摘 要
随着金融科技的快速发展，人工智能技术在金融风险管理中的应用已成为研究热点。本研究旨在探讨人工智能技术如何提升金融风险管理的效率与准确性，以应对日益复杂的金融市场环境。研究基于机器学习、深度学习和自然语言处理等核心技术，结合金融领域的实际需求，构建了多维度风险评估模型。通过分析海量金融数据，该模型能够实时监测市场动态、识别潜在风险并预测未来趋势。研究采用对比实验方法，将人工智能驱动的风险管理方案与传统统计方法进行性能比较，结果表明，人工智能技术显著提高了风险预警的精确度和响应速度。此外，本研究创新性地引入了图神经网络以捕捉金融网络中的复杂关系，并开发了可解释性强的风险决策支持系统，为金融机构提供了更加透明和可靠的工具。
关键词：人工智能技术 金融风险管理 多维度风险评估模型
Abstract
With the rapid development of fintech, the application of artificial intelligence technology in financial risk management has become a research hotspot. The purpose of this study is to explore how AI technology can improve the efficiency and accuracy of financial risk management to cope with the increasingly complex financial market environment. Based on the core technologies such as machine learning, deep learning and natural language processing, combined with the actual needs of the financial field, a multi-dimensional risk assessment model is constructed. By analyzing massive financial data, the model can monitor market dynamics in real time, identify potential risks and predict future trends. The study used a comparative experimental method to compare the performance of AI-driven risk management scheme with traditional statistical methods, and the results showed that AI technology significantly improved the accuracy and response speed of risk warning. Moreover, this study innovatively introduced graph neural networks to capture the complex relationships in financial networks and developed highly interpretable risk decision support systems to provide more transparent and reliable tools for financial institutions.
Keyword:Artificial Intelligence Technology Financial Risk Management Multi-Dimensional Risk Assessment Model
目 录
引言 1
1人工智能技术概述与金融风险管理背景 1
1.1人工智能技术发展现状 1
1.2金融风险管理的核心挑战 2
1.3人工智能在金融领域的适用性分析 2
1.4人工智能技术与风险管理的结合点 3
2数据驱动的风险评估模型构建 3
2.1风险数据的获取与预处理 3
2.2基于机器学习的风险评估方法 4
2.3深度学习在风险建模中的应用 4
2.4风险评估模型的性能优化策略 4
3人工智能在信用风险管理中的应用研究 5
3.1信用风险的特征与传统管理方法 5
3.2自然语言处理在信用评级中的作用 5
3.3异常检测技术在信用监控中的应用 6
3.4信用风险预测模型的案例分析 6
4人工智能在市场风险管理中的实践探索 7
4.1市场风险的动态特性与挑战 7
4.2时间序列分析在波动率预测中的应用 7
4.3强化学习在投资组合优化中的实践 8
4.4市场异常事件的智能识别方法 8
结论 8
参考文献 10
致谢 11
