IDEAS home Printed from https://ideas.repec.org/a/eee/chieco/v81y2023ics1043951x23001013.html
   My bibliography  Save this article

BigTech credit risk assessment for SMEs

Author

Listed:
  • Huang, Yiping
  • Li, Zhenhua
  • Qiu, Han
  • Tao, Sun
  • Wang, Xue
  • Zhang, Longmei

Abstract

Lending by big technology companies (BigTechs) is an important new financial innovation in the digital era. This paper attempts to evaluate robustness and special features of BigTech's credit risk assessment. Using 1.8 million loan transactions for online merchants of a leading Chinese virtue bank, we carry out a horse race analysis between the BigTech approach (i.e., big data and machine learning models) and the bank approach (i.e., traditional financial data and scorecard models) in predicting loan defaults. We show that the BigTech approach better predicts loan defaults, reflecting information and modeling advantages. Though bank approach do well for the firms which have records in credit registry, BigTech's proprietary information can complement or, where necessary, substitute for credit history in predicting defaults, especially for the unbanked borrowers. We further discuss inclusiveness feature of the BigTech approach and the implications for financial inclusion, financial intermediaries' businesses and regulators' policy.

Suggested Citation

  • Huang, Yiping & Li, Zhenhua & Qiu, Han & Tao, Sun & Wang, Xue & Zhang, Longmei, 2023. "BigTech credit risk assessment for SMEs," China Economic Review, Elsevier, vol. 81(C).
    • Handle: RePEc:eee:chieco:v:81:y:2023:i:c:s1043951x23001013
    DOI: 10.1016/j.chieco.2023.102016
    as

    Download full text from publisher

    File URL: http://www.sciencedirect.com/science/article/pii/S1043951X23001013
    Download Restriction: Full text for ScienceDirect subscribers only
    File URL: https://libkey.io/10.1016/j.chieco.2023.102016?utm_source=ideas
    LibKey link: if access is restricted and if your library uses this service, LibKey will redirect you to where you can use your library subscription to access this item
    ---><---

    As the access to this document is restricted, you may want to search for a different version of it.

    References listed on IDEAS

    as
    1. Vos, Ed & Yeh, Andy Jia-Yuh & Carter, Sara & Tagg, Stephen, 2007. "The happy story of small business financing," Journal of Banking & Finance, Elsevier, vol. 31(9), pages 2648-2672, September.
    2. Lei Liu & Guangli Lu & Wei Xiong, 2022. "The Big Tech Lending Model," NBER Working Papers 30160, National Bureau of Economic Research, Inc.
    3. Berger, Allen N. & Udell, Gregory F., 2006. "A more complete conceptual framework for SME finance," Journal of Banking & Finance, Elsevier, vol. 30(11), pages 2945-2966, November.
    4. Jon Frost & Leonardo Gambacorta & Yi Huang & Hyun Song Shin & Pablo Zbinden, 2019. "BigTech and the changing structure of financial intermediation," Economic Policy, CEPR, CESifo, Sciences Po;CES;MSH, vol. 34(100), pages 761-799.
    5. Khandani, Amir E. & Kim, Adlar J. & Lo, Andrew W., 2010. "Consumer credit-risk models via machine-learning algorithms," Journal of Banking & Finance, Elsevier, vol. 34(11), pages 2767-2787, November.
    6. Allen N. Berger & Gregory F. Udell, 2002. "Small Business Credit Availability and Relationship Lending: The Importance of Bank Organisational Structure," Economic Journal, Royal Economic Society, vol. 112(477), pages 32-53, February.
    7. Besanko, David & Thakor, Anjan V, 1987. "Collateral and Rationing: Sorting Equilibria in Monopolistic and Competitive Credit Markets," International Economic Review, Department of Economics, University of Pennsylvania and Osaka University Institute of Social and Economic Research Association, vol. 28(3), pages 671-689, October.
    8. Leonardo Gambacorta & Yiping Huang & Han Qiu & Jingyi Wang, 2019. "How do machine learning and non-traditional data affect credit scoring? New evidence from a Chinese fintech firm," BIS Working Papers 834, Bank for International Settlements.
    9. Asli Demirguc-Kunt & Leora Klapper, 2013. "Measuring Financial Inclusion: Explaining Variation in Use of Financial Services across and within Countries," Brookings Papers on Economic Activity, Economic Studies Program, The Brookings Institution, vol. 44(1 (Spring), pages 279-340.
    10. Sumit Agarwal, 2010. "Distance and Private Information in Lending," Review of Financial Studies, Society for Financial Studies, vol. 23(7), pages 2757-2788, July.
    11. Asli Demirguc-Kunt & Leora Klapper, 2013. "Measuring Financial Inclusion: Explaining Variation in Use of Financial Services across and within Countries," Brookings Papers on Economic Activity, Economic Studies Program, The Brookings Institution, vol. 46(1 (Spring), pages 279-340.
    12. Mark Freel & Sara Carter & Stephen Tagg & Colin Mason, 2012. "The latent demand for bank debt: characterizing “discouraged borrowers”," Small Business Economics, Springer, vol. 38(4), pages 399-418, May.
    13. Geraldo Cerqueiro & Steven Ongena & Kasper Roszbach, 2016. "Collateralization, Bank Loan Rates, and Monitoring," Journal of Finance, American Finance Association, vol. 71(3), pages 1295-1322, June.
    14. Stiglitz, Joseph E & Weiss, Andrew, 1981. "Credit Rationing in Markets with Imperfect Information," American Economic Review, American Economic Association, vol. 71(3), pages 393-410, June.
    15. Majid Bazarbash, 2019. "FinTech in Financial Inclusion: Machine Learning Applications in Assessing Credit Risk," IMF Working Papers 2019/109, International Monetary Fund.
    16. Julapa Jagtiani & Catharine Lemieux, 2019. "The roles of alternative data and machine learning in fintech lending: Evidence from the LendingClub consumer platform," Financial Management, Financial Management Association International, vol. 48(4), pages 1009-1029, December.
    17. Berger, Allen N. & Frame, W. Scott & Ioannidou, Vasso, 2016. "Reexamining the empirical relation between loan risk and collateral: The roles of collateral liquidity and types," Journal of Financial Intermediation, Elsevier, vol. 26(C), pages 28-46.
    18. L C Thomas & R W Oliver & D J Hand, 2005. "A survey of the issues in consumer credit modelling research," Journal of the Operational Research Society, Palgrave Macmillan;The OR Society, vol. 56(9), pages 1006-1015, September.
    Full references (including those not matched with items on IDEAS)

    Most related items

    These are the items that most often cite the same works as this one and are cited by the same works as this one.
    1. Dong, Yingwei & Gou, Qin & Qiu, Han, 2023. "Big tech credit score and default risk ——Evidence from loan-level data of a representative microfinance company in China," China Economic Review, Elsevier, vol. 81(C).
    2. Reto Wernli & Andreas Dietrich, 2022. "Only the brave: improving self-rationing efficiency among discouraged Swiss SMEs," Small Business Economics, Springer, vol. 59(3), pages 977-1003, October.
    3. Lei Lu & Jianxing Wei & Weixing Wu & Yi Zhou, 2023. "Pricing strategies in BigTech lending: Evidence from China," Financial Management, Financial Management Association International, vol. 52(2), pages 333-374, June.
    4. Scott McCarthy & Barry Oliver & Martie-Louise Verreynne, 2017. "Bank financing and credit rationing of Australian SMEs," Australian Journal of Management, Australian School of Business, vol. 42(1), pages 58-85, February.
    5. Lin, Aijie & Peng, Yulei & Wu, Xi, 2022. "Digital finance and investment of micro and small enterprises: Evidence from China," China Economic Review, Elsevier, vol. 75(C).
    6. Alraqeb Zeynep & Knaack Peter & Macaire Camille, 2022. "Does FinTech Promote Entrepreneurship? Evidence from China [L’adoption des Fintech favorise-t-elle l’entreprenariat ? Le cas de la Chine]," Working papers 895, Banque de France.
    7. Berger, Allen N. & Espinosa-Vega, Marco A. & Frame, W. Scott & Miller, Nathan H., 2011. "Why do borrowers pledge collateral? New empirical evidence on the role of asymmetric information," Journal of Financial Intermediation, Elsevier, vol. 20(1), pages 55-70, January.
    8. Kowalewski, Oskar & Pisany, Paweł, 2022. "Banks' consumer lending reaction to fintech and bigtech credit emergence in the context of soft versus hard credit information processing," International Review of Financial Analysis, Elsevier, vol. 81(C).
    9. Zhang, Xuan & Zhang, Yongmin & Scheffel, Eric & Zhao, Yang, 2022. "A key driver for the mixed relationship between loan risk premiums and collateral: Evidence from China," International Review of Financial Analysis, Elsevier, vol. 83(C).
    10. Stefano Filomeni & Michele Modina & Elena Tabacco, 2023. "Trade credit and firm investments: empirical evidence from Italian cooperative banks," Review of Quantitative Finance and Accounting, Springer, vol. 60(3), pages 1099-1141, April.
    11. Lee, Neil & Brown, Ross, 2016. "Innovation, SMEs and the liability of distance: the demand and supply of bank funding in peripheral UK regions," LSE Research Online Documents on Economics 66215, London School of Economics and Political Science, LSE Library.
    12. Cerqueiro, Geraldo & Degryse, Hans & Ongena, Steven, 2011. "Rules versus discretion in loan rate setting," Journal of Financial Intermediation, Elsevier, vol. 20(4), pages 503-529, October.
    13. Lei Xu & Qian Liu & Bin Li & Chen Ma, 2022. "Fintech business and firm access to bank loans," Accounting and Finance, Accounting and Finance Association of Australia and New Zealand, vol. 62(4), pages 4381-4421, December.
    14. Ferri, Giovanni & Murro, Pierluigi, 2015. "Do firm–bank ‘odd couples’ exacerbate credit rationing?," Journal of Financial Intermediation, Elsevier, vol. 24(2), pages 231-251.
    15. Refait-Alexandre, Catherine & Serve, Stéphanie, 2020. "Multiple banking relationships: Do SMEs mistrust their banks?," Research in International Business and Finance, Elsevier, vol. 51(C).
    16. David Aristei & Gabriele Angori, 2022. "Heterogeneity and state dependence in firms’ access to bank credit," Small Business Economics, Springer, vol. 59(1), pages 47-78, June.
    17. Gama, Ana Paula Matias & Duarte, Fábio Dias & Esperança, José Paulo, 2017. "Why discouraged borrowers exist? An empirical (re)examination from less developed countries," Emerging Markets Review, Elsevier, vol. 33(C), pages 19-41.
    18. Oana Peia & Radu Vranceanu, 2014. "Optimal Return in a Model of Bank Small-business Financing," Working Papers hal-00952641, HAL.
    19. Anastasiou, Dimitris & Kallandranis, Christos & Drakos, Konstantinos, 2022. "Borrower discouragement prevalence for Eurozone SMEs: Investigating the impact of economic sentiment," Journal of Economic Behavior & Organization, Elsevier, vol. 194(C), pages 161-171.
    20. Anoosheh Rostamkalaei & Miwako Nitani & Allan Riding, 2020. "Borrower discouragement: the role of informal turndowns," Small Business Economics, Springer, vol. 54(1), pages 173-188, January.

    Corrections

    All material on this site has been provided by the respective publishers and authors. You can help correct errors and omissions. When requesting a correction, please mention this item's handle: RePEc:eee:chieco:v:81:y:2023:i:c:s1043951x23001013. See general information about how to correct material in RePEc.

    If you have authored this item and are not yet registered with RePEc, we encourage you to do it here. This allows to link your profile to this item. It also allows you to accept potential citations to this item that we are uncertain about.

    If CitEc recognized a bibliographic reference but did not link an item in RePEc to it, you can help with this form .

    If you know of missing items citing this one, you can help us creating those links by adding the relevant references in the same way as above, for each refering item. If you are a registered author of this item, you may also want to check the "citations" tab in your RePEc Author Service profile, as there may be some citations waiting for confirmation.

    For technical questions regarding this item, or to correct its authors, title, abstract, bibliographic or download information, contact: Catherine Liu (email available below). General contact details of provider: http://www.elsevier.com/locate/chieco .

    Please note that corrections may take a couple of weeks to filter through the various RePEc services.

    IDEAS is a RePEc service. RePEc uses bibliographic data supplied by the respective publishers.