This paper predicts the default behavior of the financial market, reduces the bad debt rate in bank loans and securities investment, and discovers potential risks in time. The currently used technologies mainly rely on models with static weights, such as simple linear models. The advantage of these algorithms is that they are fast. But in a large number of samples, these algorithms also face inaccurate problems, requiring the use of machine learning modeling methods to train models. This paper

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... roposes a modeling framework for financial data mining algorithms based on random forests, which can accurately predict microscopic behaviors and reduce financial risks. The experimental results show that the method proposed in this paper has certain application value, the prediction accuracy (precision) reaches 85%, and the recall rate (recall) reaches 90%.