通过深度学习推进企业风险管理:使用XGBoost-CNN-BiLSTM模型的预测方法
1Department of Management, Zhengzhou Business University, Zhengzhou, China.
PloS one
|April 3, 2025
概括
本研究引入了一种用于企业风险管理的新型深度学习模型,大大改善了风险事件的预测和检测. XGBoost-CNN-BiLSTM框架为可持续的业务发展提供了一个强大的解决方案.
科学领域:
- 人工智能的人工智能
- 金融风险管理 金融风险管理
- 机器学习 机器学习
背景情况:
- 传统的企业风险管理在复杂的市场中面临着局限性.
- 不同的风险事件需要先进的检测和预测方法.
- 可持续发展取决于有效的风险减轻策略.
研究的目的:
- 引入基于深度学习的风险管理模型.
- 提高企业风险事件的预测和检测.
- 为了解决传统风险管理方法的局限性.
主要方法:
- 使用了一个新的XGBoost-CNN-BiLSTM框架.
- 组合XGBoost用于结构化数据,CNN用于特征提取,BiLSTM用于时间序列分析.
- 在多个数据集上验证模型,包括标准普尔500指数的历史数据.
主要成果:
- 在准确性,回忆力,F1分数和AUC方面取得了显著的优势.
- 在标准普尔500指数数据集上显示了高精度 (93.84%) 和回忆 (95.75%).
- 验证了模型在风险事件预测方面的强度和优势.
结论:
- XGBoost-CNN-BiLSTM模型为企业风险管理提供了更可靠的方法.
- 深度学习为推进风险管理实践提供了巨大的潜力.
- 该研究为在金融风险评估中应用AI提供了有价值的见解.
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