摘 要
信用风险作为金融体系中最为基础且关键的风险类型之一，其防控策略的研究对维护金融市场稳定具有重要意义。本文以近年来全球经济不确定性加剧、金融机构风险管理压力上升为背景，旨在探讨信用风险防控的优化路径及其实践效果。研究综合运用文献分析、案例研究与实证分析方法，选取多家代表性金融机构的数据进行深入剖析，揭示现有防控机制中的不足，并提出基于大数据技术与人工智能算法的新型防控框架。研究表明，通过引入机器学习模型预测违约概率，结合多维度数据评估借款人信用状况，可显著提升风险识别的精准度与效率。此外，研究还发现强化内部控制流程与外部监管协同的重要性。本文的主要创新点在于将前沿技术与传统风控手段有机结合，构建了动态化、智能化的信用风险防控体系，为金融机构提供了可操作性强的实践指南。研究成果不仅有助于降低信用风险事件的发生概率，还为相关领域的理论发展提供了新视角，对推动金融行业的稳健运行具有重要参考价值。
关键词：信用风险防控；大数据技术；人工智能算法；机器学习模型；内部控制与监管协同
Analysis of Credit Risk Prevention and Control Strategies and Practices
Abstract: Credit risk, as one of the most fundamental and critical types of risk in the financial system, plays a significant role in maintaining the stability of financial markets, making the study of its prevention and control strategies essential. Against the backdrop of increasing global economic uncertainty and rising pressure on financial institutions' risk management in recent years, this paper aims to explore the optimized pathways for credit risk prevention and their practical effectiveness. By employing a comprehensive approach that integrates literature analysis, case studies, and empirical analysis, the research examines data from multiple representative financial institutions to uncover deficiencies in existing preventive mechanisms and proposes a novel fr amework based on big data technology and artificial intelligence algorithms. The findings indicate that the introduction of machine learning models for predicting default probabilities, coupled with multidimensional data assessments of borrowers' creditworthiness, can substantially enhance the accuracy and efficiency of risk identification. Furthermore, the study highlights the importance of strengthening internal control processes and fostering collaboration with external regulatory fr ameworks. A key innovation of this paper lies in the organic integration of cutting-edge technologies with traditional risk control methods, establishing a dynamic and intelligent credit risk prevention system that offers financial institutions a highly actionable practical guide. The research not only contributes to reducing the likelihood of credit risk events but also provides new perspectives for theoretical development in related fields, offering valuable insights for promoting the stable operation of the financial industry.
Keywords: Credit Risk Prevention And Control; Big Data Technology; Artificial Intelligence Algorithm; Machine Learning Model; Internal Control And Supervision Coordination
目 录
一、绪论 1
（一）信用风险防控的研究背景与意义 1
（二）国内外信用风险防控研究现状 1
（三）本文研究方法与技术路线 2
二、信用风险防控的理论基础与框架构建 2
（一）信用风险的基本概念与特征分析 2
（二）信用风险管理的核心理论框架 2
（三）信用风险防控的主要策略分类 3
（四）理论框架在实践中的应用价值 3
三、信用风险防控的关键策略分析 4
（一）数据驱动的风险评估策略 4
（二）基于模型的信用风险预警机制 5
（三）多维度信用风险控制措施设计 5
（四）风险防控策略的实施路径优化 6
四、信用风险防控的实践案例与效果评价 6
（一）实践案例的选择与背景介绍 6
（二）案例中信用风险防控的具体实施 7
（三）防控效果的定量与定性评价 7
（四）实践经验总结与改进建议 8
结论 8
参考文献 9
致 谢 10
