摘要
随着我国金融市场的深化发展和对外开放程度的不断提高，商业银行面临的信用风险日益复杂多变，准确度量和有效管理信用风险成为保障银行稳健运营的关键。本研究旨在构建适应我国国情的商业银行信用风险度量与管理体系，综合运用数理统计、计量经济学等方法，基于大量商业银行信贷数据，深入分析影响信用风险的主要因素，创新性地引入机器学习算法优化传统风险度量模型，提高风险预测精度。研究发现，在宏观经济环境、行业特征等因素共同作用下，不同规模和发展阶段的商业银行信用风险表现存在显著差异；通过实证检验表明，改进后的模型在风险识别能力方面有明显提升，能够更精准地反映信用风险状况。本研究不仅为商业银行提供了科学有效的信用风险管理工具，还为监管部门制定相关政策提供理论依据，对促进我国银行业健康发展具有重要现实意义。
关键词：信用风险度量；商业银行；机器学习；风险管理；宏观经济环境
Abstract
With the deepening development of China's financial markets and increasing openness to the outside world, commercial banks are confronted with increasingly complex and volatile credit risks. Accurately measuring and effectively managing these risks has become crucial for ensuring the stable operation of banks. This study aims to construct a credit risk measurement and management system tailored to China’s national conditions. By employing methods from mathematical statistics and econometrics, and leveraging extensive commercial bank lending data, this research thoroughly analyzes the primary factors influencing credit risk. Innovatively, machine learning algorithms are introduced to optimize traditional risk measurement models, thereby enhancing the accuracy of risk prediction. The findings reveal that under the combined influence of macroeconomic environment and industry characteristics, there are significant differences in credit risk performance among commercial banks of different sizes and at different stages of development. Empirical tests demonstrate that the improved model exhibits markedly enhanced capabilities in risk identification, providing a more precise reflection of credit risk conditions. This study not only offers scientific and effective credit risk management tools for commercial banks but also provides theoretical support for regulatory authorities in formulating relevant policies, contributing significantly to the healthy development of China’s banking sector.
Keywords：Credit Risk Measurement; Commercial Bank; Machine Learning; Risk Management; Macroeconomic Environment
目 录
摘要 I
Abstract II
一、绪论 1
(一) 我国商业银行信用风险研究背景 1
(二) 研究意义与理论价值 1
(三) 国内外研究现状综述 1
(四) 本文研究方法与创新点 2
二、商业银行信用风险度量体系 2
(一) 信用风险度量指标构建 2
(二) 主要度量模型分析 3
(三) 度量模型在我国的应用实践 3
(四) 度量体系的优化建议 4
三、商业银行信用风险管理框架 5
(一) 风险管理的基本原则 5
(二) 内部控制机制建设 6
(三) 风险预警系统设计 6
(四) 全面风险管理策略 7
四、商业银行信用风险防控对策 7
(一) 宏观经济环境影响分析 8
(二) 行业风险特征识别 8
(三) 信贷政策调整方向 9
(四) 风险转移与分散措施 9
结 论 11
参考文献 12
随着我国金融市场的深化发展和对外开放程度的不断提高，商业银行面临的信用风险日益复杂多变，准确度量和有效管理信用风险成为保障银行稳健运营的关键。本研究旨在构建适应我国国情的商业银行信用风险度量与管理体系，综合运用数理统计、计量经济学等方法，基于大量商业银行信贷数据，深入分析影响信用风险的主要因素，创新性地引入机器学习算法优化传统风险度量模型，提高风险预测精度。研究发现，在宏观经济环境、行业特征等因素共同作用下，不同规模和发展阶段的商业银行信用风险表现存在显著差异；通过实证检验表明，改进后的模型在风险识别能力方面有明显提升，能够更精准地反映信用风险状况。本研究不仅为商业银行提供了科学有效的信用风险管理工具，还为监管部门制定相关政策提供理论依据，对促进我国银行业健康发展具有重要现实意义。
关键词：信用风险度量；商业银行；机器学习；风险管理；宏观经济环境
Abstract
With the deepening development of China's financial markets and increasing openness to the outside world, commercial banks are confronted with increasingly complex and volatile credit risks. Accurately measuring and effectively managing these risks has become crucial for ensuring the stable operation of banks. This study aims to construct a credit risk measurement and management system tailored to China’s national conditions. By employing methods from mathematical statistics and econometrics, and leveraging extensive commercial bank lending data, this research thoroughly analyzes the primary factors influencing credit risk. Innovatively, machine learning algorithms are introduced to optimize traditional risk measurement models, thereby enhancing the accuracy of risk prediction. The findings reveal that under the combined influence of macroeconomic environment and industry characteristics, there are significant differences in credit risk performance among commercial banks of different sizes and at different stages of development. Empirical tests demonstrate that the improved model exhibits markedly enhanced capabilities in risk identification, providing a more precise reflection of credit risk conditions. This study not only offers scientific and effective credit risk management tools for commercial banks but also provides theoretical support for regulatory authorities in formulating relevant policies, contributing significantly to the healthy development of China’s banking sector.
Keywords：Credit Risk Measurement; Commercial Bank; Machine Learning; Risk Management; Macroeconomic Environment
目 录
摘要 I
Abstract II
一、绪论 1
(一) 我国商业银行信用风险研究背景 1
(二) 研究意义与理论价值 1
(三) 国内外研究现状综述 1
(四) 本文研究方法与创新点 2
二、商业银行信用风险度量体系 2
(一) 信用风险度量指标构建 2
(二) 主要度量模型分析 3
(三) 度量模型在我国的应用实践 3
(四) 度量体系的优化建议 4
三、商业银行信用风险管理框架 5
(一) 风险管理的基本原则 5
(二) 内部控制机制建设 6
(三) 风险预警系统设计 6
(四) 全面风险管理策略 7
四、商业银行信用风险防控对策 7
(一) 宏观经济环境影响分析 8
(二) 行业风险特征识别 8
(三) 信贷政策调整方向 9
(四) 风险转移与分散措施 9
结 论 11
参考文献 12
