Fintech, Bank Risk-Taking, and Risk-Warning for Commercial Banks in the Era of Digital Technology

Abstract

Fintech risks commercial banks in three ways, particularly operational efficiency, financial innovation, and risk management. Based on the data of 37 Chinese-listed commercial banks from 2011 to 2020, the study empirically analyzes the impact of fintech on bank risk-taking, and the intermediary effects of the three channels, such as operational efficiency, financial innovation, and risk management. The results show that fintech can effectively reduce the risk of banks. The results of heterogeneity analysis revealed that fintech strongly affects the risk-taking of state-owned banks but not obviously for rural commercial banks. Financial efficiency, financial innovation, and risk management indirectly affect the risk-taking of banks that contributed 8.51, 7.18, and 5.77%, respectively. We also constructed the commercial bank risk-warning index. Based on the quarterly data of banks from 2011 to 2020, we empirically tested the early warning effect of the bank risk-warning index. The results showed that when the signal month is set to 12 months, the bank risk-warning index can have a warning effect in this period.

Enterprise life cycle, financial technology and digital transformation of banks—Evidence from China

“…Therefore, the higher the level of enterprise technological innovation. For commercial banks, financial technology promotes commercial bank business innovation, customer service innovation (G. Li et al, 2022), digital operation and digital transformation (Chen et al, 2022). Digital empowerment allows commercial banks to solve the problems of customer segmentation, accurate marketing of customers, differentiated customer service and increased customer stickiness (Liyanaarachchi et al, 2021).…”

Population age structure, asset price, and financial stability

“… are a set of control variables, including per capita GDP , capital formation , urbanization level , industrialization degree , and the country openness . ε is the error term, which is assumed to be normally distributed at zero mean value (Elahi, Khalid, Tauni, et al, 2022; Li et al, 2022; Peng et al, 2020) and constant variance (Elahi & Khalid, 2022; Elahi, Zhixin, Khalid, & Xu, 2022).…”

“…Equations ( 22) and ( 23 Open jt . ε is the error term, which is assumed to be normally distributed at zero mean value (Elahi, Khalid, Tauni, et al, 2022;Li et al, 2022;Peng et al, 2020) and constant variance Elahi, Zhixin, Khalid, & Xu, 2022).…”

Research on FinTech Talent Evaluation Index System and Recruitment Strategy: Evidence From Shanghai in China

“…How to effectively use FinTech to promote the autonomous innovation capability of society and enhance core competitiveness has become a global hot research issue (Bajwa et al, 2022;Hasan et al, 2020). Professional talent, as the core subjects of financial enterprises, are the basis and key to realize the innovative development of the financial industry and even the whole social economy (G. Li et al, 2022). Therefore, the demand for digital talent in the financial industry has shown a blowout growth, and focusing on the combination of financial and technological development has become a breakthrough for the innovative growth of financial talent (Zhang et al, 2018).…”