基于征信大数据的替代信用评分  

Substitute Credit Scoring Based on Big Data Credit Reference

作　　者：刘新海[1,2] 曲丹阳[3] 

机构地区：[1]中国人民银行征信中心,北京100031 [2]北京大学金融智能研究中心,北京100871 [3]财政部财政科学研究所,北京100142

出　　处：《征信》2016年第3期33-36,共4页Credit Reference

基　　金：国家自然科学基金青年基金(61105058);(教育部)留学回国人员科研启动基金(2015-48);国家社会科学基金(13CJY011)

摘　　要：随着征信市场的快速发展,传统信用评分因其高门槛而呈现明显的拓展局限,例如美国有20%的消费者无法获得信用评分,因而在金融服务中困难重重。全球各大征信公司都开展了大数据技术的研究和应用,纷纷利用大数据资源,挖掘信贷数据之外的其他替代数据,加大替代信用评分产品的研发力度。在介绍大数据征信应用背景的基础上,分析归纳益博睿、艾克飞和环联等传统征信机构和数据挖掘公司费埃哲合作引入替代征信大数据,以及开发替代信用评分的最近动向及成果,进而总结其借鉴意义,希望能够对中国未来的大数据征信有一些启示。With the rapid development of credit reference market,traditional credit scoring with its high thresholds showed significantly expanding limitations,for instance,twenty percent consumers in the United States are unable to obtain credit scoring and have great difficulty in obtaining financial services. Major global credit reporting companies have conducted researches and applications of big data technology,commonly use big data resources and mine other substitute data besides credit data,and take greater efforts in the research and development of replacing credit scoring products. Based on introducing the application background of big data credit reference,the paper analyzed and induced the substitute credit reference big data that was jointly introduced by traditional credit reporting agencies such as Experian,Equifax and Transunion and FICO data mining company,and the latest trends and achievements of developing substitute credit scoring. The paper also summarized some reference,hoping to provide enlightenment on the future big data credit reference in China.

关 键 词：征信 大数据 替代数据 信用评分 

分 类 号：F832.4[经济管理—金融学] F724.6