Technology credit scoring based on a quantification method

Yonghan Ju, So Young Sohn

Research output: Contribution to journalArticle

5 Citations (Scopus)

Abstract

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.

Original languageEnglish
Article number1057
JournalSustainability (Switzerland)
Volume9
Issue number6
DOIs
Publication statusPublished - 2017 Jun 18

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quantification
credit
loan
technological development
Knowledge management
profitability
Industry
Profitability
evaluation
knowledge management
new technology
rating
attribute
method
trend

All Science Journal Classification (ASJC) codes

  • Geography, Planning and Development
  • Renewable Energy, Sustainability and the Environment
  • Management, Monitoring, Policy and Law

Cite this

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abstract = "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.",
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Technology credit scoring based on a quantification method. / Ju, Yonghan; Sohn, So Young.

In: Sustainability (Switzerland), Vol. 9, No. 6, 1057, 18.06.2017.

Research output: Contribution to journalArticle

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