Collateral is used in debt contracts to mitigate agency problems arising from asymmetric information. Banks usually require their borrowers to pledge tangible assets, such as real estate, to lessen ex ante adverse selection problems or as a way to reduce ex post frictions, such as moral hazard. The use of collateral is more widespread for opaque firms, such as small and medium-sized enterprises (SMEs). It is common for SME owners to pledge their homes to finance their firms. According to a recent survey by the Financial Stability Board, 90% of bank loans to SMEs in the US are collateralised, compared with 82% in Switzerland and 65% in Canada. This drops to 53% in China, where many SMEs lack basic documentation and are geographically remote from bank branches.
With the development of fintech, especially the entry of large technology firms (big techs) into financial services, nontraditional data play an increasingly important role in credit assessment for SMEs. The business model of big techs rests on enabling direct interactions among a large number of users. An essential by-product of their business is the large stock of user data. Data are used as an input to offer a range of services that exploit natural network effects, generating further user activity. Increased user activity then completes the circle, as it generates yet more data. The mutually reinforcing data-network-activity feedback loop helps big tech firms identify the characteristics of their clients and offer them financial services that best suit their needs.
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, we find that big tech credit does not correlate with local business conditions (see figure) and house prices when controlling for demand factors, but that it does react strongly to changes in firm-specific characteristics, such as the transaction volumes and network scores used to calculate firm credit ratings. This is particularly the case when a borrower firm conducts its business activity on the relevant e-commerce platform managed by the big tech. By contrast, both secured and unsecured bank credit reacts significantly to local house price dynamics, which incorporate useful information on the client’s creditworthiness and the business conditions in which it operates. This evidence implies that the wider use of big tech credit could reduce the importance of the collateral channel but, at the same time, make lending more reactive to changes in firms’ business activity.
Figure 1: Elasticity of credit with respect to house prices and GDP
Note: The figure reports the coefficient of three different regressions (one for each credit types) in which the log of credit is regressed with respect to the log of house prices at the city level, the log of GDP at the city level and a complete set of time dummies. Significance level: ** p<0.05; *** p<0.01