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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, a new paper by Yiping Huang and co-authors investigates how different forms of credit correlate with local economic activity, house prices and firm characteristics. In this upcoming seminar, Huang will discuss their findings, including 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.

About the Seminar Series

Financial market development goes hand-in-hand with economic growth. The development of China’s capital markets in terms of size, regulations, capability, and efficiency has been impressive. China may now even lead globally in some dimensions, notably e-payments systems. Yet, China’s capital markets are still a work-in-progress facing both generic and unique challenges. Other Asian capital markets have even greater uneven development. Some in advanced Asian economies have acquired globally acclaimed reputation and capabilities while various regulatory and structural weaknesses dwarf others. Corporations and investors have been inclined to arbitrage cross-border regulatory and developmental gaps; so the very uneven status of capital markets across Asia is a policy issue for the governments in the entire region and perhaps globally. Analyzing the positive and negative lessons in the functioning of Asia’s capital markets, and identifying reforms and applications of technology that could further improve Asian capital markets’ allocation efficiency, financial inclusion, and forewarning against reforms that might cause problems can benefit practitioners, policymakers and researchers, and can contribute significantly to overall prosperity.

The ABFER and the University of Chicago’s Becker Friedman Institute China (BFI-China), in collaboration with National University of Singapore (NUS) Business School, Shanghai Advanced Institute of Finance (SAIF), The Chinese University of Hong Kong (CUHK) Department of Economics, CUHK-Shenzhen and Tsinghua University PBC School of Finance (Tsinghua PBCSF), hope to provide a virtual network to benefit researchers, policymakers, and practitioners from Asia and beyond.

All times are listed in Central Standard Time. A unique Zoom webinar link will be sent to you two days before the event. 

Session Format

Each session lasts for an hour (30 minutes for the author, 15 minutes for the discussion, 15 minutes for participants’ Q&A).


Wednesday, September 15, 2021
9:00 pm - 10:00 pm
Data vs. Collateral

Yiping Huang, 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

Discussant: Wei Jiang, Arthur F. Burns Professor of Free and Competitive Enterprise, Columbia Business School, Finance & Economics Division, Columbia University