Taking my country’s Internet finances, based on the home and abroad, combined with the characteristics of Internet financial companies, the SMOTE algorithm is used and combined with random forests to establish the financial management of Internet financial companies. Risk early warning model. Research shows that the random forest early warning model has stable recognition accuracy and good prediction performance, so it has a wide range of practical value. The improved SMOTE algorithm based on PCA can realize the equalization of unbalanced data sets and use random forest as a classifier to classify and predict geological data. Because the noise data in the original data set may cause the change of the data distribution after interpolation, it is proposed to combine the PCA algorithm and the SMOTE algorithm, first perform noise reduction and dimension reduction, and then perform data interpolation to improve the classification performance of imbalanced data sets. My country’s Internet financial listed companies conduct experiments on research samples; algorithms can better improve classification accuracy and provide new ideas for the classification and prediction of unbalanced data.
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Share and Cite
Qu, Y., Ji, P. (2025) Financial Risk Early Warning Model of Internet Financial Companies Based on SMOTE-Random Forest. Hong Kong Financial Bulletin, 1(1), 19-32. https://doi.org/10.71052/hkfb2025/VBNC9876