摘 要
随着经济环境的复杂化和市场竞争的加剧，企业面临的财务风险日益多样化和隐蔽化，传统的财务风险管理方法已难以满足现代企业的需求。为此，本研究旨在构建一种高效、精准的企业财务风险预警模型，并探讨其在实际中的应用价值。研究以财务风险识别与防控为核心目标，结合大数据分析技术与机器学习算法，提出了一种基于多源数据融合的预警模型框架。该模型通过整合财务报表数据、市场动态信息及非结构化数据，利用支持向量机（SVM）和随机森林（RF）等算法进行风险评估与预测，显著提升了预警的准确性和时效性。实证研究结果表明，相较于传统单一指标分析方法，本模型能够更全面地捕捉企业潜在的财务风险特征，并实现对高风险事件的提前预警。此外，研究还验证了非财务数据在财务风险预警中的重要性，为模型的进一步优化提供了理论依据。本研究的主要创新点在于突破了传统财务预警模型的数据局限性，引入多维度数据源并结合先进算法，为企业财务风险管理提供了新的思路和技术支持。研究成果不仅有助于提升企业的风险防范能力，也为相关领域的学术研究奠定了基础。
关键词：财务风险预警；多源数据融合；支持向量机；随机森林；非财务数据
Abstract
With the increasing complexity of the economic environment and intensifying market competition, the financial risks faced by enterprises are becoming more diversified and concealed. Traditional financial risk management methods are no longer sufficient to meet the demands of modern enterprises. To address this challenge, this study aims to construct an efficient and precise early warning model for enterprise financial risks and explore its practical application value. Focusing on the identification and prevention of financial risks, the study integrates big data analytics with machine learning algorithms to propose a warning model fr amework based on multi-source data fusion. By consolidating financial statement data, market dynamics information, and unstructured data, the model employs algorithms such as Support Vector Machine (SVM) and Random Forest (RF) for risk assessment and prediction, thereby significantly enhancing the accuracy and timeliness of warnings. Empirical research results indicate that compared to traditional single-indicator analysis methods, this model can comprehensively capture potential financial risk characteristics of enterprises and provide early warnings for high-risk events. Additionally, the study validates the importance of non-financial data in financial risk warnings, offering theoretical support for further model optimization. The primary innovation of this research lies in overcoming the data limitations of traditional financial warning models by incorporating multi-dimensional data sources and advanced algorithms, thus providing new perspectives and technical support for enterprise financial risk management. The research outcomes not only contribute to enhancing enterprises' risk prevention capabilities but also lay a foundation for academic studies in related fields.
Keywords: Financial Risk Warning; Multi-Source Data Fusion; Support Vector Machine; Random Forest; Non-Financial Data
目 录
1绪论 1
1.1企业财务风险预警研究背景与意义 1
1.2国内外财务风险预警研究现状分析 1
1.3本文研究方法与技术路线设计 2
2财务风险预警模型理论基础 2
2.1财务风险的内涵与分类研究 2
2.2预警模型构建的核心理论支撑 3
2.3数据驱动的财务风险预警机制分析 3
2.4常见预警模型的优缺点对比研究 4
3财务风险预警模型构建方法 4
3.1模型构建的数据来源与预处理方法 4
3.2关键财务指标体系的设计与筛选 5
3.3基于机器学习的预警模型算法选择 5
3.4模型参数优化与性能评估标准设定 6
3.5实时动态预警系统的架构设计 6
4财务风险预警模型应用案例分析 7
4.1案例企业财务数据的收集与整理 7
4.2警示信号识别与风险等级划分实践 7
4.3预警模型在实际场景中的效果验证 8
4.4模型应用中的问题与改进策略探讨 8
4.5财务风险管理对策建议与启示 9
结论 10
参考文献 11
致 谢 12
