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Journal of Financial Services Research

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14-12-2023 | Original Research

How FinTech Affects Bank Systemic Risk: Evidence from China

Authors: Qian Chen, Chuang Shen

Published in: Journal of Financial Services Research | Issue 1/2024

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Abstract

In this paper, we investigate whether and how financial technology (FinTech) affects the systemic risk of Chinese banks. Based on bank-level panel data and the system generalized method of moments (SYS-GMM), we find that FinTech increases both banks’ exposure and their contribution to systemic risk, and these effects only occur in local commercial banks, less profitable banks, and banks in regions with less developed FinTech. We also investigate the source of FinTech’s influence and find that it increases the scale of interbank business and enhances the correlation between banks that increases the possibility of risk contagion.

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The effect of FinTech on banks can be divided into two aspects: internal and external. External effects refer to the impact of FinTech on banks; internal effects refer to the effect of FinTech tools used by the bank itself. In this paper, we only consider internal effects.

The five state-owned banks are the Industrial and Commercial Bank of China, Bank of China, Agriculture Bank of China, China Construction Bank, and Bank of Communication. The 10 joint-stock banks are China Merchants Bank, China Minsheng Bank, China City Bank, China Everbright Bank, Shanghai Pudong Development Bank, Industrial Bank, Pingan Bank, Huaxia Bank, China Zheshang Bank, and Guangfa Bank. Other banks include city commercial banks, rural commercial banks and others, such as the Postal Saving Bank of China, Chongqing Rural Commercial Bank, Bank of Shanghai, Bank of Beijing, and the Guangzhou Rural Commercial Bank.

In order to avoid inaccurate results caused by winsorizing the variables, we add a robustness check for the non-winsorized sample in Subsection 4.5.2.

This index was produced by a research team from the Institute of Digital Finance at Peking University and Ant Group and involves coverage breadth, usage depth, and digitization level; usage depth involves subindexes such as payment, credit, insurance, credit, investment, and money funds. The index covers 31 provinces (and municipalities directly under the central government and autonomous regions, referred to as “provinces”), 337 cities above the prefecture level, and nearly 2,800 counties. The data of some regions are lacking, for example Hong Kong SAR, Macao SAR, and Taiwan province. The time span is 2011-2020 for the provincial and prefecture levels and 2014-2020 for county level.

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Title: How FinTech Affects Bank Systemic Risk: Evidence from China
Authors: Qian Chen
Chuang Shen
Publication date: 14-12-2023
Publisher: Springer US
Published in: Journal of Financial Services Research / Issue 1/2024
Print ISSN: 0920-8550
Electronic ISSN: 1573-0735
DOI: https://doi.org/10.1007/s10693-023-00421-7