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

Author

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

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) reactivity 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

  • Cornelli, Giulio & De Fiore, Fiorella & Gambacorta, Leonardo & Manea, Cristina, 2024. "Fintech vs bank credit: How do they react to monetary policy?," Economics Letters, Elsevier, vol. 234(C).
    • Handle: RePEc:eee:ecolet:v:234:y:2024:i:c:s0165176523005013
    DOI: 10.1016/j.econlet.2023.111475
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    1. De Fiore, Fiorella & Gambacorta, Leonardo & Manea, Cristina, 2023. "Big Techs and the Credit Channel of Monetary Policy," CEPR Discussion Papers 18217, C.E.P.R. Discussion Papers.
    2. Canova, Fabio & Ciccarelli, Matteo, 2013. "Panel Vector Autoregressive Models: A Survey," CEPR Discussion Papers 9380, C.E.P.R. Discussion Papers.
    3. 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.
    4. Hasan, Iftekhar & Kwak, Boreum & Li, Xiang, 2024. "Financial technologies and the effectiveness of monetary policy transmission," European Economic Review, Elsevier, vol. 161(C).
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    7. Gambacorta, Leonardo & Huang, Yiping & Qiu, Han & Wang, Jingyi, 2024. "How do machine learning and non-traditional data affect credit scoring? New evidence from a Chinese fintech firm," Journal of Financial Stability, Elsevier, vol. 73(C).
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    Cited by:

    1. Argudo, Esteban, 2025. "Monetary policy transmission via nonbank lending: Evidence from peer-to-peer loans," Journal of Financial Stability, Elsevier, vol. 80(C).
    2. Chen, Xi & Wang, Qinggang & Wang, Shuai, 2025. "Enterprise digital transformation and bank credit loans—Empirical evidence from listed companies in China," Pacific-Basin Finance Journal, Elsevier, vol. 90(C).
    3. Renzhi, Nuobu & Beirne, John, 2025. "The nexus of peer-to-peer lending and monetary policy transmission: Evidence from the People's Republic of China," Pacific-Basin Finance Journal, Elsevier, vol. 91(C).
    4. Georgios P. Kouretas & Małgorzata Pawłowska, 2025. "Impact of Digital Technology on Traditional Banking: A Case From the Credit Market in the European Union," Journal of International Development, John Wiley & Sons, Ltd., vol. 37(2), pages 468-488, March.
    5. Yarong Shi & Yahan Jin, 2025. "How Fintech Impacts Urban Economic Resilience: A Perspective on the Empowerment of Digital Inclusive Finance," Sustainability, MDPI, vol. 17(17), pages 1-24, August.

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    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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