Early warning of Enterprise financial risk based on stochastic forest model

In order to solve the realistic problems of non-balance data in modern enterprise financial risk early warning, such as lack of accuracy of minority class identification, this paper uses the random forest algorithm as the technical route to put forward an improved early warning model that integrates the oversampling technique and the random forest algorithm. By constructing the double random mechanism and SMOTE algorithm, a few class samples were generated to effectively alleviate the influence of the imbalance of class distribution on classification performance. Taking technology financial listed companies as the research object, a total of 752 sets of quarterly financial data of 3 ST enterprises and 60 normal enterprises were selected. The empirical results show that the improved model has an early warning accuracy of 76.43% for ST enterprises, which is 30.25% higher than that of unbalanced data scenario, and the overall accuracy is 97.36%. Through feature importance analysis, it is found that net profit rate on sales, net asset value per share and net asset growth rate have the highest contribution to risk identification. The stability test results show that the reliability score of the proposed model in 10 rounds of high concurrent stress tests is 0.88, which is significantly better than the traditional statistical method. The study confirmed that SMOTE- Random Forest model can significantly improve the sensitivity and generalization ability of financial risk early warning through data balancing and dynamic time series feature extraction, and provide theoretical support for risk prevention and control in high volatility industry. Future research will focus on model parallelization acceleration and quantitative modeling of user response satisfaction to enhance engineering applicability.