Work

Credit scorecard based on logistic regression with random coefficients

Year: 2010

Type: conference-paper

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.

Source: Procedia Computer Science

DOI: 10.1016/j.procs.2010.04.278

Authors: Gang Dong, Kin Keung Lai, Jerome Yen

Institutions: City University of Hong Kong, North China Electric Power University, Tung Wah College

Language: English

Topic: Financial Distress and Bankruptcy Prediction

Subfield: Accounting

Field: Business, Management and Accounting

Domain: Social Sciences

Open Access status: diamond

Funder: City University of Hong Kong

Award: G4212088833