Data vs Collateral

45 Pages Posted: 22 Sep 2020

See all articles by Leonardo Gambacorta

Leonardo Gambacorta

Bank for International Settlements (BIS); Centre for Economic Policy Research (CEPR)

Yiping Huang

Peking University

Zhenhua Li

Independent

Han Qiu

Peking University

Shu Chen

Independent

Date Written: September 1, 2020

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.

Keywords: asymmetric information, banks, Big Data, big tech, Collateral, credit markets

JEL Classification: D22, G31, R30

Suggested Citation

Gambacorta, Leonardo and Huang, Yiping and Li, Zhenhua and Qiu, Han and Chen, Shu, Data vs Collateral (September 1, 2020). Available at SSRN: https://ssrn.com/abstract=3696342

Leonardo Gambacorta

Bank for International Settlements (BIS) ( email )

Centralbahnplatz 2
Basel, Basel-Stadt 4002
Switzerland

Centre for Economic Policy Research (CEPR)

London
United Kingdom

Yiping Huang

Peking University ( email )

Beijing, 100871
China

Han Qiu

Peking University ( email )

No. 38 Xueyuan Road
Haidian District
Beijing, Beijing 100871
China

Shu Chen

Independent ( email )

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