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Behavioral credit scoring model for technology-based firms that considers uncertain financial ratios obtained from relationship banking

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  • So Sohn
  • Yoon Kim

Abstract

The Korea government offers technology credit guarantee service to many technology-based small and medium enterprises (SMEs) suffering from funding problems. Many advanced application credit scoring models have been developed based on technology to reduce the high default rates of this service. However, a credit scoring model which can reflect changes in firms after a loan has been granted has not yet been developed. In the study reported here, we propose a behavioral credit scoring model that reflects the debt-paying ability of recipient firms, which is observed as a time series of financial ratios of firms via the relationship banking activities. We utilize this time series, as well as missing patterns of financial information, as additional predictors of loan defaults. We compare our proposed behavioral credit scoring models, fitted at different points of elapsed time, to the application credit scoring model. Finally, we suggest the best behavioral credit scoring model for technology-based SMEs. Our study can contribute to the reduction of the risk involved in credit funding for technology-based SMEs. Copyright Springer Science+Business Media New York 2013

Suggested Citation

  • So Sohn & Yoon Kim, 2013. "Behavioral credit scoring model for technology-based firms that considers uncertain financial ratios obtained from relationship banking," Small Business Economics, Springer, vol. 41(4), pages 931-943, December.
    • Handle: RePEc:kap:sbusec:v:41:y:2013:i:4:p:931-943
    DOI: 10.1007/s11187-012-9457-5
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    Cited by:

    1. Francesco Quatraro & Marco Vivarelli, 2015. "Drivers of Entrepreneurship and Post-entry Performance of Newborn Firms in Developing Countries," The World Bank Research Observer, World Bank, vol. 30(2), pages 277-305.
    2. 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.
    3. Yonghan Ju & So Young Sohn, 2017. "Technology Credit Scoring Based on a Quantification Method," Sustainability, MDPI, vol. 9(6), pages 1-16, June.
    4. Carmen Gallucci & Rosalia Santullli & Michele Modina & Vincenzo Formisano, 2023. "Financial ratios, corporate governance and bank-firm information: a Bayesian approach to predict SMEs’ default," Journal of Management & Governance, Springer;Accademia Italiana di Economia Aziendale (AIDEA), vol. 27(3), pages 873-892, September.
    5. Long Wu & Lei Xu, 2022. "Bank loans and firm environmental information disclosure: Evidence from China's heavy polluters," Australian Economic Papers, Wiley Blackwell, vol. 61(1), pages 42-71, March.
    6. Yonghan Ju & So Young Sohn, 2015. "Stress test for a technology credit guarantee fund based on survival analysis," Journal of the Operational Research Society, Palgrave Macmillan;The OR Society, vol. 66(3), pages 463-475, March.
    7. Raffaella Calabrese & Galina Andreeva & Jake Ansell, 2019. "“Birds of a Feather” Fail Together: Exploring the Nature of Dependency in SME Defaults," Risk Analysis, John Wiley & Sons, vol. 39(1), pages 71-84, January.
    8. Andreeva, Galina & Calabrese, Raffaella & Osmetti, Silvia Angela, 2016. "A comparative analysis of the UK and Italian small businesses using Generalised Extreme Value models," European Journal of Operational Research, Elsevier, vol. 249(2), pages 506-516.
    9. Dennis Bams & Magdalena Pisa & Christian C. P. Wolff, 2021. "Spillovers to small business credit risk," Small Business Economics, Springer, vol. 57(1), pages 323-352, June.
    10. Long Wu & Lei Xu, 2020. "Venture capital certification of small and medium‐sized enterprises towards banks: evidence from China," Accounting and Finance, Accounting and Finance Association of Australia and New Zealand, vol. 60(2), pages 1601-1633, June.
    11. Maté-Sánchez-Val, Mariluz & López-Hernandez, Fernando & Rodriguez Fuentes, Christian Camilo, 2018. "Geographical factors and business failure: An empirical study from the Madrid metropolitan area," Economic Modelling, Elsevier, vol. 74(C), pages 275-283.
    12. Won Sang Lee & So Young Sohn, 2017. "Identifying Emerging Trends of Financial Business Method Patents," Sustainability, MDPI, vol. 9(9), pages 1-21, September.
    13. 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.

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    More about this item

    Keywords

    Technology application credit scoring; Behavior scoring model; Relationship banking; Financial ratios; Logistic regression model; Sustainability; C35; G21; G24; L26;
    All these keywords.

    JEL classification:

    • C35 - Mathematical and Quantitative Methods - - Multiple or Simultaneous Equation Models; Multiple Variables - - - Discrete Regression and Qualitative Choice Models; Discrete Regressors; Proportions
    • G21 - Financial Economics - - Financial Institutions and Services - - - Banks; Other Depository Institutions; Micro Finance Institutions; Mortgages
    • G24 - Financial Economics - - Financial Institutions and Services - - - Investment Banking; Venture Capital; Brokerage
    • L26 - Industrial Organization - - Firm Objectives, Organization, and Behavior - - - Entrepreneurship

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