliable credit scoring models played a very important role of retail banks to evaluate credit applications and it has been widely studied. The main objective of this paper is to build a hybrid credit scoring model using feature selection approach. In this study, we constructed a credit scoring model based on parallel GBM (Gradient Boosted Model), filter and wrapper approaches to evaluate the applicant’s credit score from the input features. Feature scoring expression are combined by feature important (Gini index) and Information Value. Backward sequential scheme is used for selecting optimal subset of relevant features while the subset is evaluated by GBM classifier. To reduce the running time, we applied parallel GBM classifier to evaluate the proposed subset of features. The experimental results showed that the proposed method obtained a higher predictive accuracy than a baseline method for some certain datasets. It also showed faster speed and better generalization than traditional feature selection methods widely used in credit scoring.

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Published

06-03-2017

How to Cite

1.
Ha Van S, Nguyen Ha N, Nguyen Thi Bao H. A hybrid feature selection method for credit scoring. EAI Endorsed Trans Context Aware Syst App [Internet]. 2017 Mar. 6 [cited 2024 Nov. 21];4(11):e2. Available from: https://publications.eai.eu/index.php/casa/article/view/1969