Credit scoring models are usually formulated by fitting the probability of loan default as a function of individual evaluation attributes. Typically, these attributes are measured using a Likert-type scale, but are treated as interval scale explanatory variables to predict loan defaults. Existing models also do not distinguish between types of default, although they vary: default by an insolvent company and default by an insolvent debtor. This practice can bias the results. In this paper, we applied Quantification Method II, a categorical version of canonical correlation analysis, to determine the relationship between two sets of categorical variables: a set of default types and a set of evaluation attributes. We distinguished between two types of loan default patterns based on quantification scores. In the first set of quantification scores, we found knowledge management, new technology development, and venture registration as important predictors of default from non-default status. Based on the second quantification score, we found that the technology and profitability factors influence loan defaults due to an insolvent company. Finally, we proposed a credit-risk rating model based on the quantification score.
Bibliographical noteFunding Information:
This work was supported by the National Research Foundation of Korea (NRF) grant funded by the Korea government (MSIP) (2016R1A2A1A05005270).
© 2017 by the authors.
All Science Journal Classification (ASJC) codes
- Computer Science (miscellaneous)
- Environmental Science (miscellaneous)
- Geography, Planning and Development
- Energy Engineering and Power Technology
- Hardware and Architecture
- Management, Monitoring, Policy and Law
- Computer Networks and Communications
- Renewable Energy, Sustainability and the Environment