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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

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  • 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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    References listed on IDEAS

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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," World Bank Research Observer, World Bank Group, vol. 30(2), pages 277-305.
    2. 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.
    3. repec:gam:jsusta:v:9:y:2017:i:9:p:1670-:d:112597 is not listed on IDEAS
    4. repec:gam:jsusta:v:9:y:2017:i:9:p:1588-:d:111035 is not listed on IDEAS
    5. repec:gam:jsusta:v:9:y:2017:i:6:p:1057-:d:101868 is not listed on IDEAS

    More about this item

    Keywords

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

    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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