With the surge in complexity and data volume of financial markets, traditional risk assessment methods face limitations when dealing with high-dimensional, nonlinear, and dynamic data. Random Forest, as an ensemble learning algorithm, has demonstrated outstanding performance in numerous fields since its theoretical framework was proposed by Leo Breiman in 2001. By combining multiple decision trees, it significantly enhances prediction accuracy and robustness, leading to its widespread application in financial risk assessment in recent years. This paper systematically reviews the applications of Random Forest in areas such as credit risk, market risk, and operational risk, analyzing its principles, advantages, and performance. Research indicates that Random Forest outperforms traditional models (such as XGBoost and Neural Networks) in terms of prediction accuracy, feature interpretability, and resistance to overfitting, but it also faces challenges related to interpretability and computational efficiency.
Liu Yongzhi (Wed,) studied this question.