基于机器学习的商业银行信贷风险评估系统构建与实证研究

Construction and Empirical Research of a Machine Learning-Based Commercial Bank Credit Risk Assessment System

摘要: 随着金融市场的深度演进，传统信贷风险评估模式已难以适配复杂金融环境下的精准风险管控需求。机器学习技术凭借其强大的数据挖掘能力与复杂关系建模优势，为商业银行信贷风险评估提供了创新性解决方案。本文首先通过深度解构商业银行信贷风险评估的业务逻辑，明确系统的核心功能与非功能需求；其次融合机器学习算法与软件工程技术，设计包含数据层、算法层、服务层及可视化层的分层架构体系，并划分为用户管理、风险评估、数据分析三大功能模块；继而以德国银行信贷数据集为基础，采用K-Means聚类算法实现客户分群，结合随机森林算法构建信贷风险预测模型，通过重采样技术解决样本不平衡问题，并基于网格搜索完成模型超参数调优。实证结果表明，所构建系统的风险评估模型测试准确率较高，在高风险客户识别与低风险客户精准判定方面表现优异，具有较强的实践应用价值。

Abstract: With the deepening evolution of financial markets, traditional credit risk assessment models have become inadequate for meeting the precise risk management needs within complex financial environments. Machine learning technology, leveraging its robust data mining capabilities and advantages in modeling complex relationships, offers innovative solutions for credit risk assessment in commercial banks. This paper begins by deconstructing the business logic of credit risk assessment in commercial banks, clarifying the core functional and non-functional requirements of the system. Next, it integrates machine learning algorithms with software engineering techniques to design a layered architecture comprising the data layer, algorithm layer, service layer, and visualization layer, which is further divided into three functional modules: user management, risk assessment, and data analysis. Subsequently, based on a German bank credit dataset, the study employs the K-Means clustering algorithm to achieve customer segmentation and combines the random forest algorithm to construct a credit risk prediction model. Resampling techniques are applied to address class imbalance issues, and model hyperparameter optimization is conducted via grid search. Empirical results demonstrate that the risk assessment model of the constructed system achieves high test accuracy, excels in identifying high-risk customers and accurately classifying low-risk customers, and holds significant practical application value.