摘 要：随着信息技术的迅猛发展，大数据在企业财务决策中的应用日益广泛，为企业管理创新提供了重要支撑。本研究以大数据技术为核心，探讨其在企业财务决策中的具体应用及价值挖掘路径，旨在提升企业财务决策的科学性和精准性。通过文献分析与案例研究相结合的方法，本文系统梳理了大数据技术在财务预测、风险评估和资源配置等关键环节的作用机制，并提出了一种基于大数据驱动的财务决策优化框架。研究表明，大数据技术能够显著提高财务数据处理效率，增强决策支持能力，并帮助企业识别潜在的商业机会与风险因素。此外，本文创新性地引入了多维度数据分析模型，为复杂财务场景下的决策提供了新思路。研究结论表明，大数据的应用不仅重塑了传统财务管理模式，还为企业创造了显著的竞争优势，其核心贡献在于构建了理论与实践相结合的分析体系，为企业数字化转型提供了参考依据。
关键词：大数据技术；财务决策优化；风险评估
Abstract：With the rapid development of information technology, the application of big data in corporate financial decision-making has become increasingly widespread, providing crucial support for management innovation. This study focuses on big data technology to explore its specific applications and value extraction pathways in financial decision-making, aiming to enhance the scientific rigor and precision of such decisions. By integrating literature analysis with case studies, this paper systematically examines the mechanisms through which big data technology influences key aspects of financial operations, including forecasting, risk assessment, and resource allocation. A big-data-driven fr amework for optimizing financial decision-making is proposed. The findings indicate that big data technology can substantially improve the efficiency of financial data processing, strengthen decision-support capabilities, and assist enterprises in identifying latent business opportunities and risk factors. Furthermore, this study innovatively introduces a multi-dimensional data analysis model, offering new perspectives for decision-making in complex financial scenarios. The conclusions reveal that the application of big data not only transforms traditional financial management models but also generates significant competitive advantages for enterprises. Its core contribution lies in establishing an analytical system that integrates theory with practice, providing reference for corporate digital transformation.
Keywords: Big Data Technology；Financial Decision Optimization；Risk Assessment
目 录
引言 1
一、大数据在企业财务中的应用基础 1
(一)大数据技术概述 1
(二)企业财务决策的基本框架 2
(三)大数据与财务决策的结合点 2
二、大数据驱动的企业财务分析方法 3
(一)数据采集与预处理技术 3
(二)财务预测模型构建 3
(三)风险评估的数据支持 4
三、价值挖掘在财务决策中的实现路径 4
(一)数据挖掘的核心算法应用 4
(二)企业价值创造的关键指标分析 5
(三)挖掘结果对决策的影响 5
四、大数据应用的挑战与优化策略 6
(一)技术实施中的难点分析 6
(二)数据安全与隐私保护问题 6
(三)提升大数据应用效能的建议 7
结论 7
参考文献 9
致谢 9
关键词：大数据技术；财务决策优化；风险评估
Abstract：With the rapid development of information technology, the application of big data in corporate financial decision-making has become increasingly widespread, providing crucial support for management innovation. This study focuses on big data technology to explore its specific applications and value extraction pathways in financial decision-making, aiming to enhance the scientific rigor and precision of such decisions. By integrating literature analysis with case studies, this paper systematically examines the mechanisms through which big data technology influences key aspects of financial operations, including forecasting, risk assessment, and resource allocation. A big-data-driven fr amework for optimizing financial decision-making is proposed. The findings indicate that big data technology can substantially improve the efficiency of financial data processing, strengthen decision-support capabilities, and assist enterprises in identifying latent business opportunities and risk factors. Furthermore, this study innovatively introduces a multi-dimensional data analysis model, offering new perspectives for decision-making in complex financial scenarios. The conclusions reveal that the application of big data not only transforms traditional financial management models but also generates significant competitive advantages for enterprises. Its core contribution lies in establishing an analytical system that integrates theory with practice, providing reference for corporate digital transformation.
Keywords: Big Data Technology；Financial Decision Optimization；Risk Assessment
目 录
引言 1
一、大数据在企业财务中的应用基础 1
(一)大数据技术概述 1
(二)企业财务决策的基本框架 2
(三)大数据与财务决策的结合点 2
二、大数据驱动的企业财务分析方法 3
(一)数据采集与预处理技术 3
(二)财务预测模型构建 3
(三)风险评估的数据支持 4
三、价值挖掘在财务决策中的实现路径 4
(一)数据挖掘的核心算法应用 4
(二)企业价值创造的关键指标分析 5
(三)挖掘结果对决策的影响 5
四、大数据应用的挑战与优化策略 6
(一)技术实施中的难点分析 6
(二)数据安全与隐私保护问题 6
(三)提升大数据应用效能的建议 7
结论 7
参考文献 9
致谢 9
