摘要
小额借贷中的个人信用风险问题持续制约着小额贷款行业的健康可持续发展。针对小贷公司在进行信用风险评估时对高违约风险客户识别准确率较低的难题,运用混合式SMOTE、RF算法来同时处理业务数据中高维、非均衡两个问题。本文借助江苏J小贷公司的实例数据,依次构建随机森林(Random Forest, RF)模型、SMOTE-RF模型以及Borderline-SMOTE-RF模型并进行模型测试;再选用SVM算法进行对比实验以此衡量模型的信用风险评价精度。随后基于模型对于指标重要性的评分筛选出6项指标作为影响个人信用风险的关键指标。实验证明基于Borderline-SMOTE-RF算法对于小额贷款个人信用风险评价模型的分类性能最佳;在筛选关键指标时,为避免人工合成虚拟样本对指标重要性影响,需要结合三类模型评分进行综合选择。
The microfinance industry plays a crucial role in providing financial services to individuals who often lack access to traditional banking systems.However,the inherent risk associated with small-scale lending,particularly the challenge of accurately assessing the creditworthiness of individuals,poses a threat to the stability and growth of microloan institutions.The persistent challenge of individual credit risk in microloans continues to hinder the healthy and sustainable development of the microfinance industry.Specifically,the accurate identification of high default-risk clients remains a significant issue for microfinance companies when conducting credit risk assessments.This research holds theoretical significance by proposing a hybrid model that combines SMOTE and RF algorithms to address the challenges posed by high-dimensional and imbalanced datasets in the microloan context.The practical significance lies in its potential to enhance the accuracy of credit risk assessments,providing microfinance companies with more robust tools for making informed lending decisions.To enhance the accuracy of credit risk assessments,this research leverages real-world data from Jiangsu-based J Microfinance Company.To tackle the challenges presented by microloan business data,the study employs a hybrid approach.The Random Forest(RF)model is initially constructed,followed by the development and evaluation of the SMOTE-RF and Borderline-SMOTE-RF models.These models integrate oversampling techniques with the powerful predictive capabilities of the Random Forest algorithm,aiming to improve the accuracy of credit risk assessments.Support Vector Machine(SVM)is selected for comparative experiments to benchmark the performance of the proposed models.The empirical testing reveals that the Borderline-SMOTE-RF algorithm outperforms the other models,demonstrating superior classification performance in personal credit risk assessment for microloans.The hybrid approach effectively addresses the challenges of high dimensionality and data i
作者
严晴
徐海燕
YAN Qing;XU Haiyan(School of Economics and Management,Nanjing University of Aeronautics and Astronautics,Nanjing 211106,China)
出处
《运筹与管理》
CSCD
北大核心
2024年第1期191-197,共7页
Operations Research and Management Science
基金
国家自然科学基金资助项目(71971115,71471087,61673209)。
