Research on Corporate Financial Risk Prediction Model Based on Machine Learning

This study aims to explore the application effect of machine learning models in corporate financial risk prediction. By comprehensively applying various machine learning algorithms such as decision trees, random forests, and support vector machines (SVM), this paper constructs a set of financial risk prediction models and verifies and evaluates them on a dataset of actual listed companies. The study first preprocessed the data and selected features to optimize the model input quality. Then, through model training and cross-validation, the performance of different algorithms was compared, and the superior performance of the random forest model in this study was finally determined. The case analysis further demonstrates the potential of the model in practical financial risk management. Despite certain research limitations, such as the size and diversity limitations of the dataset, this study provides valuable insights into financial risk prediction and clear guidance for future research directions.