Credit scorecard based on logistic regression with random coefficients

Research output: Journal Publications and Reviews (RGC: 21, 22, 62)21_Publication in refereed journalpeer-review

30 Scopus Citations
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Author(s)

  • Gang Dong
  • Kin Keung Lai
  • Jerome Yen

Related Research Unit(s)

Detail(s)

Original languageEnglish
Pages (from-to)2469-2478
Journal / PublicationProcedia Computer Science
Volume1
Issue number1
Publication statusPublished - 2010

Conference

Title10th International Conference on Computational Science 2010, ICCS 2010
PlaceNetherlands
CityAmsterdam
Period31 May - 2 June 2010

Link(s)

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.

Research Area(s)

  • Bayesian procedures, Credit scorecard, Logistic regression, Random coefficients

Citation Format(s)

Credit scorecard based on logistic regression with random coefficients. / Dong, Gang; Lai, Kin Keung; Yen, Jerome.

In: Procedia Computer Science, Vol. 1, No. 1, 2010, p. 2469-2478.

Research output: Journal Publications and Reviews (RGC: 21, 22, 62)21_Publication in refereed journalpeer-review