Enterprise credit risk indicator system.

Enterprise risk management is a key element to ensure the sustainable and steady development of enterprises. However, traditional risk management methods have certain limitations when facing complex market environments and diverse risk events. This study introduces a deep learning-based risk management model utilizing the XGBoost-CNN-BiLSTM framework to enhance the prediction and detection of risk events. This model combines the structured data processing capabilities of XGBoost, the feature extraction capabilities of CNN, and the time series processing capabilities of BiLSTM to more comprehensively capture the key characteristics of risk events. Through experimental verification on multiple data sets, our model has achieved significant advantages in key indicators such as accuracy, recall, F1 score, and AUC. For example, on the S&P 500 historical data set, our model achieved a precision rate of 93.84% and a recall rate of 95.75%, further verifying its effectiveness in predicting risk events. These experimental results fully demonstrate the robustness and superiority of our model. Our research is of great significance, not only providing a more reliable risk management method for enterprises, but also providing useful inspiration for the application of deep learning in the field of risk management.企业风险管理（Enterprise Risk Management）是保障企业可持续稳健发展的核心要素。然而，传统风险管理方法在应对复杂市场环境与多样化风险事件时存在一定局限。本研究提出一种基于深度学习的风险管理模型，采用XGBoost-CNN-BiLSTM融合架构以提升风险事件的预测与检测能力。该模型融合了极端梯度提升树（Extreme Gradient Boosting, XGBoost）的结构化数据处理能力、卷积神经网络（Convolutional Neural Network, CNN）的特征提取能力，以及双向长短期记忆网络（Bidirectional Long Short-Term Memory, BiLSTM）的时序数据处理能力，从而更全面地捕捉风险事件的关键特征。通过多数据集上的实验验证，本模型在准确率（Accuracy）、召回率（Recall）、F1分数（F1 Score）以及曲线下面积（Area Under Curve, AUC）等关键指标上均取得显著优势。例如，在标普500（S&P 500）历史数据集上，本模型的精确率达到93.84%，召回率达到95.75%，进一步验证了其在风险事件预测中的有效性。上述实验结果充分证明了本模型的稳健性与优越性。本研究具有重要意义：不仅为企业提供了更为可靠的风险管理方法，也为深度学习在风险管理领域的应用提供了有益借鉴。