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Differentiated Use of Small Business Credit Scoring by Relationship Lenders and Transactional Lenders: Evidence from Firm-Bank Matched Data in Japan

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  • Hasumi, Ryo
  • Hirata, Hideaki
  • Ono, Arito

Abstract

This paper examines the ex-post performance of small and medium enterprises (SMEs) that obtained small business credit scoring (SBCS) loans by using a unique Japanese firm-bank matched dataset. The ex-post probability of default after the SBCS loan was provided significantly increased for SMEs that obtained an SBCS loan from a transactional lender. Also, the lending attitude of relationship lenders during the recent global financial crisis was more severe if a transactional lender had extended an SBCS loan to a firm. These findings suggest that SBCS loans by a transactional lender are detrimental to a relationship lender’s incentive to monitor SMEs and maintain relationships.

Suggested Citation

  • Hasumi, Ryo & Hirata, Hideaki & Ono, Arito, 2012. "Differentiated Use of Small Business Credit Scoring by Relationship Lenders and Transactional Lenders: Evidence from Firm-Bank Matched Data in Japan," Working Paper Series 23, Center for Interfirm Network, Institute of Economic Research, Hitotsubashi University.
    • Handle: RePEc:hit:cinwps:23
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    Cited by:

    1. Song Zhang & Liang Han & Konstantinos Kallias & Antonios Kallias, 2021. "The value of in-person banking: evidence from U.S. small businesses," Review of Quantitative Finance and Accounting, Springer, vol. 57(4), pages 1393-1435, November.
    2. Giorgio Albareto & Roberto Felici & Enrico Sette, 2016. "Does credit scoring improve the selection of borrowers and credit quality?," Temi di discussione (Economic working papers) 1090, Bank of Italy, Economic Research and International Relations Area.
    3. Ono, Arito & Hasumi, Ryo & Hirata, Hideaki, 2014. "Differentiated use of small business credit scoring by relationship lenders and transactional lenders: Evidence from firm–bank matched data in Japan," Journal of Banking & Finance, Elsevier, vol. 42(C), pages 371-380.
    4. 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.
    5. Ono, Arito & Uesugi, Iichiro, 2014. "SME Financing in Japan during the Global Financial Crisis: Evidence from Firm Surveys," HIT-REFINED Working Paper Series 6, Institute of Economic Research, Hitotsubashi University.
    6. Fricke, Daniel & Roukny, Tarik, 2020. "Generalists and specialists in the credit market," Journal of Banking & Finance, Elsevier, vol. 112(C).
    7. Pankaj C. Patel & Mike G. Tsionas, 2022. "Learning‐by‐lending and learning‐by‐repaying: A two‐sided learning model for defaults on Small Business Administration loans," Managerial and Decision Economics, John Wiley & Sons, Ltd., vol. 43(4), pages 906-919, June.
    8. Saiki Tsuchiya & Shinichi Nishioka, 2014. "Estimation of Firms' Default Rates in terms of Intangible Assets," Bank of Japan Working Paper Series 14-E-2, Bank of Japan.
    9. 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.
    10. Agostino, Mariarosaria & Errico, Lucia & Rondinella, Sandro & Trivieri, Francesco, 2023. "Enduring lending relationships and european firms default," Research in Economics, Elsevier, vol. 77(4), pages 459-477.
    11. Xiaowo Wu & Jiangwei Tu & Boru Liu & Xi Zhou & Yanxiong Wu, 2022. "Credit Risk Evaluation of Forest Farmers under Internet Crowdfunding Mode: The Case of China’s Collective Forest Regions," Sustainability, MDPI, vol. 14(10), pages 1-17, May.
    12. Ono, Arito & Saito, Yukiko & Sakai, Koji & Uesugi, Iichiro, 2016. "Does Geographical Proximity Matter in Small Business Lending? Evidence from Changes in Main Bank Relationships," HIT-REFINED Working Paper Series 40, Institute of Economic Research, Hitotsubashi University.
    13. Yao-Zhi Xu & Jian-Lin Zhang & Ying Hua & Lin-Yue Wang, 2019. "Dynamic Credit Risk Evaluation Method for E-Commerce Sellers Based on a Hybrid Artificial Intelligence Model," Sustainability, MDPI, vol. 11(19), pages 1-17, October.
    14. Nitani, Miwako & Legendre, Nicolas, 2021. "Cooperative lenders and the performance of small business loans," Journal of Banking & Finance, Elsevier, vol. 128(C).
    15. Song Zhang & Liang Han & Konstantinos Kallias & Antonios Kallias, 2022. "Bank switching of US small businesses: new methods and evidence," Review of Quantitative Finance and Accounting, Springer, vol. 58(4), pages 1573-1616, May.

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    More about this item

    Keywords

    small business credit scoring; lending technology; relationship lending;
    All these keywords.

    JEL classification:

    • G21 - Financial Economics - - Financial Institutions and Services - - - Banks; Other Depository Institutions; Micro Finance Institutions; Mortgages
    • G32 - Financial Economics - - Corporate Finance and Governance - - - Financing Policy; Financial Risk and Risk Management; Capital and Ownership Structure; Value of Firms; Goodwill

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