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Credit Scorecard Based on Logistic Regression with Random Coefficients
Gang Dong1; Kin Keung Lai2; Jerome Yen3
2010
Conference NameInternational Conference on Computational Science (ICCS)
Source PublicationICCS 2010 - INTERNATIONAL CONFERENCE ON COMPUTATIONAL SCIENCE, PROCEEDINGS
Conference DateMAY 31-JUN 02, 2010
Conference PlaceUniv Amsterdam, Amsterdam, NETHERLANDS
Abstract

Many credit scoring techniques have been used to build credit scorecards. Among them, logistic regression model is the most commonly used in the banking industry due to its desirable features (e.g., robustness and transparency). Although some new techniques (e.g., support vector machine) have been applied to credit scoring and shown superior prediction accuracy, they have problems with the results interpretability. Therefore, these advanced techniques have not been widely applied in practice. To improve the prediction accuracy of logistic regression, logistic regression with random coefficients is proposed. The proposed model can improve prediction accuracy of logistic regression without sacrificing desirable features. It is expected that the proposed credit scorecard building method can contribute to effective management of credit risk in practice.

KeywordBayesian Procedures Credit Scorecard Logistic Regression Random Coefficients
DOIhttps://doi.org/10.1016/j.procs.2010.04.278
Indexed BySSCI
Language英语
WOS Research AreaComputer Science
WOS SubjectComputer Science, Theory & Methods
WOS IDWOS:000281951600277
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Cited Times [WOS]:12   [WOS Record]     [Related Records in WOS]
Document TypeConference paper
CollectionFaculty of Business Administration
INSTITUTE OF COLLABORATIVE INNOVATION
Affiliation1.Department of Management Sciences, City University of Hong Kong, Hong Kong
2.School of Business Administration, North China ElectricPower University, China
3.Department of Finance and Economics, Tung Wah College, Hong Kong
Recommended Citation
GB/T 7714
Gang Dong,Kin Keung Lai,Jerome Yen. Credit Scorecard Based on Logistic Regression with Random Coefficients[C],2010.
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