摘 要
随着信息技术的迅猛发展，大数据与人工智能技术在财务风险管理领域展现出巨大潜力。本研究旨在探讨大数据与人工智能在财务风险预测中的协同作用及其实际应用价值，以提升企业风险预警能力并优化决策支持体系。基于此目标，本文采用多源数据融合方法，结合深度学习模型与传统统计分析技术，构建了一种集成化财务风险预测框架。通过引入大规模企业运营数据、市场动态信息及宏观经济指标，该框架能够有效捕捉复杂财务行为模式，并实现对潜在风险的精准识别与量化评估。研究选取了某行业内的代表性企业作为样本，利用历史财务数据进行模型训练与验证，结果表明，相较于单一技术手段，大数据与人工智能的协同应用显著提高了预测准确率和鲁棒性。此外，本研究创新性地提出了一种动态权重调整机制，用于平衡不同数据源的重要性，从而增强了模型的适应性和泛化能力。最终结论显示，大数据与人工智能的深度融合不仅能够弥补传统方法的局限性，还为财务风险管理提供了更为全面和智能化的解决方案，为企业战略规划与风险防控提供了重要参考依据。这一研究成果对推动财务风险管理的技术革新具有重要意义，同时为相关领域的理论研究与实践探索奠定了坚实基础。
关键词：财务风险预测；大数据；人工智能；深度学习模型；动态权重调整机制
Abstract
With the rapid development of information technology, big data and artificial intelligence have demonstrated significant potential in the field of financial risk management. This study aims to explore the synergistic effects of big data and artificial intelligence in financial risk prediction and their practical application value, thereby enhancing corporate risk early warning capabilities and optimizing decision support systems. To achieve this ob jective, the paper employs a multi-source data fusion approach, integrating deep learning models with traditional statistical analysis techniques to construct an integrated financial risk prediction fr amework. By incorporating large-scale enterprise operational data, market dynamics information, and macroeconomic indicators, this fr amework effectively captures complex financial behavior patterns and achieves precise identification and quantitative assessment of potential risks. The study selects representative enterprises within a specific industry as samples, utilizing historical financial data for model training and validation. The results indicate that compared to single-technology approaches, the collaborative application of big data and artificial intelligence significantly improves prediction accuracy and robustness. Additionally, this research innovatively proposes a dynamic weight adjustment mechanism to balance the importance of different data sources, thereby enhancing the adaptability and generalization capability of the model. The final conclusion reveals that the deep integration of big data and artificial intelligence not only overcomes the limitations of traditional methods but also provides a more comprehensive and intelligent solution for financial risk management, offering critical reference for corporate strategic planning and risk prevention. This research outcome is of great significance in promoting technological innovation in financial risk management and lays a solid foundation for theoretical studies and practical explorations in related fields.
Keywords: Financial Risk Prediction; Big Data; Artificial Intelligence; Deep Learning Model; Dynamic Weight Adjustment Mechanism
目 录
1绪论 1
1.1财务风险预测的研究背景与意义 1
1.2大数据与人工智能在财务风险预测中的研究现状 1
1.3研究方法与技术路线 2
2大数据在财务风险预测中的作用分析 2
2.1数据采集与处理的技术框架 2
2.2大数据对财务风险特征的挖掘能力 3
2.3数据质量对预测模型的影响评估 3
2.4大数据驱动的风险预警机制构建 4
3人工智能在财务风险预测中的应用探索 4
3.1机器学习算法在风险识别中的优势 4
3.2深度学习模型对复杂风险模式的捕捉 5
3.3自然语言处理在非结构化数据中的应用 5
3.4人工智能模型的可解释性与优化策略 6
4大数据与人工智能的协同效应研究 7
4.1协同框架的设计与实现路径 7
4.2数据与算法融合提升预测精度的实证分析 7
4.3协同作用下的动态风险监控体系构建 8
4.4技术协同面临的挑战与解决思路 8
结论 10
参考文献 11
致 谢 12
