The value of FinTech innovations for the finance industry: Evidence from China

Abstract

This paper explores the value effects of FinTech innovations on the finance industry in China, focussing on the banking system. We use a crawler-like application programme to grab patent filing information and manually identify and classify FinTech innovations based on their application fields. FinTech patents generally bring adverse value effects to various finance sectors, revealing a competitive relationship between innovators and financial firms. FinTech innovations applied to insurance and asset management cause the most harm to the industry, especially to state-owned and joint-stock commercial banks. Moreover, disruptive FinTech innovations by start-up firms appear to pose more potential threats to commercial banks.

Keywords:

• FinTech
• financial innovations
• China

Disclosure statement

No potential conflict of interest was reported by the author(s).

Notes

1 The Financial Stability Board (FSB) defines FinTech as technology-enabled innovations in financial services that could result in new business models, applications, processes and products with an associated material effect on the provision of financial services.

2 In fact, some applications are unrelated to finance.

3 According to the database, non-profit organisations include individuals, universities and academic institutes, and other non-profit groups.

4 For example, if the patent files with the keywords of ‘blockchain’ and ‘big data credit’ explicitly describe that both innovations will be applied to the payment system, then they both belong to the category of payment.

5 For example, the data are available from the website of the School of Finance, Central University of Finance and Economics: http://sf.cufe.edu.cn/info/1198/10012.htm.

6 In the subsequent analysis, we drop the patent events of public firms to exclude the disturbance of self-influence. Besides, the observations of public firms are not sufficient to obtain a robust result from the Poisson regression.

7 We calculate daily abnormal returns as the residuals of regressing raw daily returns on the Fama–French three factors (Fama and French 1992).

8 The effect on insurance is even not statistically significant.

9 From 2015 to 2016, the defaults of large P2P platforms, such as Ezubao, also attracted the attention from the central government.

10 The data are obtained from the website of PBC, http://www.pbc.gov.cn.