摘要
随着金融科技的巨大进步,机器学习在金融风控领域的应用也逐渐深化起来。信用评分卡模型作为一种应用最为广泛的风险评估模型,在大数据时代存在着不能对高维、复杂、非线性的个人征信数据进行全面分析的局限性。从中国的互联网金融发展的实际情况出发,提出一种基于XGBoost机器学习算法的互联网金融风控模型,并与传统的统计评分卡模型进行了对比试验,同时给出了将机器学习模型预测结果转化为传统信用评分的解决方法。研究结果表明,机器学习模型能更好地预测个人信用风险,从而构建更加有效的风控体系。
With the great development of financial technology,machine learning has been deeply applied in the field of financial risk management.As one of the most widely used risk assessment models,credit scorecard model has the limitation on comprehensive analysis of high dimensional,complex and nonlinear personal credit data in the big data era.Starting from the actual situation of the development of Internet finance in China,it presents an innovative risk management model of Internet finance based on XGboost machine learning algorithm,and compares with the traditional statistical scorecard model.Also the method of how to transform the predicted results of machine learning model into credit scoring is given.The results show that machine learning model can predict individual credit risk better and a more effective risk management system could be built based on it.
作者
刘志惠
黄志刚
谢合亮
LIU Zhi-hui;HUANG Zhi-gang;XIE He-liang(School of Ecomomics and Management,Fuzhou University,Fuzhou 350108,China;School of Finance,Fujian Business University,Fuzhou 350012,China;School of Statistics and Mathematics,Central University of Finance and Economics,Beijing 100081,China)
出处
《统计与信息论坛》
CSSCI
北大核心
2019年第9期18-26,共9页
Journal of Statistics and Information
基金
福建省教育厅社会科学基金项目《面向小微企业的互联网金融模式创新研究》(JAS150953)
关键词
风控
信用评分卡
机器学习
大数据
risk management
score card
machine learning
big data
