Thu Sep 16 +08 (9 months ago)
In your timezone (EDT): Wed Sep 15 9:50pm - Wed Sep 15 11:10pm
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 two 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.
Professor of Finance, Shanghai Advanced Institute of Finance (SAIF), Shanghai Jiao Tong University and Senior Fellow, ABFER
Sinar Mas Chair Professor of Finance of Economics, Deputy Dean of the National School of Development, and Director of the Institute of Digital Finance, Peking University
Arthur F. Burns Professor of Free and Competitive Enterprise, Columbia Business School, Finance & Economics Division, Columbia University