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Fintech vs bank credit: How do they react to monetary policy?

Author

Listed:
  • Giulio Cornelli
  • Fiorella De Fiore
  • Leonardo Gambacorta
  • Cristina Manea

Abstract

Fintech credit, which includes peer-to-peer and marketplace lending as well as lending facilitated by major technology firms, is witnessing rapid growth worldwide. However, its responsiveness to monetary policy shifts remains largely unexplored. This study employs a novel credit dataset spanning 19 countries from 2005 to 2020 and conducts a PVAR analysis to shed some light on the different reaction of fintech and bank credit to changes in policy rates. The main result is that fintech credit shows a lower (even non-significant) sensitivity to monetary policy shocks in comparison to traditional bank credit. Given the still marginal – although fast growing – macroeconomic significance of fintech credit, its contribution in explaining the variability of real GDP is less than 2%, against around one quarter for bank credit.

Suggested Citation

  • Giulio Cornelli & Fiorella De Fiore & Leonardo Gambacorta & Cristina Manea, 2023. "Fintech vs bank credit: How do they react to monetary policy?," BIS Working Papers 1157, Bank for International Settlements.
    • Handle: RePEc:bis:biswps:1157
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    References listed on IDEAS

    as
    1. Canova, Fabio & Ciccarelli, Matteo, 2013. "Panel Vector Autoregressive Models: A Survey," CEPR Discussion Papers 9380, C.E.P.R. Discussion Papers.
    2. 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.
    3. Krippner, Leo, 2013. "Measuring the stance of monetary policy in zero lower bound environments," Economics Letters, Elsevier, vol. 118(1), pages 135-138.
    4. Stijn Claessens & Jon Frost & Grant Turner & Feng Zhu, 2018. "Fintech credit markets around the world: size, drivers and policy issues," BIS Quarterly Review, Bank for International Settlements, September.
    5. Gambacorta, Leonardo & De Fiore, Fiorella & Manea, Cristina, 2023. "Big Techs and the Credit Channel of Monetary Policy," CEPR Discussion Papers 18217, C.E.P.R. Discussion Papers.
    6. Andrews, Donald W. K. & Lu, Biao, 2001. "Consistent model and moment selection procedures for GMM estimation with application to dynamic panel data models," Journal of Econometrics, Elsevier, vol. 101(1), pages 123-164, March.
    7. Hasan, Iftekhar & Kwak, Boreum & Li, Xiang, 2023. "Financial technologies and the effectiveness of monetary policy transmission," IWH Discussion Papers 26/2020, Halle Institute for Economic Research (IWH), revised 2023.
    8. Cornelli, Giulio & Frost, Jon & Gambacorta, Leonardo & Rau, P. Raghavendra & Wardrop, Robert & Ziegler, Tania, 2023. "Fintech and big tech credit: Drivers of the growth of digital lending," Journal of Banking & Finance, Elsevier, vol. 148(C).
    9. 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.
    Full references (including those not matched with items on IDEAS)

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    More about this item

    Keywords

    fintech credit; monetary policy; PVAR; collateral channel;
    All these keywords.

    JEL classification:

    • D22 - Microeconomics - - Production and Organizations - - - Firm Behavior: Empirical Analysis
    • G31 - Financial Economics - - Corporate Finance and Governance - - - Capital Budgeting; Fixed Investment and Inventory Studies
    • R30 - Urban, Rural, Regional, Real Estate, and Transportation Economics - - Real Estate Markets, Spatial Production Analysis, and Firm Location - - - General

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