摘 要
随着金融市场的复杂性和不确定性日益增加，传统风险预测方法在处理海量非结构化数据时逐渐显现出局限性，而机器学习技术为金融风险预测提供了新的解决方案。本研究旨在探索机器学习算法在金融风险预测中的应用潜力，并构建高效的风险预测模型。研究选取了多种主流机器学习算法，包括支持向量机、随机森林、梯度提升决策树以及深度学习模型，并结合金融领域的实际需求对模型进行优化设计。通过引入特征选择与降维技术，有效提升了模型的计算效率和预测精度。实验部分以某商业银行的信贷数据为样本，验证了所构建模型在违约风险预测中的表现。结果表明，基于梯度提升决策树的模型在准确率、召回率及F1分数等指标上均优于传统统计方法，同时深度学习模型在处理高维复杂数据时展现出更强的适应能力。
关键词：金融风险预测 机器学习 梯度提升决策树
Abstract
With the increasing complexity and uncertainty of financial markets, traditional risk prediction methods gradually show limitations when dealing with massive amounts of unstructured data, and machine learning technology provides a new solution for financial risk prediction. This study aims to explore the potential of machine learning algorithms in financial risk prediction and to construct efficient risk prediction models. A variety of mainstream machine learning algorithms were selected, including support vector machine, random forest, gradient promotion decision tree and deep learning models, and the model was optimized according to the actual needs of the financial field. By introducing feature selection and dimension reduction techniques, the computational efficiency and prediction accuracy of the model are effectively improved. In the experimental part, the credit data of a commercial bank is taken as a sample to verify the performance of the constructed model in the default risk prediction. The results show that the model based on gradient improvement decision tree is better than the traditional statistical methods in accuracy, recall rate and F1 score, and the deep learning model shows stronger adaptability in processing high-dimensional complex data.
Keyword:Financial Risk Prediction Machine Learning Gradient Boosting Decision Tree
目 录
引言 1
1金融风险预测的背景与挑战 1
1.1金融风险预测的重要性 1
1.2传统方法的局限性分析 2
1.3机器学习的优势探讨 2
1.4数据驱动的风险管理需求 3
2机器学习模型在金融风险预测中的应用 3
2.1常见机器学习算法概述 3
2.2监督学习在风险预测中的作用 4
2.3非监督学习的应用场景分析 4
2.4深度学习模型的潜力探索 4
3金融数据处理与特征工程 5
3.1金融数据的特点与挑战 5
3.2数据清洗与预处理技术 5
3.3特征选择与降维方法研究 6
3.4时间序列数据的处理策略 6
4模型构建与性能评估 7
4.1模型构建的基本流程 7
4.2超参数优化与调优方法 7
4.3性能评估指标的设计与选择 8
4.4模型解释性与可解释性研究 8
结论 8
参考文献 10
致谢 11
