Fintech’s regulatory challenges and dimensional construction -Part 7

The key to financial supervision is to take data as the foundation, enrich the diversified methods of data supervision, and propose scientific and technological governance plans, from rule and principle governance to scientific and technological governance. The core concept is transparency, equality, and intelligence, so as to build a real-time and dynamic supervision system. Specifically, the use of data in a technology-driven regulatory model includes the following four progressive processes:

First, data collection and data access, that is, the acquisition of data and the identification of the authenticity of the data.

Second, data sharing. That is to say, the sharing of data between or within the supervisory level, industry associations, and consumers. Data sharing is the basis of integrated financial supervision.

Third, data analysis and decision-making. The development of the financial market increasingly relies on data. Data analysis is becoming more and more important for financial services and financial institutions. With the help of complete data sets and effective data analysis, Fintech companies can better make credit ratings for actual risks and provide "personalized" financial services at a low cost per customer.

Fourth, supervise the implementation. Technological supervision is not only to obtain and analyze data, but also to use technological means to allow the supervisor to accept the supervision of the supervisor in real-time, and even withdraw from the market and be punished when necessary.

In summary, in the era of Fintech, data and information are the core of supervision. Financial supervision is increasingly driven by data, and regulatory agencies require data to be more granular and more frequent. The importance of data determines that legal and effective methods must be used to realize data collection and in-depth mining to promote effective supervision.