基于主成分分析的信用评分模型研究
Research on Credit Scoring Models Based on Principal Component Analysis

信用评分是确保金融机构安全借贷和减少坏账风险的重要工具，其中主成分分析(PCA)能提高处理贷款数据的精确度和效率，进而提升信用评分模型的预测能力。本文首先介绍了研究背景和意义，探讨了信用评分模型的发展与现状，分析了PCA在信用评分中的应用优势及局限。通过分析上市公司数据构建指标体系和主成分分析，计算因子得分，并利用主成分数据构建决策树模型以验证PCA模型的准确性。最后，文章总结了研究成果并对未来的研究方向进行了展望。
Credit scoring is a crucial tool for financial institutions to lend safely and reduce the risk of bad debts, where principal component analysis (PCA) enhances the accuracy and efficiency of processing loan data, thus improving the predictive power of credit scoring models. This article begins with an introduction to the background and significance of the study, discusses the development and current state of credit scoring models, and analyzes the advantages and limitations of applying PCA in credit scoring. By examining data from listed companies to construct an indicator system and performing PCA to calculate factor scores, the study further validates the accuracy of the PCA model through the construction of a decision tree model based on principal component data. Finally, the paper concludes with a summary of the findings and a perspective on future research directions.