国内个人信用评分机制的规范研究
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摘要
信用评分日益成为金融机构贷款决策的重要依据。目前,公民个人的信用关联信息主要来自于以下途径:中国人民银行个人征信信息、信用服务机构的个人信用评分、地方的个人信用评分和电信运营商提供的个人信用评分。从规范我国个人信用评分机制考虑,应建立全国统一的个人信用评分机制,由中国人民银行征信中心统一管理个人信用分数,同时,对不确定性因素进行考察,并区别个人信用与个人声誉评价标准。为建立我国统一的个人信用评分机制,应完善全国征信系统中的个人信用记录,设定个人信用计分卡和计分权重,设置个人信用的风险分数,确立科学的个人信用评分模型,确保个人信用分数计算方法的科学性与合理性。通过规范个人信用评分机制,扩大信用分数在各个领域的应用,进一步促进我国信用体系的建立与完善。
Credit scoring has increasingly become an important basis for financial institutions to make loandecisions. At present, citizens.personal credit information mainly comes from the following channels: personalcredit information of the People.s Bank of China, personal credit score of credit service agencies, local personalcredit score and personal credit score provided by telecom operators. In consideration of normalizing theindividual credit scoring mechanism in China, a national unified individual credit scoring mechanism should beestablished and the Credit Reference Center of the Central Bank should manage the individual credit scoreuniformly. Meanwhile, the uncertainty factors should be investigated and the evaluation standards of individualcredit and reputation should be distinguished. In order to establish a unified personal credit scoring mechanismin China, it's necessary to improve the personal credit record in the national credit reporting system, to set thepersonal credit score card and scoring weights, to set the risk score of personal credit, and to establish ascientific personal credit scoring model, so as to ensure the scientificalness and rationality of the calculationmethod of personal credit scoring. By normalizing the individual credit scoring mechanism and expanding the useof credit score in various fields, the establishment and improvement of China's credit system will be furtherpromoted.
Credit scoring has increasingly become an important basis for financial institutions to make loandecisions. At present, citizens.personal credit information mainly comes from the following channels: personalcredit information of the People.s Bank of China, personal credit score of credit service agencies, local personalcredit score and personal credit score provided by telecom operators. In consideration of normalizing theindividual credit scoring mechanism in China, a national unified individual credit scoring mechanism should beestablished and the Credit Reference Center of the Central Bank should manage the individual credit scoreuniformly. Meanwhile, the uncertainty factors should be investigated and the evaluation standards of individualcredit and reputation should be distinguished. In order to establish a unified personal credit scoring mechanismin China, it's necessary to improve the personal credit record in the national credit reporting system, to set thepersonal credit score card and scoring weights, to set the risk score of personal credit, and to establish ascientific personal credit scoring model, so as to ensure the scientificalness and rationality of the calculationmethod of personal credit scoring. By normalizing the individual credit scoring mechanism and expanding the useof credit score in various fields, the establishment and improvement of China's credit system will be furtherpromoted.
引文
[1]GG Chen and T ?stebro.Bound and Collapse Bayesian Reject Inference for Credit Scoring[J].The Journal of the Operational Research Society,2012,63(10):1374.
[2]朱浩,董险锋,陈彦舟.国内城市个人信用评分指标体系和应用场景研究[J].征信,2019(4):9-15.
[3]Ashlyn Aiko Nelson.Credit Scores,Race,and Residential Sorting[J].Journal of Policy Analysis and Management,2010,29(1):39.
[4]Evan Hendricks.Credit Reports,Credit Checks,Credit Scores[J].GP Solo,2011,28(5):33.
[5]孙宝文,李涛.中国互联网经济发展报告(2018)[M].北京:社会科学文献出版社,2018:2.
[6]詹姆斯·W.布罗克.美国产业结构(第十二版)[M].罗宇,潘碧玥,胡善斌,等译.北京:中国人民大学出版社,2011:326.
[7]M.Cary Collins,Keith D.Harvey and Peter J.Nigro.The Influence of Bureau Scores,Customized Scores and Judgmental Review on the Bank Underwriting Decision-Making Process[J].The Journal of Real Estate Research,2002,24(2):129.
[8]Raj K.Chhikara.The State of the Art in Credit Evaluation[J].American Journal of Agricultural Economics,1989,71(5):1138.
[9]张晓冉.我国个人失信惩罚的规范研究:类型、适用及其限制[J].电子政务,2019(2):114-121.
[10]TH Moon and SY Sohn.Survival Analysis for Technology Credit Scoring Adjusting Total Perception[J].The Journal of the Operational Research Society,2011,62(6):1159.
[11]Annie McClanahan.Bad Credit:The Character of Credit Scoring[J].Representations,2014,126(1):31.
[12]K.Rajaratnam,P.Beling and G.Overstreet.Scoring Decisions in the Context of Economic Uncertainty[J].The Journal of the Operational Research Society,2010,61(3):421.
[13]VL Miguéis,DF.Benoit and D.Van den Poel.Enhanced Decision Support in Credit Scoring Using Bayesian Binary Quantile Regression[J].The Journal of the Operational Research Society,2013,64(9):1375.
[14]AI Marqués,V.García and JS Sánchez.A Literature Review on the Application of Evolutionary Computing to Credit Scoring[J].The Journal of the Operational Research Society,2013,64(9):1384.
[15]Robert B.Avery,Kenneth P.Brevoort and Glenn B.Canner.Credit Scoring and Its Effects on the Availability and Affordability of Credit[J].The Journal of Consumer Affairs,2009,43(3):516.
[16]Salomon Israel,Avshalom Caspi,Daniel W.Belsky,HonaLee Harrington,Sean Hogan,Renate Houts,Sandhya Ramrakha,Seth Sanders,Richie Poulton and Terrie E.Moffitt.Credit Scores,Cardiovascular Disease Risk,and Human Capital[J].Proceedings of the National Academy of Sciences of the United States of America,2014,111(48):17087.
[17]Peter J.Barry and Paul N.Ellinger.Credit Scoring,Loan Pricing,and Farm Business Performance[J].Western Journal of Agricultural Economics,1989,14(1):50.
[18]Anne M.Wenninger Gehring.Recent Developments in Credit Score Disclosures[J].The Business Lawyer,2012,67(2):657.
[19]K Bijak.Kalman Filtering as a Performance Monitoring Technique for a Propensity Scorecard[J].The Journal of the Operational Research Society,2011,62(1):29.
[20]张婷婷,景英川.个人信用评分的Adaptive Lasso-Logistic回归分析[J].数学的实践与认识,2016,46(18):92-99.
[21]向晖,杨胜刚.个人信用评分关键技术研究的新进展[J].财经理论与实践,2011,32(4):20-24.
(1)关于宿迁市民评分的级别与等次,可参见宿迁市社会信用体系建设领导小组办公室发布的《宿迁市关于自然人信用积分体系建设与积分等级评价试行规定》(宿信用办[2018]9号)第一章第三条,2018年3月7日。
(1)参见D.J.Hand and W.E.Henley:“Statistical Classification Methods in Consumer Credit Scoring”:A Review,Journal of the Royal Statistical Society.Series A(Statistics in Society),Vol.160,No.3(1997),p.523.
(2)参见Danny Ben-Shahar:“Default,Credit Scoring,and Loan-to-Value:A Theoretical Analysis of Competitive and Non-Competitive Mortgage Markets”,The Journal of Real Estate Research,Vol.30,No.2(2008),p.161.
(1)美国有三个全国信用报告机构,分别是:Equifax,Experian,TransUnion.
(2)参见Kenneth P.Brevoort,Philipp Grimm and Michelle Kambara:“Credit Invisibles and the Unscored”,Cityscape,Vol.18,No.2,BorrowerBeware(2016),p.12.
(3)参见David J Hand and Niall M Adams:“Selection Bias in Credit Scorecard Evaluation”,The Journal of the Operational Research Society,Vol.65,No.3,Special Issue:Credit Risk Modelling(MARCH2014),p.408,p.414.
(1)参见Noel Capon:“Credit Scoring Systems:A Critical Analysis”,Journal of Marketing,Vol.46,No.2(Spring,1982),p.83.
(1)参见George J.Dreyer and James N.Rice:“Equal Credit Opportunity”,The Business Lawyer,Vol.36,No.3(April 1981),pp.1203-1217.
[2]朱浩,董险锋,陈彦舟.国内城市个人信用评分指标体系和应用场景研究[J].征信,2019(4):9-15.
[3]Ashlyn Aiko Nelson.Credit Scores,Race,and Residential Sorting[J].Journal of Policy Analysis and Management,2010,29(1):39.
[4]Evan Hendricks.Credit Reports,Credit Checks,Credit Scores[J].GP Solo,2011,28(5):33.
[5]孙宝文,李涛.中国互联网经济发展报告(2018)[M].北京:社会科学文献出版社,2018:2.
[6]詹姆斯·W.布罗克.美国产业结构(第十二版)[M].罗宇,潘碧玥,胡善斌,等译.北京:中国人民大学出版社,2011:326.
[7]M.Cary Collins,Keith D.Harvey and Peter J.Nigro.The Influence of Bureau Scores,Customized Scores and Judgmental Review on the Bank Underwriting Decision-Making Process[J].The Journal of Real Estate Research,2002,24(2):129.
[8]Raj K.Chhikara.The State of the Art in Credit Evaluation[J].American Journal of Agricultural Economics,1989,71(5):1138.
[9]张晓冉.我国个人失信惩罚的规范研究:类型、适用及其限制[J].电子政务,2019(2):114-121.
[10]TH Moon and SY Sohn.Survival Analysis for Technology Credit Scoring Adjusting Total Perception[J].The Journal of the Operational Research Society,2011,62(6):1159.
[11]Annie McClanahan.Bad Credit:The Character of Credit Scoring[J].Representations,2014,126(1):31.
[12]K.Rajaratnam,P.Beling and G.Overstreet.Scoring Decisions in the Context of Economic Uncertainty[J].The Journal of the Operational Research Society,2010,61(3):421.
[13]VL Miguéis,DF.Benoit and D.Van den Poel.Enhanced Decision Support in Credit Scoring Using Bayesian Binary Quantile Regression[J].The Journal of the Operational Research Society,2013,64(9):1375.
[14]AI Marqués,V.García and JS Sánchez.A Literature Review on the Application of Evolutionary Computing to Credit Scoring[J].The Journal of the Operational Research Society,2013,64(9):1384.
[15]Robert B.Avery,Kenneth P.Brevoort and Glenn B.Canner.Credit Scoring and Its Effects on the Availability and Affordability of Credit[J].The Journal of Consumer Affairs,2009,43(3):516.
[16]Salomon Israel,Avshalom Caspi,Daniel W.Belsky,HonaLee Harrington,Sean Hogan,Renate Houts,Sandhya Ramrakha,Seth Sanders,Richie Poulton and Terrie E.Moffitt.Credit Scores,Cardiovascular Disease Risk,and Human Capital[J].Proceedings of the National Academy of Sciences of the United States of America,2014,111(48):17087.
[17]Peter J.Barry and Paul N.Ellinger.Credit Scoring,Loan Pricing,and Farm Business Performance[J].Western Journal of Agricultural Economics,1989,14(1):50.
[18]Anne M.Wenninger Gehring.Recent Developments in Credit Score Disclosures[J].The Business Lawyer,2012,67(2):657.
[19]K Bijak.Kalman Filtering as a Performance Monitoring Technique for a Propensity Scorecard[J].The Journal of the Operational Research Society,2011,62(1):29.
[20]张婷婷,景英川.个人信用评分的Adaptive Lasso-Logistic回归分析[J].数学的实践与认识,2016,46(18):92-99.
[21]向晖,杨胜刚.个人信用评分关键技术研究的新进展[J].财经理论与实践,2011,32(4):20-24.
(1)关于宿迁市民评分的级别与等次,可参见宿迁市社会信用体系建设领导小组办公室发布的《宿迁市关于自然人信用积分体系建设与积分等级评价试行规定》(宿信用办[2018]9号)第一章第三条,2018年3月7日。
(1)参见D.J.Hand and W.E.Henley:“Statistical Classification Methods in Consumer Credit Scoring”:A Review,Journal of the Royal Statistical Society.Series A(Statistics in Society),Vol.160,No.3(1997),p.523.
(2)参见Danny Ben-Shahar:“Default,Credit Scoring,and Loan-to-Value:A Theoretical Analysis of Competitive and Non-Competitive Mortgage Markets”,The Journal of Real Estate Research,Vol.30,No.2(2008),p.161.
(1)美国有三个全国信用报告机构,分别是:Equifax,Experian,TransUnion.
(2)参见Kenneth P.Brevoort,Philipp Grimm and Michelle Kambara:“Credit Invisibles and the Unscored”,Cityscape,Vol.18,No.2,BorrowerBeware(2016),p.12.
(3)参见David J Hand and Niall M Adams:“Selection Bias in Credit Scorecard Evaluation”,The Journal of the Operational Research Society,Vol.65,No.3,Special Issue:Credit Risk Modelling(MARCH2014),p.408,p.414.
(1)参见Noel Capon:“Credit Scoring Systems:A Critical Analysis”,Journal of Marketing,Vol.46,No.2(Spring,1982),p.83.
(1)参见George J.Dreyer and James N.Rice:“Equal Credit Opportunity”,The Business Lawyer,Vol.36,No.3(April 1981),pp.1203-1217.
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