Advancing enterprise risk management with deep learning: A predictive approach using the XGBoost-CNN-BiLSTM model

Hui Zhang and Weihua Zhang

PLOS ONE, 2025, vol. 20, issue 4, 1-23

Abstract: 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.

Date: 2025
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Persistent link: https://EconPapers.repec.org/RePEc:plo:pone00:0319773

DOI: 10.1371/journal.pone.0319773

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