摘要
随着经济环境的复杂化和金融市场的不确定性增加，商业银行面临的信贷风险日益凸显，传统的信贷风险评估与管理体系已难以满足当前需求。为此，本研究旨在优化商业银行信贷风险评估与管理体系，以提升其风险识别、量化及应对能力。研究基于现代风险管理理论，结合大数据分析技术与机器学习算法，构建了一套动态、智能化的信贷风险评估模型，并提出了涵盖事前预防、事中监控与事后处置的全流程管理体系。通过对中国多家商业银行的实际数据进行实证分析，结果表明，该模型能够显著提高风险预测的准确性和时效性，同时降低不良贷款率。此外，研究创新性地引入了多维度信用评分机制和情景模拟工具，增强了对潜在风险的全面感知能力。最终结论显示，优化后的体系不仅提升了商业银行的风险管理效率，还为其战略决策提供了科学依据。本研究的主要贡献在于将先进技术与传统银行业务深度融合，为商业银行在数字化转型背景下的风险管理提供了新思路与实践参考。
关键词：信贷风险评估；智能化风险管理；大数据分析；机器学习；多维度信用评分
Abstract
With the increasing complexity of the economic environment and rising uncertainty in financial markets, credit risks faced by commercial banks have become increasingly prominent. Traditional credit risk assessment and management systems are no longer sufficient to meet current demands. To address this challenge, this study aims to optimize the credit risk assessment and management system of commercial banks, thereby enhancing their capabilities in risk identification, quantification, and response. Grounded in modern risk management theory, the research integrates big data analytics and machine learning algorithms to develop a dynamic and intelligent credit risk assessment model. Additionally, it proposes a comprehensive management fr amework encompassing pre-event prevention, intra-event monitoring, and post-event resolution. Empirical analysis using real-world data from multiple commercial banks in China demonstrates that the proposed model significantly improves the accuracy and timeliness of risk prediction while reducing non-performing loan rates. Furthermore, the study innovatively incorporates a multi-dimensional credit scoring mechanism and scenario simulation tools, which enhance the ability to comprehensively perceive potential risks. The final conclusion indicates that the optimized system not only improves the efficiency of risk management in commercial banks but also provides a scientific basis for strategic decision-making. The primary contribution of this research lies in its deep integration of advanced technologies with traditional banking operations, offering new insights and practical references for risk management in the context of digital transformation for commercial banks.
Keywords：Credit Risk Assessment; Intelligent Risk Management; Big Data Analysis; Machine Learning; Multi-dimensional Credit Scoring
目 录
摘要 I
Abstract II
一、绪论 1
(一) 商业银行信贷风险管理的背景与意义 1
(二) 国内外研究现状分析 1
(三) 本文研究方法与技术路线 2
二、商业银行信贷风险评估体系优化 2
(一) 信贷风险评估的关键指标分析 2
(二) 数据驱动的风险评估模型构建 3
(三) 评估体系优化的实践路径 3
三、商业银行信贷管理体系优化策略 4
(一) 现有信贷管理体系的问题剖析 4
(二) 基于流程改进的管理优化方案 5
(三) 技术支持下的信贷管理升级 5
四、商业银行信贷风险防控机制完善 6
(一) 风险预警机制的设计与实施 6
(二) 内部控制在风险防控中的作用 6
(三) 外部环境对风险防控的影响分析 7
结 论 9
参考文献 10
