Research on Financial Data Risk Prediction Models Based on XGBoost Algorithm

With the increasing uncertainty in financial markets, financial risk prediction has become a crucial task in the financial sector. Traditional financial risk assessment methods face challenges such as high-dimensional data, non-linear complexity, and sample imbalance, requiring more efficient and accurate models to address these issues. To improve the accuracy and stability of financial risk prediction, this study proposes a financial risk prediction model based on the XGBoost algorithm.This study begins by thoroughly preprocessing the data, including filling missing values, removing outliers, and standardizing the data to ensure its quality and the stability of the model. In terms of feature selection, we utilize grid search and random search methods to fine-tune the hyperparameters of the XGBoost model, selecting the optimal parameter combinations to enhance the model's predictive accuracy. The experimental results show that the XGBoost model outperforms traditional models such as logistic regression and decision trees in terms of accuracy, precision, recall, and AUC values, particularly demonstrating superior performance in risk prediction accuracy and generalization ability. The main contribution of this study lies in the introduction of the XGBoost algorithm, which overcomes the limitations of traditional models in handling complex data and high-dimensional features, offering an efficient and precise method for financial risk prediction. Furthermore, a comparative analysis between the XGBoost model and advanced deep learning techniques, such as Deep Neural Networks (DNN), was conducted. The experimental results demonstrate the advantages of XGBoost in terms of efficiency and stability while highlighting the potential of deep learning models for more complex data scenarios.