Research on Financial Audit Risk Prediction of State-owned Enterprises Based on Momentum Factor Improved BP Neural Network

In the context of current economic globalization and rapid development of capital markets, state-owned enterprises, as important pillars of the national economy, have a profound impact on market confidence due to their financial health and stable operation. Financial audit risk assessment, as a crucial financial management tool, plays a crucial role in identifying and preventing potential financial issues. Traditional financial audit risk assessment methods are limited by the static and subjective nature of the model, making it difficult to adapt to the dynamic changes in the market. Therefore, this study proposes a BP neural network model based on momentum factor improvement, aiming to optimize the accuracy and efficiency of financial audit risk assessment in state-owned enterprises. By introducing the momentum factor, the model can better handle the local minimum problem during the training process, improve convergence speed, and thus enhance the predictive ability of the model. Through empirical analysis, it has been verified that the BP neural network model based on momentum factor improvement has certain practical value and efficiency in the field of financial audit risk assessment.