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Data vs collateral

Author

Listed:
  • Gambacorta, Leonardo
  • Huang, Yiping
  • Li, Zhenhua
  • Qiu, Han
  • Chen, Shu
Abstract
The use of massive amounts of data by large technology firms (big techs) to assess firms’ creditworthiness could reduce the need for collateral in solving asymmetric information problems in credit markets. Using a unique dataset of more than 2 million Chinese firms that received credit from both an important big tech firm (Ant Group) and traditional commercial banks, this paper investigates how different forms of credit correlate with local economic activity, house prices and firm characteristics. We find that big tech credit does not correlate with local business conditions and house prices when controlling for demand factors, but reacts strongly to changes in firm characteristics, such as transaction volumes and network scores used to calculate firm credit ratings. By contrast, both secured and unsecured bank credit react significantly to local house prices, which incorporate useful information on the environment in which clients operate and on their creditworthiness. This evidence implies that a greater use of big tech credit – granted on the basis of machine learning and big data – could reduce the importance of collateral in credit markets and potentially weaken the financial accelerator mechanism.

Suggested Citation

  • Gambacorta, Leonardo & Huang, Yiping & Li, Zhenhua & Qiu, Han & Chen, Shu, 2020. "Data vs collateral," CEPR Discussion Papers 15262, C.E.P.R. Discussion Papers.
    • Handle: RePEc:cpr:ceprdp:15262
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    Citations

    Blog mentions

    As found by EconAcademics.org, the blog aggregator for Economics research:
    1. Big Tech, Fintech, and the Future of Credit
      by Steve Cecchetti and Kim Schoenholtz in Money, Banking and Financial Markets on 2020-12-21 15:06:54

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    2. Boot, Arnoud & Hoffmann, Peter & Laeven, Luc & Ratnovski, Lev, 2021. "Fintech: what’s old, what’s new?," Journal of Financial Stability, Elsevier, vol. 53(C).

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    More about this item

    Keywords

    Big tech; Big data; Collateral; Banks; Asymmetric information; Credit markets;
    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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