IDEAS home Printed from https://ideas.repec.org/a/eee/ecolet/v234y2024ics0165176523005013.html
   My bibliography  Save this article

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
    as

    Download full text from publisher

    File URL: http://www.sciencedirect.com/science/article/pii/S0165176523005013
    Download Restriction: Full text for ScienceDirect subscribers only
    File URL: https://libkey.io/10.1016/j.econlet.2023.111475?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 look for a different version below or search for a different version of it.

    Other versions of this item:

    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. 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).
    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. Krippner, Leo, 2013. "Measuring the stance of monetary policy in zero lower bound environments," Economics Letters, Elsevier, vol. 118(1), pages 135-138.
    5. 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.
    6. 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).
    7. 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.
    8. Fiorella De Fiore & Leonardo Gambacorta & Cristina Manea, 2023. "Big techs and the credit channel of monetary policy," BIS Working Papers 1088, Bank for International Settlements.
    9. Yiping Huang & Xiang Li & Han Qiu & Changhua Yu, 2023. "Big tech credit and monetary policy transmission: micro-level evidence from China," BIS Working Papers 1084, Bank for International Settlements.
    10. 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.
    11. Huang, Yiping & Li, Xiang & Qiu, Han & Su, Dan & Yu, Changhua, 2024. "Bigtech credit, small business, and monetary policy transmission: Theory and evidence," IWH Discussion Papers 18/2022, Halle Institute for Economic Research (IWH), revised 2024.
    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. Tobias Berg & Andreas Fuster & Manju Puri, 2022. "FinTech Lending," Annual Review of Financial Economics, Annual Reviews, vol. 14(1), pages 187-207, November.
    2. Giulio Cornelli & Leonardo Gambacorta & Livia Pancotto, 2023. "Buy now, pay later: a cross-country analysis," BIS Quarterly Review, Bank for International Settlements, December.
    3. Cuadros-Solas, Pedro J. & Cubillas, Elena & Salvador, Carlos, 2023. "Does alternative digital lending affect bank performance? Cross-country and bank-level evidence," International Review of Financial Analysis, Elsevier, vol. 90(C).
    4. Eccles, Peter & Grout, Paul & Siciliani, Paolo & Zalewska, Anna, 2021. "The impact of machine learning and big data on credit markets," Bank of England working papers 930, Bank of England.
    5. 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.
    6. Pierri, Nicola & Timmer, Yannick, 2022. "The importance of technology in banking during a crisis," Journal of Monetary Economics, Elsevier, vol. 128(C), pages 88-104.
    7. Oskar Kowalewski & Pawel Pisany & Emil Slazak, 2021. "What determines cross-country differences in fintech and bigtech credit markets?," Working Papers 2021-ACF-02, IESEG School of Management.
    8. Cuadros-Solas, Pedro J. & Cubillas, Elena & Salvador, Carlos & Suárez, Nuria, 2024. "Digital disruptors at the gate. Does FinTech lending affect bank market power and stability?," Journal of International Financial Markets, Institutions and Money, Elsevier, vol. 92(C).
    9. Kowalewski, Oskar & Pisany, Paweł & Ślązak, Emil, 2022. "Digitalization and data, institutional quality and culture as drivers of technology-based credit providers," Journal of Economics and Business, Elsevier, vol. 121(C).
    10. Giulio Cornelli & Jon Frost & Leonardo Gambacorta & Raghavendra Rau & Robert Wardrop & Tania Ziegler, 2020. "Fintech and big tech credit: a new database," BIS Working Papers 887, Bank for International Settlements.
    11. Nicola Branzoli & Ilaria Supino, 2020. "FinTech credit: a critical review of empirical research," Questioni di Economia e Finanza (Occasional Papers) 549, Bank of Italy, Economic Research and International Relations Area.
    12. Rapih, Subroto & Susantiningrum, & Wahyono, Budi & Borges, Jorge Tavares & Phongsounthone, Somesanook, 2023. "Financial openness and fintech credit," Finance Research Letters, Elsevier, vol. 56(C).
    13. Catalán-Herrera, Juan & Arriaza, Juan Carlos & Alvarado, Ricardo, 2019. "Is the financial accelerator story, empirically relevant for the determinants of the interest rate spread?," The Quarterly Review of Economics and Finance, Elsevier, vol. 71(C), pages 37-47.
    14. Doerr, Sebastian & Frost, Jon & Gambacorta, Leonardo & Shreeti, Vatsala, 2023. "Big techs in finance," CEPR Discussion Papers 18665, C.E.P.R. Discussion Papers.
    15. Sumon Kumar Bhaumik & Manisha Chakrabarty & Ali M. Kutan & Ekta Selarka, 2021. "How Effective are Stock Market Reforms in Emerging Market Economies? Evidence from a Panel VAR Model of the Indian Stock Market," Journal of Quantitative Economics, Springer;The Indian Econometric Society (TIES), vol. 19(4), pages 795-818, December.
    16. 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).
    17. Martin Feldkircher & Gabriele Tondl, 2020. "Global Factors Driving Inflation and Monetary Policy: A Global VAR Assessment," International Advances in Economic Research, Springer;International Atlantic Economic Society, vol. 26(3), pages 225-247, August.
    18. Joan Costa-Font & Cristina Vilaplana-Prieto, 2023. "‘Investing’ in care for old age? An examination of long-term care expenditure dynamics and its spillovers," Empirical Economics, Springer, vol. 64(1), pages 1-30, January.
    19. Stewart, Robert & Chowdhury, Murshed & Arjoon, Vaalmikki, 2021. "Interdependencies between regulatory capital, credit extension and economic growth," Journal of Economics and Business, Elsevier, vol. 117(C).
    20. Giulio Cornelli & Jon Frost & Leonardo Gambacorta & Raghavendra Rau & Robert Wardrop & Tania Ziegler, 2021. "Fintech and Big Tech Credit: What Explains the Rise of Digital Lending?," CESifo Forum, ifo Institute - Leibniz Institute for Economic Research at the University of Munich, vol. 22(02), pages 30-34, March.

    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

    Statistics

    Access and download statistics

    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:ecolet:v:234:y:2024:i:c:s0165176523005013. 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/ecolet .

    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.