This part proposes an artificial intelligence financial risk intelligent prevention and control scheme based on machine learning and deep learning methods, and utilizes these methods to extract information from large-scale datasets from different sources to identify financial risks and generate automatically updated risk assessment models. In addition, the model is also validated from different perspectives by combining relevant theoretical research and data analysis based on the consideration of macro-environmental factors, market micro-financial variables, and the company’s internal accounting statements; it is concluded through simulation that the risk management model under the integrated intelligent early warning mechanism has obvious advantages in terms of the accuracy rate, the response speed, and the economic cost compared with the traditional method, and that the integrated intelligent early warning mechanism has obvious advantages in the accuracy rate, response speed, and economic cost. Realize the real-time and precision of risk monitoring. This study provides some effective solution ideas and methods for the theoretical innovation in the field of financial risk management and the practical application of financial institutions in the context of digital transformation, which helps to realize the transformation from passive risk management to active risk control.
