福建电脑2025，Vol.41Issue(11)：26-31,6.DOI:10.16707/j.cnki.fjpc.2025.11.005
随机森林赋能小额贷款信用风险评估研究
Research on Credit Risk Assessment Method for Small Loans Based on Random ForestTitle
摘要
Abstract
Amidst the macroeconomic downturn,credit risks for small and micro enterprises have intensified,necessitating effective risk assessment methods for microfinance companies.Based on the actual loan data of HY Microfinance Company from 2020 to 2024,this paper employs a random forest model for credit risk classification and assessment.The empirical results indicate that the model achieves a prediction accuracy rate of 90%,significantly outperforming traditional methods such as logistic regression and support vector machines.Through the calculation of weighted risk values,the model's effective quantification capability for loan portfolio risks is verified(with an example risk value of 4.69).The research demonstrates that the random forest algorithm can adapt to the characteristics of microfinance data,providing reliable technical support for industry credit risk management.关键词
随机森林/小额贷款/信用风险/机器学习/风险评估Key words
Random Forest Method/Micro-Credit/Credit Risk/Machine Learning/Risk Assessment分类
计算机与自动化引用本文复制引用
卢花兰..随机森林赋能小额贷款信用风险评估研究[J].福建电脑,2025,41(11):26-31,6.基金项目
本文得到广东省哲学社会科学规划项目(No.GD24CYJ54)资助. （No.GD24CYJ54）
