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Pattern recognition for evaluator errors in a credit scoring model for technology-based SMEs

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  • S Y Sohn

    (Yonsei University, Seoul, Republic of Korea)

  • M K Doo

    (Yonsei University, Seoul, Republic of Korea)

  • Y H Ju

    (Yonsei University, Seoul, Republic of Korea)

Abstract

A credit scoring model for technology-based small and medium enterprises presupposes evaluator objectivity and evaluation consistency; however, there is always some amount of error in any technology evaluation. This can be due in part to the subjective evaluation attributes that comprise part of the credit scoring model. The evaluated values of subjective attributes can vary among evaluators. In this study, we identified the significant characteristics of both evaluator and evaluation teams in terms of evaluation error using a decision tree analysis. Our results can improve the accuracy of a wide range of evaluation procedures for technology financing.

Suggested Citation

  • S Y Sohn & M K Doo & Y H Ju, 2012. "Pattern recognition for evaluator errors in a credit scoring model for technology-based SMEs," Journal of the Operational Research Society, Palgrave Macmillan;The OR Society, vol. 63(8), pages 1051-1064, August.
    • Handle: RePEc:pal:jorsoc:v:63:y:2012:i:8:p:1051-1064
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    Citations

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    Cited by:

    1. Jong Wook Lee & So Young Sohn, 2021. "Evaluating borrowers’ default risk with a spatial probit model reflecting the distance in their relational network," PLOS ONE, Public Library of Science, vol. 16(12), pages 1-11, December.
    2. Ju, Yong Han & Sohn, So Young, 2014. "Updating a credit-scoring model based on new attributes without realization of actual data," European Journal of Operational Research, Elsevier, vol. 234(1), pages 119-126.
    3. Bo Kyeong Lee & So Young Sohn, 2017. "A Credit Scoring Model for SMEs Based on Accounting Ethics," Sustainability, MDPI, vol. 9(9), pages 1-15, September.
    4. Janne Harkonen & Harri Haapasalo & Kai Hanninen, 2013. "Productisation: A Literature Review," Diversity, Technology, and Innovation for Operational Competitiveness: Proceedings of the 2013 International Conference on Technology Innovation and Industrial Management,, ToKnowPress.
    5. Harkonen, Janne & Haapasalo, Harri & Hanninen, Kai, 2015. "Productisation: A review and research agenda," International Journal of Production Economics, Elsevier, vol. 164(C), pages 65-82.
    6. Yonghan Ju & So Young Sohn, 2017. "Technology Credit Scoring Based on a Quantification Method," Sustainability, MDPI, vol. 9(6), pages 1-16, June.
    7. Francesco Ciampi & Alessandro Giannozzi & Giacomo Marzi & Edward I. Altman, 2021. "Rethinking SME default prediction: a systematic literature review and future perspectives," Scientometrics, Springer;Akadémiai Kiadó, vol. 126(3), pages 2141-2188, March.

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