Potential security risks in financial data, such as fraudulent transactions and illegal capital flows, pose serious threats. However, traditional risk prediction methods have problems such as low prediction accuracy and difficulty in capturing complex nonlinear relationships between data when faced with complex and changeable financial data. To solve these problems, this study applies the GBDT (Gradient Boosting Decision Tree) ensemble algorithm to the field of financial data security risk prediction. This paper collects financial data, including corporate report data, transaction flow data, network access logs and other multi-source data. Then, the collected data is cleaned to remove noise data, missing values and outliers. Feature engineering technology is then used to extract features such as financial indicator fluctuations, transaction behavior patterns, and network access frequency from the original data, and standardized processing is performed to make the data have a unified scale. During the model building process, multiple decision trees are continuously iterated and trained. Each tree learns based on the residual of the previous tree, gradually reducing the prediction error and improving the accuracy of the model. At the same time, by adjusting the algorithm parameters, the model is optimized to avoid overfitting and underfitting. The GBDT model performs well in data security risk prediction, with an average prediction time of only about 414.3 ms, and an average accuracy of 95.75% in economic loss prediction on different data sets, which is higher than the logistic regression model. This shows that the GBDT integrated algorithm can capture potential risk factors in the data, provide enterprises with real-time and efficient risk warnings, and help enterprises develop steadily in a complex and changing market environment.