CREDIT SCORING MODELS WITH AUC MAXIMIZATION BASED ON WEIGHTED SVM

Ligang Zhou (), Kin Keung Lai () and Jerome Yen
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Ligang Zhou: Department of Management Sciences, City University of Hong Kong, Kowloon Tong, Hong Kong
Kin Keung Lai: Department of Management Sciences, City University of Hong Kong, Kowloon Tong, Hong Kong
Jerome Yen: Department of Finance, Hong Kong University of Science and Technology, Hong Kong

International Journal of Information Technology & Decision Making (IJITDM), 2009, vol. 08, issue 04, 677-696

Abstract: Credit scoring models are very important tools for financial institutions to make credit granting decisions. In the last few decades, many quantitative methods have been used for the development of credit scoring models with focus on maximizing classification accuracy. This paper proposes the credit scoring models with the area under receiver operating characteristics curve (AUC) maximization based on the new emerged support vector machines (SVM) techniques. Three main SVM models with different features weighted strategies are discussed. The weighted SVM credit scoring models are tested using 10-fold cross validation with two real world data sets and the experimental results are compared with other six traditional methods including linear regression, logistic regression,knearest neighbor, decision tree, and neural network. Results demonstrate that weighted 2-norm SVM with radial basis function (RBF) kernel function andt-test feature weighting strategy has the overall better performance with very narrow margin than other SVM models. However, it also consumes more computational time. In considering the balance of performance and time, least squares support vector machines (LSSVM) with RBF kernel maybe a better choice for large scale credit scoring applications.

Keywords: Credit scoring; AUC; SVM; features weighting (search for similar items in EconPapers)
Date: 2009
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Citations: View citations in EconPapers (9)

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DOI: 10.1142/S0219622009003582

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