Risk Prediction of Financial Management by Macro Indicator Method

The main preventive means in the process of financial management and research of spring insurance funds. Therefore, financial risk can be used to optimize financial management. Change.In view of the limitations of the traditional multiobjective particle swarm optimization algorithm in the risk prediction of financial management, a risk prediction scheme based on the macro index method is proposed. Firstly, the influencing factors is accurately located through economic theory, and the indicators is reasonably divided to reduce interference, and the macro indicator method is used to construct a risk prediction scheme. Experimental results show that under certain evaluation criteria, the proposed scheme is superior to the traditional multi-objective particle swarm optimization algorithm in terms of risk prediction accuracy and processing time of influencing factors, and has obvious advantages. Risk prediction plays an extremely important role in financial management, which can accurately predict and optimize the growth characteristics and product generation of financial management. However, the traditional multi-objective particle swarm optimization algorithm has certain limitations in solving the risk prediction simulation problem, especially when dealing with complex problems. In this regard, this paper proposes a risk prediction scheme based on the macro indicator method to better solve this problem. The scheme accurately locates the influencing factors through economic theory, so as to determine the division of indicators, and uses the macro indicator method to construct the scheme. Experimental results show that under certain evaluation criteria, the accuracy and speed of the scheme is significantly improved for different problems, and it has better performance. Therefore, the use of a simulation scheme based on the macro index method in the risk prediction of financial management can better solve the limitations of the traditional multi-objective particle swarm optimization algorithm and improve the simulation accuracy and efficiency. If the financial risk is displayed, the accuracy of its risk prediction can be improved by about $10 \%$ on the basis of the intelligent method algorithm.