摘 要
在大数据技术迅猛发展的背景下，企业信用风险评估面临数据规模庞大、维度复杂及实时性要求高等新挑战，传统评估方法难以满足当前需求。为此，本研究旨在探索基于大数据技术的企业信用风险评估方法创新，以提升评估的精准性和时效性。研究综合运用机器学习算法、数据挖掘技术和自然语言处理等手段，构建了一种融合多源异构数据的信用风险评估框架，该框架能够有效整合财务数据、交易记录、舆情信息及网络行为等多种数据源，并通过深度学习模型对非结构化数据进行特征提取与分析。实验结果表明，相较于传统方法，所提方法在预测准确率和风险识别能力方面均有显著提升，特别是在早期预警和动态监控方面表现出更强的优势。此外，研究还提出了一种基于图神经网络的关系风险传播模型，用于揭示企业间隐性关联及其对信用风险的影响机制，这一创新为系统性风险防控提供了新思路。总体而言，本研究不仅拓展了大数据技术在信用风险管理领域的应用边界，还为企业信用评估体系的优化升级提供了理论支持和技术保障，具有重要的实践意义和推广应用价值。
关键词：企业信用风险评估；大数据技术；机器学习；图神经网络；关系风险传播模型
Abstract
In the context of the rapid development of big data technology, enterprise credit risk assessment is confronted with new challenges such as large-scale data, complex dimensions, and high requirements for real-time performance, which traditional assessment methods struggle to meet. To address these issues, this study aims to explore innovations in enterprise credit risk assessment based on big data technology to enhance both accuracy and timeliness. By integrating machine learning algorithms, data mining techniques, and natural language processing, a credit risk assessment fr amework that incorporates multi-source heterogeneous data is constructed. This fr amework effectively consolidates various data sources, including financial data, transaction records, public sentiment information, and online behavior, while utilizing deep learning models for feature extraction and analysis of unstructured data. Experimental results demonstrate that the proposed method significantly outperforms traditional approaches in terms of predictive accuracy and risk identification capability, particularly excelling in early warning and dynamic monitoring. Furthermore, this study introduces a relationship risk propagation model based on graph neural networks to uncover implicit inter-enterprise connections and their impact mechanisms on credit risk, offering novel insights for systemic risk prevention and control. Overall, this research not only expands the application boundaries of big data technology in the field of credit risk management but also provides theoretical support and technical assurance for the optimization and upgrading of enterprise credit evaluation systems, thereby holding significant practical implications and potential for widespread application.
Keywords: Enterprise Credit Risk Assessment; Big Data Technology; Machine Learning; Graph Neural Network; Relationship Risk Propagation Model
目 录
1绪论 1
1.1大数据与企业信用风险评估的背景分析 1
1.2企业信用风险评估方法创新的意义探讨 1
1.3国内外研究现状与发展趋势综述 1
1.4本文研究方法与技术路线设计 2
2大数据在信用风险评估中的应用基础 2
2.1大数据技术对企业信用评估的影响机制 2
2.2数据来源与信用风险评估的关键要素分析 3
2.3数据质量对评估模型性能的作用研究 3
2.4大数据驱动的企业信用信息整合框架构建 4
2.5应用基础研究的挑战与解决方案探讨 4
3企业信用风险评估方法的创新路径 5
3.1基于机器学习的信用风险预测模型优化 5
3.2非结构化数据在信用评估中的应用探索 5
3.3动态监测技术在信用风险管理中的实践 6
3.4跨领域数据融合提升评估准确性的研究 6
3.5方法创新对企业决策支持的价值分析 7
4创新方法的实际应用与效果验证 7
4.1实证案例：大数据驱动的信用评估流程设计 7
4.2数据驱动模型在中小企业信用评估中的应用 8
4.3创新方法对企业信用评级准确性的影响分析 8
4.4实际应用中面临的主要问题及解决策略 9
4.5方法创新的社会经济效益评估与展望 9
结论 11
参考文献 12
致 谢 13
