摘 要
随着金融市场的日益复杂化和全球化，金融风险的准确预测成为金融机构稳健运营的关键。传统统计方法在处理非线性、高维数据时存在局限，而机器学习凭借其强大的模式识别能力为金融风险预测提供了新的思路。本研究旨在探索机器学习算法在信用风险、市场风险和操作风险预测中的应用效果，通过构建基于支持向量机、随机森林、神经网络等典型算法的风险预测模型，利用某商业银行2015 - 2020年的信贷客户数据进行实证分析。结果表明，相较于传统Logistic回归模型，机器学习模型在预测精度上平均提高了15% - 25%，其中集成学习方法表现尤为突出。研究创新性地将深度学习与传统金融理论相结合，提出了一种融合企业财务指标、交易行为特征的多维度风险评估框架，有效解决了小样本情况下的过拟合问题。此外，本研究还建立了动态风险预警系统，能够根据市场环境变化实时调整模型参数，提高预测时效性。该成果不仅为金融机构风险管理提供了有效的技术工具，也为进一步深化机器学习在金融领域的应用奠定了理论基础。
关键词：金融风险预测 机器学习 信用风险
Abstract
With the increasing complexity and globalization of financial markets, accurate prediction of financial risks has become crucial for the stable operation of financial institutions. Traditional statistical methods have limitations in handling nonlinear and high-dimensional data, while machine learning offers new approaches to financial risk prediction through its powerful pattern recognition capabilities. This study explores the effectiveness of machine learning algorithms in predicting credit risk, market risk, and operational risk. By constructing risk prediction models based on typical algorithms such as Support Vector Machines (SVM), Random Forests, and Neural Networks, and using empirical data from a commercial bank's loan customers from 2015 to 2020, the results demonstrate that machine learning models achieve an average improvement of 15% to 25% in predictive accuracy compared to traditional Logistic regression models, with ensemble learning methods showing particularly outstanding performance. Innovatively, this research integrates deep learning with traditional financial theory, proposing a multi-dimensional risk assessment fr amework that combines corporate financial indicators and transaction behavior characteristics, effectively addressing overfitting issues in small sample scenarios. Additionally, a dynamic risk warning system is established, capable of adjusting model parameters in real-time according to changes in the market environment, thereby enhancing the timeliness of predictions. These findings not only provide effective technical tools for financial institutions' risk management but also lay a theoretical foundation for further applications of machine learning in the financial sector.
Keyword:Financial Risk Prediction Machine Learning Credit Risk
目 录
1绪论 1
1.1金融风险预测的背景与意义 1
1.2机器学习在该领域的研究现状 1
1.3本文的研究方法概述 2
2机器学习算法选择与评估 2
2.1常用机器学习算法介绍 2
2.2算法性能评估指标 3
2.3适合金融风险预测的算法 3
3数据处理与特征工程 4
3.1金融数据获取与预处理 4
3.2特征选择与构建方法 5
3.3数据质量对预测的影响 5
4模型应用与风险管理 6
4.1风险预测模型构建 6
4.2模型解释性与可解释性 7
4.3实际应用案例分析 7
结论 8
参考文献 9
致谢 10
