Research on Enterprise Financial Risk Early Warning Model Based on Machine Learning

Enterprise financial risk forecasting is important for forward-looking decision making and long-term business management. This paper introduces an early warning approach to enterprise financial risk using machine Learning. The approach utilizes the capability of probabilistic reasoning in coping with uncertainty in financial indicators and Random Forest's ability to extract intricate nonlinear relations between enterprise financial measures. The chosen dataset, consists of organized financial information across several businesses with significant indicators like liquidity ratios, profitability ratios, debt ratios, and cash flows. The proposed model shows better accuracy and stability than traditional Random Forest models, especially in determining high-risk businesses. Experimental findings confirm the validity of the designed approach in providing timely and accuracy of 96.5%, allowing enterprises and financial regulators to implement preventive measures. The model can be extended in future studies for the inclusion of temporal financial sequences and real-time surveillance for better predictive performance.