Abstract:Driven by the goal of becoming a financial power and artificial intelligence technology, digital financial platforms have rapidly emerged and deeply applied algorithm technology to the traditional financial industry. As a fusion product of digital financial and platform economy, the digital financial platform engages in collaborative activities with financial institutions by exporting technology or providing scenarios, and has natural technological preferences and financial attributes, so it effectively promotes the digitalization and inclusiveness of financial products and services. However, everything has two sides, not the result of either one or the other, but the product of both advantages and disadvantages. Through the use of case analysis methods, it is found that when algorithm technology is deeply applied to digital financial platforms, there is always a certain degree of error or uncertainty (algorithmic black box), which has systemic risk characteristics different from traditional enterprises, such as being too big to fail, too much to fail, and too strong to fail. In addition, algorithmic black box risk is formed in the third-party systems (platform side, user side, and regulatory side), including the risk of platform fog which hinders the stable development of the platform end, technical masking risk which aggravates risk defects in the user end, and regulatory vacuum risk which constrains the governance of public authority at the regulatory end. Therefore, in order to achieve the goal of becoming a financial power and prevent and resolve the systemic risks of the algorithm black box in digital financial platforms, based on the theory of tripartite control and the system governance experience of international algorithmic black box risk, China should build a system governance mechanism for the algorithm black box in digital financial platforms from the tripartite system (platform, user, and regulatory end).