Financial Risk Early Warning for Enterprises Based on the CatBoost Algorithm

As the complexity of corporate financial data increases, traditional financial risk assessment methods are difficult to cope with. Machine learning methods, especially CatBoost algorithms, have become emerging technologies for financial risk prediction due to their advantages in processing category features. This paper first selected 224 non-ST companies and 112 ST companies as samples, and constructed an early warning system including 16 key financial indicators such as profitability, debt repayment ability, operating ability, and capital structure. The significant indicators were screened out through the T test, and then the financial risk early warning model was trained based on the CatBoost algorithm. The experimental results show that the prediction accuracy of the model based on the CatBoost algorithm in T-2 and T-3 years is 93.75% and 96.43% respectively. Compared with other models, the indicators perform better, proving that the model can effectively distinguish between companies with higher financial risks and normal companies. The research conclusion shows that the application of the CatBoost algorithm in corporate financial risk early warning has high prediction accuracy and can provide effective support for the financial decision-making of enterprises.