By Cheng Jianlong

Year 2023

Abstract

To achieve maximum shareholders’ wealth, in terms of the highest market value of common stock, financial institutions are required to improve their business processes by using information technology and large amounts of data in the form of big data. This study aimed to determine the primary aspects of internet financial intelligence that have an impact on the profitability of banks in China and contribute to the development of contemporary financial artificial intelligence to fulfill the requirements of customers.

Data samples included 27 banks in China studied for a period of 8 years during 2014-2021 from which 216 observations were made. The dependent variable used was bank profitability measured as return on equity. There were three explanatory variables: the depth index, the breadth of coverage, and the degree of intelligence. In addition, there were three control variables: leverage ratio, business size, and book-to market value ratio. This research employed three statistics for data analysis: descriptive statistics, Pearson’s correlation and Variance Inflation Factor, and multiple regression.

The findings demonstrated that bank profitability remained positive, indicating that there were no operational losses. The depth index and the breadth of coverage index were statistically significant at the .01 level, reflecting that both were major factors affecting bank profitability. The reverse relationship of the depth index and the breadth of coverage index to bank profitability implied that the study period was in the introductory stages of financial technology development for the banking sector. However, this research supported the present trend of improvements in financial technology and emphasizes the significance of responding to customer demands for the long-term future of the banking sector. The deeper online financial information usage, in terms of a greater variety of financial services and products supplied, may increase the profitability of Chinese banks in the long run.

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