摘 要：随着信息技术的迅猛发展，企业财务数据规模呈爆炸式增长，如何有效利用大数据技术进行风险预警成为学术界与实务界的关注焦点本研究以企业财务大数据分析为切入点，旨在探讨其在风险预警中的应用机制及实际效果通过整合机器学习算法与财务数据分析方法，构建了一套基于多源异构数据的风险预警模型该模型不仅涵盖了传统财务指标，还引入了非结构化数据如舆情信息和市场动态，并采用深度学习技术优化预测能力实证结果表明，相较于传统单一数据源的预警方法，本研究提出的模型在准确性和时效性方面均有显著提升，能够更早识别潜在风险并提供决策支持本研究的创新点在于将非财务数据纳入分析框架，并通过技术手段实现复杂数据的高效处理，为企业风险管理提供了新思路和实践工具这一成果对完善现代企业风险管理体系具有重要理论价值和现实意义
关键词：企业财务大数据；风险预警模型；非结构化数据
Abstract：With the rapid development of information technology, the scale of corporate financial data has grown explosively, and how to effectively utilize big data technology for risk warning has become a focal point of attention in both academia and practice. This study takes the analysis of corporate financial big data as its starting point, aiming to explore the application mechanism and practical effects of such analysis in risk warning. By integrating machine learning algorithms with financial data analysis methods, a risk warning model based on multi-source heterogeneous data is constructed. This model not only encompasses traditional financial indicators but also incorporates unstructured data, such as public sentiment information and market dynamics, while employing deep learning techniques to optimize predictive capabilities. Empirical results indicate that, compared with traditional single-data-source warning methods, the model proposed in this study demonstrates significant improvements in both accuracy and timeliness, enabling earlier identification of potential risks and providing decision support. The innovation of this research lies in incorporating non-financial data into the analytical fr amework and achieving efficient processing of complex data through technical means, thereby offering new perspectives and practical tools for corporate risk management. This achievement holds important theoretical and practical significance for improving the modern corporate risk management system.
Keywords: Enterprise Financial Big Data；Risk Warning Model；Unstructured Data
目 录
引言 1
一、企业财务大数据分析基础 1
(一)财务大数据的特征与价值 1
(二)数据采集与预处理技术 2
(三)分析方法与工具选择 2
二、风险预警体系构建研究 3
(一)风险预警的核心要素 3
(二)预警指标体系的设计 3
(三)数据驱动的模型构建 4
三、大数据分析在风险预警中的应用 4
(一)财务异常检测方法 4
(二)动态风险评估机制 5
(三)实时预警系统实现 5
四、案例分析与效果评估 6
(一)典型企业案例选取 6
(二)预警效果实证分析 6
(三)应用优化与改进建议 7
结论 7
参考文献 9
致谢 9
关键词：企业财务大数据；风险预警模型；非结构化数据
Abstract：With the rapid development of information technology, the scale of corporate financial data has grown explosively, and how to effectively utilize big data technology for risk warning has become a focal point of attention in both academia and practice. This study takes the analysis of corporate financial big data as its starting point, aiming to explore the application mechanism and practical effects of such analysis in risk warning. By integrating machine learning algorithms with financial data analysis methods, a risk warning model based on multi-source heterogeneous data is constructed. This model not only encompasses traditional financial indicators but also incorporates unstructured data, such as public sentiment information and market dynamics, while employing deep learning techniques to optimize predictive capabilities. Empirical results indicate that, compared with traditional single-data-source warning methods, the model proposed in this study demonstrates significant improvements in both accuracy and timeliness, enabling earlier identification of potential risks and providing decision support. The innovation of this research lies in incorporating non-financial data into the analytical fr amework and achieving efficient processing of complex data through technical means, thereby offering new perspectives and practical tools for corporate risk management. This achievement holds important theoretical and practical significance for improving the modern corporate risk management system.
Keywords: Enterprise Financial Big Data；Risk Warning Model；Unstructured Data
目 录
引言 1
一、企业财务大数据分析基础 1
(一)财务大数据的特征与价值 1
(二)数据采集与预处理技术 2
(三)分析方法与工具选择 2
二、风险预警体系构建研究 3
(一)风险预警的核心要素 3
(二)预警指标体系的设计 3
(三)数据驱动的模型构建 4
三、大数据分析在风险预警中的应用 4
(一)财务异常检测方法 4
(二)动态风险评估机制 5
(三)实时预警系统实现 5
四、案例分析与效果评估 6
(一)典型企业案例选取 6
(二)预警效果实证分析 6
(三)应用优化与改进建议 7
结论 7
参考文献 9
致谢 9
