摘 要
随着信息技术的迅猛发展，大数据环境为企业财务风险管理带来了前所未有的机遇与挑战。传统财务风险管理模式在数据处理能力、风险识别精准度等方面逐渐难以满足现代企业需求，亟需创新以适应新的发展形势。本研究旨在探索大数据环境下企业财务风险管理创新路径，通过构建基于大数据分析的企业财务风险预警模型，融合机器学习算法与财务指标体系，实现对财务风险的动态监测与实时预警。采用文献研究法梳理国内外相关理论成果，运用案例分析法选取典型企业进行实证研究，结合问卷调查获取一手数据，从多维度剖析大数据技术对企业财务风险管理的影响机制。研究发现，大数据技术能够显著提升企业财务风险识别效率与准确性，降低信息不对称程度，优化资源配置。
关键词：大数据环境 财务风险管理 风险预警模型
Abstract
With the rapid development of information technology, the big data environment has brought unprecedented opportunities and challenges to enterprise financial risk management. The traditional financial risk management mode is gradually difficult to meet the needs of modern enterprises in terms of data processing ability and risk identification accuracy, so it is urgent for innovation to adapt to the new development situation. This research aims to explore the innovative path of enterprise financial risk management in the environment of big data. To realize the dynamic monitoring and real-time early warning of financial risks by constructing the enterprise financial risk early warning model based on big data analysis, integrating machine learning algorithm and financial index system. The literature research method is used to sort out the relevant theoretical achievements at home and abroad, the case analysis method is used to select typical enterprises for empirical research, the first-hand data is obtained with questionnaire survey, and the influence mechanism of big data technology on enterprise financial risk management is analyzed from multiple dimensions. It is found that big data technology can significantly improve the efficiency and accuracy of enterprise financial risk identification, reduce the degree of information asymmetry, and optimize resource allocation.
Keyword:Big Data Environment Financial Risk Management Risk Warning Model
目 录
1绪论 1
1.1研究背景 1
1.2研究意义与价值分析 1
1.3国内外研究现状综述 1
1.4本文研究方法概述 2
2大数据对企业财务风险的影响 2
2.1数据驱动的风险识别变化 2
2.2风险评估模型的革新 3
2.3风险预警机制的优化 3
3基于大数据的财务风险管理创新 4
3.1创新理念与理论基础 4
3.2技术手段的应用探索 5
3.3管理模式的转型路径 5
4大数据环境下风险管理实践 6
4.1实证案例分析研究 6
4.2实施效果评估体系 7
4.3应对挑战与改进建议 7
结论 8
参考文献 9
致谢 10
随着信息技术的迅猛发展，大数据环境为企业财务风险管理带来了前所未有的机遇与挑战。传统财务风险管理模式在数据处理能力、风险识别精准度等方面逐渐难以满足现代企业需求，亟需创新以适应新的发展形势。本研究旨在探索大数据环境下企业财务风险管理创新路径，通过构建基于大数据分析的企业财务风险预警模型，融合机器学习算法与财务指标体系，实现对财务风险的动态监测与实时预警。采用文献研究法梳理国内外相关理论成果，运用案例分析法选取典型企业进行实证研究，结合问卷调查获取一手数据，从多维度剖析大数据技术对企业财务风险管理的影响机制。研究发现，大数据技术能够显著提升企业财务风险识别效率与准确性，降低信息不对称程度，优化资源配置。
关键词：大数据环境 财务风险管理 风险预警模型
Abstract
With the rapid development of information technology, the big data environment has brought unprecedented opportunities and challenges to enterprise financial risk management. The traditional financial risk management mode is gradually difficult to meet the needs of modern enterprises in terms of data processing ability and risk identification accuracy, so it is urgent for innovation to adapt to the new development situation. This research aims to explore the innovative path of enterprise financial risk management in the environment of big data. To realize the dynamic monitoring and real-time early warning of financial risks by constructing the enterprise financial risk early warning model based on big data analysis, integrating machine learning algorithm and financial index system. The literature research method is used to sort out the relevant theoretical achievements at home and abroad, the case analysis method is used to select typical enterprises for empirical research, the first-hand data is obtained with questionnaire survey, and the influence mechanism of big data technology on enterprise financial risk management is analyzed from multiple dimensions. It is found that big data technology can significantly improve the efficiency and accuracy of enterprise financial risk identification, reduce the degree of information asymmetry, and optimize resource allocation.
Keyword:Big Data Environment Financial Risk Management Risk Warning Model
目 录
1绪论 1
1.1研究背景 1
1.2研究意义与价值分析 1
1.3国内外研究现状综述 1
1.4本文研究方法概述 2
2大数据对企业财务风险的影响 2
2.1数据驱动的风险识别变化 2
2.2风险评估模型的革新 3
2.3风险预警机制的优化 3
3基于大数据的财务风险管理创新 4
3.1创新理念与理论基础 4
3.2技术手段的应用探索 5
3.3管理模式的转型路径 5
4大数据环境下风险管理实践 6
4.1实证案例分析研究 6
4.2实施效果评估体系 7
4.3应对挑战与改进建议 7
结论 8
参考文献 9
致谢 10
