摘 要：随着全球经济环境的不确定性加剧，企业财务风险管理成为保障可持续发展的重要课题，其中信用风险评估与控制作为核心环节备受关注。本研究旨在探讨企业信用风险的形成机制及其管理策略，通过构建基于多维指标体系的信用风险评估模型，结合定性与定量分析方法，为企业提供科学的风险管理工具。研究采用实证分析与案例研究相结合的方式，选取典型行业样本数据进行验证，发现传统信用评估方法在动态性和精准性方面存在不足，而引入大数据技术和机器学习算法可显著提升预测能力。研究表明，通过优化信用风险评估流程并实施分级管控措施，能够有效降低违约概率，提高资金使用效率。本研究的创新点在于将智能化技术融入信用风险管理框架，并提出适应复杂经济环境的动态调整机制，为相关理论研究和实践应用提供了重要参考。
关键词：信用风险评估；智能化技术；多维指标体系
Abstract：As the uncertainty in the global economic environment intensifies, enterprise financial risk management has become a crucial issue for ensuring sustainable development, with credit risk assessment and control receiving particular attention as a core component. This study aims to explore the formation mechanism of corporate credit risk and its management strategies by constructing a credit risk evaluation model based on a multidimensional indicator system, integrating both qualitative and quantitative analytical methods to provide enterprises with scientific risk management tools. The research combines empirical analysis with case studies, selecting sample data from typical industries for validation, revealing that traditional credit assessment methods are insufficient in terms of dynamism and accuracy. The introduction of big data technology and machine learning algorithms, however, significantly enhances predictive capabilities. The findings indicate that optimizing the credit risk assessment process and implementing tiered control measures can effectively reduce default probabilities and improve the efficiency of capital utilization. The innovation of this study lies in incorporating intelligent technologies into the fr amework of credit risk management and proposing a dynamic adjustment mechanism adaptable to complex economic environments, offering significant references for both theoretical research and practical applications in this field.
Keywords: Credit Risk Assessment；Intelligent Technology；Multi-Dimensional Index System
目 录
引言 1
一、信用风险评估的理论基础 1
(一)信用风险的基本概念 1
(二)信用风险评估的核心方法 2
(三)理论框架在企业财务中的应用 2
二、企业信用风险评估的关键要素 3
(一)财务指标与信用风险的关系 3
(二)非财务因素对信用风险的影响 3
(三)关键要素的综合分析框架 4
三、信用风险控制的策略与实践 4
(一)企业信用风险管理的目标设定 4
(二)基于数据驱动的风险控制模型 5
(三)实践案例与经验总结 5
四、信用风险评估与控制的优化路径 6
(一)技术创新在信用风险管理中的作用 6
(二)信用风险评估体系的改进方向 6
(三)未来研究展望与挑战 7
结论 7
参考文献 9
致谢 9
关键词：信用风险评估；智能化技术；多维指标体系
Abstract：As the uncertainty in the global economic environment intensifies, enterprise financial risk management has become a crucial issue for ensuring sustainable development, with credit risk assessment and control receiving particular attention as a core component. This study aims to explore the formation mechanism of corporate credit risk and its management strategies by constructing a credit risk evaluation model based on a multidimensional indicator system, integrating both qualitative and quantitative analytical methods to provide enterprises with scientific risk management tools. The research combines empirical analysis with case studies, selecting sample data from typical industries for validation, revealing that traditional credit assessment methods are insufficient in terms of dynamism and accuracy. The introduction of big data technology and machine learning algorithms, however, significantly enhances predictive capabilities. The findings indicate that optimizing the credit risk assessment process and implementing tiered control measures can effectively reduce default probabilities and improve the efficiency of capital utilization. The innovation of this study lies in incorporating intelligent technologies into the fr amework of credit risk management and proposing a dynamic adjustment mechanism adaptable to complex economic environments, offering significant references for both theoretical research and practical applications in this field.
Keywords: Credit Risk Assessment；Intelligent Technology；Multi-Dimensional Index System
目 录
引言 1
一、信用风险评估的理论基础 1
(一)信用风险的基本概念 1
(二)信用风险评估的核心方法 2
(三)理论框架在企业财务中的应用 2
二、企业信用风险评估的关键要素 3
(一)财务指标与信用风险的关系 3
(二)非财务因素对信用风险的影响 3
(三)关键要素的综合分析框架 4
三、信用风险控制的策略与实践 4
(一)企业信用风险管理的目标设定 4
(二)基于数据驱动的风险控制模型 5
(三)实践案例与经验总结 5
四、信用风险评估与控制的优化路径 6
(一)技术创新在信用风险管理中的作用 6
(二)信用风险评估体系的改进方向 6
(三)未来研究展望与挑战 7
结论 7
参考文献 9
致谢 9
