Application of GBDT Integrated Algorithm in Financial Data Security Risk Prediction

Financial data is usually high-dimensional and contains a large amount of information such as financial indicators, transaction records, and personnel permissions. Traditional machine learning models are susceptible to the "dimensionality disaster" when processing these high-dimensional data, resulting in a decline in model performance. To this end, this paper proposes an application method based on the GBDT (gradient boosting decision tree) algorithm for financial data security risk prediction. First, through data preprocessing steps, including missing value processing, outlier detection, and feature engineering, the original financial data is cleaned and optimized to provide high-quality data input for model training. In the feature selection stage, GBDT automatically evaluates the contribution of each feature to the prediction result through the built-in feature importance evaluation function, helping to screen the most important features. During the training process, the GBDT algorithm trains multiple decision trees through multiple iterations, gradually minimizing the prediction error and improving the prediction accuracy of the model. After completing the model training, the GBDT model can output the prediction results and judge the security risks of the data by setting thresholds. Finally, in the experiment, the prediction accuracy of the model is significantly higher than that of traditional statistical methods, and it has strong robustness to outliers and noise data. By setting the risk threshold, the model can better judge whether there are potential security risks in financial data and meet the needs of practical applications.