摘 要
随着大数据技术的快速发展，企业财务风险管理面临着前所未有的机遇与挑战。传统财务风险识别与评估方法受限于数据规模和分析能力，难以满足现代复杂经济环境下的需求。本研究旨在构建一种基于大数据技术的财务风险识别与评估模型，以提高风险预警的准确性和时效性。研究结合机器学习算法与多源异构数据处理技术，通过对企业财务报表、市场动态、行业趋势及非结构化数据的综合分析，提出了一种集成特征选择与深度学习的模型框架。该框架能够有效挖掘隐藏在海量数据中的潜在风险因素，并实现对财务风险的动态监测与量化评估。实验结果表明，所提出的模型相较于传统统计方法具有更高的预测精度和更强的泛化能力，特别是在早期风险信号捕捉方面表现突出。此外，本研究创新性地引入了时间序列分析与情境模拟技术，进一步增强了模型的适应性和解释性。总体而言，本研究不仅为财务风险管理提供了新的理论支持和技术手段，还为企业决策者制定风险应对策略提供了科学依据，对推动财务管理领域的数字化转型具有重要意义。
关键词：大数据技术；财务风险识别；深度学习；时间序列分析；情境模拟
Abstract
With the rapid development of big data technology, enterprise financial risk management is facing unprecedented opportunities and challenges. Traditional financial risk identification and evaluation methods, constrained by data scale and analytical capabilities, struggle to meet the demands of modern complex economic environments. This study aims to construct a financial risk identification and evaluation model based on big data technology to enhance the accuracy and timeliness of risk warnings. By integrating machine learning algorithms with multi-source heterogeneous data processing techniques, this research proposes a fr amework that combines feature selection and deep learning for comprehensive analysis of corporate financial statements, market dynamics, industry trends, and unstructured data. The fr amework can effectively uncover potential risk factors hidden in massive datasets and achieve dynamic monitoring and quantitative assessment of financial risks. Experimental results indicate that the proposed model demonstrates higher predictive accuracy and stronger generalization ability compared to traditional statistical methods, particularly excelling in the detection of early risk signals. Additionally, this study innovatively incorporates time series analysis and scenario simulation techniques, further enhancing the adaptability and interpretability of the model. Overall, this research not only provides new theoretical support and technical means for financial risk management but also offers scientific evidence for decision-makers in formulating risk response strategies, playing a significant role in promoting the digital transformation of the financial management domain.
Keywords: Big Data Technology; Financial Risk Identification; Deep Learning; Time Series Analysis; Scenario Simulation
目 录
1绪论 1
1.1研究背景与意义 1
1.2国内外研究现状分析 1
1.3本文研究方法与技术路线 2
2大数据在财务风险识别中的应用基础 2
2.1财务风险识别的基本概念与框架 2
2.2大数据技术在财务领域的适用性 3
2.3数据来源与预处理的关键问题 3
2.4基于大数据的财务风险特征提取 4
3财务风险评估模型的设计与构建 4
3.1模型构建的核心理论依据 5
3.2数据驱动的风险评估指标体系 5
3.3机器学习算法在模型中的应用 6
3.4模型验证与优化策略 6
3.5高效计算框架的选择与实现 7
4模型的实际应用与案例分析 7
4.1实际应用场景概述 7
4.2数据采集与处理流程 8
4.3案例分析：企业财务风险识别实践 8
4.4模型效果评估与改进方向 9
4.5应用中的挑战与未来展望 9
结论 10
参考文献 11
致 谢 12
