In recent years, artificial intelligence technology has rapidly penetrated various industries, including the financial industry. Many financial institutions have begun to attempt to apply artificial intelligence technology to the field of risk prevention and control, using technological innovation to prevent financial risks. Currently, China has made active attempts and explorations in the field of "artificial intelligence+risk control", which has a certain first mover advantage compared to its peers in the international financial industry. At the World Artificial Intelligence Conference in July 2023, Tencent released a large-scale financial risk control model to the public. In November of the same year, Tencent, together with research institutions such as the China Academy of Information and Communications Technology, the University of Science and Technology of China, Nanyang Technological University in Singapore, Zhongyuan Consumer Finance, and WeBank, jointly developed the world's first international standard for large-scale financial risk control models. What will artificial intelligence technology bring to financial risk control? In theory, artificial intelligence empowers risk control, reduces human errors and interference, and can improve the efficiency and accuracy of risk identification. However, considering that artificial intelligence technology is still a developing and immature new thing, hasty promotion in the field of financial risk control may bring new risks. The most concerning is the risk of data leakage. At present, many financial institutions choose to cooperate with technology companies with artificial intelligence technology in the field of risk control, which often involves data sharing. Artificial intelligence large models rely on a large amount of sample data for training, and the scale and quality of the data have a crucial impact on the accuracy of risk control. In theory, the richer the data, the stronger the ability of large models to accurately depict users, and the higher the accuracy of identifying risks in credit approval and other aspects. However, with more and more data being shared, whether privacy can be effectively protected has become a new risk challenge. It is worth emphasizing that financial data not only has the general characteristics of data, but also includes important content such as national account information and enterprise fund flows. This means that once financial data is leaked, it may bring greater risks than general data leakage. In addition to data leakage, legal risks cannot be ignored. From a historical perspective, the revision of laws and regulations often lags behind the application of new technologies. At present, artificial intelligence technology still has the possibility of generating false content due to data and algorithm errors, and to some extent, it causes user discrimination. Once an inaccurate financial risk control report is generated by a large model, it will be difficult to distinguish whether the technology provided by technology companies is unreliable or the data provided by financial institutions is unreliable. This makes it difficult to define legal responsibilities, and it is easy for financial institutions and technology companies to shift blame and buck each other. In the process of arguing, reasonable demands such as loan approval from customers may be delayed, and the risk will ultimately be borne by the customer. The Central Financial Work Conference proposed to comprehensively strengthen financial supervision and effectively prevent and resolve financial risks. In response to the new risks that may arise from the application of artificial intelligence technology in the financial industry, on the one hand, it is necessary to improve laws and regulations, safeguard the right of individuals to reasonably question the results generated by artificial intelligence technology, and ensure that artificial intelligence technology is constrained by accountability mechanisms and principles such as transparency, fairness, and security; On the other hand, it is necessary to effectively manage financial data information, steadily and cautiously promote the application of artificial intelligence technology, continuously improve risk control technology, and enhance risk management and forecasting