Enterprise Financial Risk Warning Based on Random Forest Algorithm

In the current complex and changing economic environment, enterprises are facing many financial risks, such as broken capital chain and declining profitability, which pose a serious threat to the survival and development of enterprises. Therefore, it is particularly important to establish an accurate and efficient financial risk warning system. This study selected the financial data of A-share listed companies in Shanghai and Shenzhen Stock Exchanges in the past five years as samples, covering multi-dimensional financial indicators and non-financial indicators. Through data preprocessing, feature selection, model training and testing, a financial risk warning model was constructed. The experimental results show that the financial risk warning model based on random forest performs well in evaluation indicators such as accuracy, recall rate and F1 score, and performs well in identifying enterprises that are about to fall into financial difficulties. This study not only verifies the feasibility and superiority of random forest algorithm in enterprise financial risk warning, but also provides new ideas and methods for further exploring intelligent financial risk management. Future research can be based on this, combined with more non-financial information and deep learning technology to further improve the accuracy and practicality of the warning system.