Original Research

Modelling the predictive performance of credit scoring

Shi-Wei Shen, Tri-Dung Nguyen, Udechukwu Ojiako
Acta Commercii | Vol 13, No 1 | a189 | DOI: https://doi.org/10.4102/ac.v13i1.189 | © 2013 Shi-Wei Shen, Tri-Dung Nguyen, Udechukwu Ojiako | This work is licensed under CC Attribution 4.0
Submitted: 28 March 2013 | Published: 16 July 2013

About the author(s)

Shi-Wei Shen, The Management School, University of Southampton, United Kingdom
Tri-Dung Nguyen, The Management School, University of Southampton, United Kingdom
Udechukwu Ojiako, Faculty of Management, University of Johannesburg, South Africa


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Abstract

Orientation: The article discussed the importance of rigour in credit risk assessment.

Research purpose: The purpose of this empirical paper was to examine the predictive performance of credit scoring systems in Taiwan.

Motivation for the study: Corporate lending remains a major business line for financial institutions. However, in light of the recent global financial crises, it has become extremely important for financial institutions to implement rigorous means of assessing clients seeking access to credit facilities.

Research design, approach and method: Using a data sample of 10 349 observations drawn between 1992 and 2010, logistic regression models were utilised to examine the predictive performance of credit scoring systems.

Main findings: A test of Goodness of fit demonstrated that credit scoring models that incorporated the Taiwan Corporate Credit Risk Index (TCRI), micro- and also macroeconomic variables possessed greater predictive power. This suggests that macroeconomic variables do have explanatory power for default credit risk.

Practical/managerial implications: The originality in the study was that three models were developed to predict corporate firms’ defaults based on different microeconomic and macroeconomic factors such as the TCRI, asset growth rates, stock index and gross domestic product.

Contribution/value-add: The study utilises different goodness of fits and receiver operator characteristics during the examination of the robustness of the predictive power of these factors.


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