Research On The Legal Regulation Of Cross-Border Flow Of Financial Data

In the context of the rapid development of the digital economy,data plays an increasingly important role as a factor of production.The value of data is reflected through aggregation and flow,and in the modern financial industry,the aggregation and flow of data is the key to the formation of core competitiveness of financial enterprises,but it is worth noting that the domestic flow and cross-border flow of data involve privacy protection and security issues,and how to balance the freedom of data flow and data security needs to be further studied,and the thesis is divided into five parts,except for the introduction and conclusion:In the first part,the concept of data rights as well as data cross-border is discussed.Data right is a legally protected right.From the theoretical level,non-anonymous data has the dual attributes of personal and property rights,and financial data,as special data,needs to be classified and protected because of its sensitivity and professional degree.The identification of financial data cross-border scenarios needs to be understood in conjunction with the basic concepts of data cross-border,and the concepts of outbound and inbound can only be flexibly applied in practice.In the second part,we analyze the classic case of cross-border data flow,in which the listing of Di Di in the United States encountered China’s security review.Overseas listing is a means to develop international market and enterprise financing,and the cause of security review of the listing of Di Di is related to the type of data held by Di Di,and the data stored by Di Di is related to personal privacy and national security.Unlike traditional enterprises going abroad for investment in the past,the dilemma of cross-border data flow faced by emerging Internet enterprises going abroad requires both improvement of data security measures by the enterprises themselves and continuous negotiation and unification of legal regulation standards for cross-border data flow among countries.In the third part,a comparative study of data governance in the EU,the US,and Singapore is conducted on extraterritorial law.After the comparative law study,it is found that the more meaningful solutions in data cross-border governance mainly include data protection officer system,data whitelist model,data hierarchy protection system and data standard contract.Four specific measures can provide clear ideas for China’s financial data cross-border governance in terms of legal framework building for data cross-border flow,compliance personnel training and data exit review facilitation.In the fourth part,on the basis of sorting out China’s existing laws and regulations on general types of data and financial data,the existing problems in the governance of cross-border flow of financial data are pointed out.China has been accelerating the pace of data legislation in recent years,but the following problems remain to be solved: first,the laws and regulations on cross-border flow of financial data fail to reflect the industry specificity of financial information and data protection,but stipulate some principles in a general way;second,the setting of financial data cross-border authorities is unreasonable;third,China’s talent training system for data protection officers is not yet sound;fourth,the data cross-border risk There is still room for progress in automatic identification technology.In the fifth part,the specific measures of data cross-border governance based on extraterritorial law research are combined with the actual situation in China,and the ways to optimize the existing problems of financial data crossborder flow governance are proposed.The data entry and exit procedures can be simplified through the model of data whitelist,and the rights and obligations of data controllers and processors can be clarified through the establishment of data standard contracts;the qualification and professional threshold of data protection officers can be introduced into international standards;the improvement of automatic data cross-border risk identification technology can be improved with the help of artificial intelligence,relying on computer deep learning to continuously improve the data cross-border supervision system,and the key industries can be The classification of data risk coefficients in the financial industry can be graded,and the cross-border flow of data can be realized under the premise of ensuring data security.