消费贷款中信用风险的评估研究
|
摘要
消费信贷是指为了生活消费的需要,金融或商业机构以货物、货币等形式向有一定支付能力的消费者提供信用,消费者在将来的某个时期偿还的一种信贷行为和信用体系。我国消费信贷起步于20世纪80年代中期,但真正迅速发展却是从20世纪90年代末期开始,1997年底,全国个人消费信贷规模仅为172亿元,到2008年底,个人消费信贷增至3.7万亿元,比1997年增长217倍；个人消费贷款余额占人民币各项贷款总额的比例也由1997年的0.2%上升到2008年的12.28%。个人消费信贷规模的逐年增大,产生了对个人信用风险的控制需求。然而,受制于我国长期以来以企业信用制度建设为重点,缺乏专门的机构对个人信用记录数据收集等一系列因素的影响,我国商业银行信用风险管理存在着贷款发放时信息不对称现象严重、个人信用评估科学方法缺乏等问题,与快速增长的个人消费信贷不相适应。国外经验表明,个人信用评分模型作为一种有效的个人信用风险评估管理工具,在商业银行的消费信贷风险管理中起着非常重要的作用。在目前个人信用数据不完备、信用技术不完善、专业信用人员缺乏的条件下借鉴国外先进经验,充分利用商业银行现有信息系统中的数据和信息,建立一个具有一定预测能力、在信贷决策中具有一定参考价值、符合我国国情的个人信用风险评估模型是很有实践意义的。
本文以H农村商业银行2008年3月26日至2009年10月13日审批通过的1356笔个人消费贷款客户信息为样本,尝试应用统计方法中生存分析、Logistic回归及判别分析等方法来构建个人信用风险评估模型,并通过模型间变量作用的差别比较找出影响个人消费信贷风险的主要因素,为H银行个人贷款风险管理提供相应的可行建议。
本文共分五部分。首先,本文对选题的背景和意义作了简要陈述,同时鉴于本文研究的重点是通过生存分析、Logistic回归及判别分析等方法的应用来构建个人信用风险评估模型,所以本文在第一部分中重点对目前国际比较流行的信用评分模型建模方法、优劣势以及在我国信用评估中的应用等进行了详细阐述,为下阶段评分模型的建立提供参考和思路。然后,本文就消费信贷、信用风险、个人信用评估等基本概念、内涵及定位作了界定。第三部分,本文接着对文章中要用到的χ2检验、单变量生存函数、Cox回归、Logistic回归及判别分析等方法及建模过程作了详细研究和分析。最后,本文通过对H农村商业银行的实证分析,得出了在H农村商业银行现有信贷系统数据中,学历、贷款方式、年龄这三个变量对客户类型的识别具有重要意义,并认为对优良客户和淘汰客户的识别方式上,应尽可能采用两种以上的信用评分模型进行综合识别,即采用Logistic回归模型和Fisher判别分析,做到最大限度的提高信贷风险的识别能力。
Consumer credit is a credit conduct and system that for the need of living, financial or commercial organizations provide consumers of the capacity to pay with some consumer credit in the forms of goods, money and other forms, and the consumers pay back a certain period in the future. Consumer Credit in China started in the mid 1980s of the 20th century, but its rapid development began in the late 1990s of the 20th century. By the end of 1997, national personal consumption is only 17.2 billion credit scale, and to the end of 2008,consumer credit had increased to 37 000 billion, an increase of 217 times over 1997;personal consumer loans accounted for RMB loans ratio 0.2% in 1997 rose to 12.28% in 2008.The scale of consumer credit increased year by year, resulting in the needs of the individual credit risk management. However, because of China has long been emphases on the enterprise credit system construction, the lack of specialized agencies to the credit history data collection and a series of other factors, there are many problems in the China's commercial bank credit risk management, that are serious information asymmetry during the loans are provide and the lack of scientific methods of individuals credit evaluation and other issues, which is incompatible with the rapid growth of consumer credit. Overseas experience shows that personal credit score model as an effective management tool for individual credit risk assessment, has played a very important role in consumer credit risk management of the commercial banks.Under the current conditions of the imperfect personal credit data, imperfect credit technology and the lack of professional staff it has a very practical sense that making full use of existing data and information of information systems in commercial banks and establishing a personal credit risk assessment model which has certain predictability and some reference value in the credit decision-making in line with our national conditions.
In this paper, with the sample of 1356 personal consumer loans document information approved by H Rural Commercial Bank from March 26,2008 to October 13,2009,the author has tried to build personal credit risk assessment model by the statistical methods of survival analysis, Logistic regression and discriminant analysis and find out the main factors affecting consumer credit risk through the model variables comparison in order to put forward practical recommendations about making the appropriate credit decision criteria. This article is divided into five parts.Firstly, it has the brief statement on the research background and significance. In view of the emphases on building individual credit risk assessment model through survival analysis, Logistic regression and discriminant analysis, the first part of the paper has the focus on the currently popular method of credit scoring modeling, the advantages and disadvantages as well as the application in our credit evaluation for considering the score model establishment of the next stage. Then, this paper has defined the basic concepts, content and location of consumer credit, credit risk, credit scoring and other concepts.In the PartⅢthis paper has the research and analysis in detail about the methods of the survival function of a single variable, Cox regression, Logistic regression and discriminant analysis and the modeling which are used in the article in the test. Finally, by the empirical analysis of H Rural Commercial Bank, the paper has showed that the three variables of education, loans modes and the age is important to the customer type recognition in the existing credit systems of H Rural Commercial Bank and proposed the solution.
本文以H农村商业银行2008年3月26日至2009年10月13日审批通过的1356笔个人消费贷款客户信息为样本,尝试应用统计方法中生存分析、Logistic回归及判别分析等方法来构建个人信用风险评估模型,并通过模型间变量作用的差别比较找出影响个人消费信贷风险的主要因素,为H银行个人贷款风险管理提供相应的可行建议。
本文共分五部分。首先,本文对选题的背景和意义作了简要陈述,同时鉴于本文研究的重点是通过生存分析、Logistic回归及判别分析等方法的应用来构建个人信用风险评估模型,所以本文在第一部分中重点对目前国际比较流行的信用评分模型建模方法、优劣势以及在我国信用评估中的应用等进行了详细阐述,为下阶段评分模型的建立提供参考和思路。然后,本文就消费信贷、信用风险、个人信用评估等基本概念、内涵及定位作了界定。第三部分,本文接着对文章中要用到的χ2检验、单变量生存函数、Cox回归、Logistic回归及判别分析等方法及建模过程作了详细研究和分析。最后,本文通过对H农村商业银行的实证分析,得出了在H农村商业银行现有信贷系统数据中,学历、贷款方式、年龄这三个变量对客户类型的识别具有重要意义,并认为对优良客户和淘汰客户的识别方式上,应尽可能采用两种以上的信用评分模型进行综合识别,即采用Logistic回归模型和Fisher判别分析,做到最大限度的提高信贷风险的识别能力。
Consumer credit is a credit conduct and system that for the need of living, financial or commercial organizations provide consumers of the capacity to pay with some consumer credit in the forms of goods, money and other forms, and the consumers pay back a certain period in the future. Consumer Credit in China started in the mid 1980s of the 20th century, but its rapid development began in the late 1990s of the 20th century. By the end of 1997, national personal consumption is only 17.2 billion credit scale, and to the end of 2008,consumer credit had increased to 37 000 billion, an increase of 217 times over 1997;personal consumer loans accounted for RMB loans ratio 0.2% in 1997 rose to 12.28% in 2008.The scale of consumer credit increased year by year, resulting in the needs of the individual credit risk management. However, because of China has long been emphases on the enterprise credit system construction, the lack of specialized agencies to the credit history data collection and a series of other factors, there are many problems in the China's commercial bank credit risk management, that are serious information asymmetry during the loans are provide and the lack of scientific methods of individuals credit evaluation and other issues, which is incompatible with the rapid growth of consumer credit. Overseas experience shows that personal credit score model as an effective management tool for individual credit risk assessment, has played a very important role in consumer credit risk management of the commercial banks.Under the current conditions of the imperfect personal credit data, imperfect credit technology and the lack of professional staff it has a very practical sense that making full use of existing data and information of information systems in commercial banks and establishing a personal credit risk assessment model which has certain predictability and some reference value in the credit decision-making in line with our national conditions.
In this paper, with the sample of 1356 personal consumer loans document information approved by H Rural Commercial Bank from March 26,2008 to October 13,2009,the author has tried to build personal credit risk assessment model by the statistical methods of survival analysis, Logistic regression and discriminant analysis and find out the main factors affecting consumer credit risk through the model variables comparison in order to put forward practical recommendations about making the appropriate credit decision criteria. This article is divided into five parts.Firstly, it has the brief statement on the research background and significance. In view of the emphases on building individual credit risk assessment model through survival analysis, Logistic regression and discriminant analysis, the first part of the paper has the focus on the currently popular method of credit scoring modeling, the advantages and disadvantages as well as the application in our credit evaluation for considering the score model establishment of the next stage. Then, this paper has defined the basic concepts, content and location of consumer credit, credit risk, credit scoring and other concepts.In the PartⅢthis paper has the research and analysis in detail about the methods of the survival function of a single variable, Cox regression, Logistic regression and discriminant analysis and the modeling which are used in the article in the test. Finally, by the empirical analysis of H Rural Commercial Bank, the paper has showed that the three variables of education, loans modes and the age is important to the customer type recognition in the existing credit systems of H Rural Commercial Bank and proposed the solution.
引文
[1]龙游洋.我国消费信贷的现状及问题分析[J].经济研究导刊,2009(8)：143-144
[2]邵福利.我国消费信贷发展现状及潜力[J].2006.8
[3]徐龙.我国个人信用管理体系发展的思考[J].商业经济,2010(2)：40-41
[4]修晶.我国个人消费贷款信用评级研究--模型与实证[D]：[硕士学位论文].杭州：浙江工商大学.2008
[5]尹文.信用评分卡在个人信贷中的应用研究[J].福建商业高等专科学校学报.2008,12(6)：37-39
[6]钱水土,黄震宇.信用评分模型在中小企业信贷评估中的应用[J].商业经济与管理,2004,(2)：57-61
[7]陈文华.信用评分模型与方法[J].中国信用卡,2007(3)：24-27
[8]Myers. J.,E.Forgy.The Development of Numerical Credit Evaluation Systems[J].Journal of the American Statistical Association,1963
[9]章彰.商业银行信用风险管理[M].北京：中国人民大学出版社,2002年11月
[10]朱忠明.金融风险管理学[M].北京：中国人民大学出版社,2004年12月
[11]R. J. Barro."The Loan Market, Collateral,and Rates of Interest,"Journal of Money, Credit and Banking,1976,8(4):439-456
[12]Joseph E.Stiglitz,Andrew Weiss.Credit Rationing in Markerts with Imperfect Information[J].The American Economic Review,1981(71),393-410
[13]Benjamin Klein,Keith B.Leffler.The Role of Market Forces in Assuring Contractual Performance[J].Journal of Political Economy,1981(89),615
[14]Yuk-Shee Chan,Geogre Kanatas.Asymmetric Valuations and the Role of Collateral in Loan Agreements[J].Journal of Money, Credit and Banking,1985(17),84-95
[15]Hayre,Lakhbir. Analyzing Prepayment and Credit Risk of Mortgage Securities[J].in Fixed-Income Investment:Recent Research.1995
[16]何爱武,孙海波.商业银行个人消费信贷风险分析及其对策[J].中国金融,2003(15)：33-3
[17]张亚珍.我国消费信贷的风险及其控制措施[J].商业研究,2003(20)：47-49
[18]陈杰.消费信贷中的潜在风险及成因[J].浙江金融,2006(3)：60-61
[19]任金政,陈宝峰,信息不对称与消费信贷信用风险管理[J].河南金融管理干部学院学报,2004(4)：16-18
[20]李可佳,蒋晓蓉.个人贷款业务风险不容忽视[J].西南金融,2004(8)：1-13
[21]杨华辉,健全信贷管理与防范消费信贷风险[J].当代经济科学,2006(1)120-121
[22]林均跃.消费者信用管理[M].北京：中国方正出版社,2002年8月
[23]钟波,肖智,刘朝林.基于LS-SVM的信用评价方法[J].统计研究,2005(11)29-31
[24]尚立红,黄立平.消费信贷中的行为评分研究[J].山东理工大学学报(社科版),2004(7)：15-17
[25]龙海明,邓杏秀.基于违约概率的消费信贷风险分析与度量[J].金融理论与实践,2006(1)：18-20
[26]王淳,国有商业银行消费信贷业务信贷风险管理现状分析及建议[J].现代商业银行导刊,2005(9)：22-25
[27]梁晓娟,建立个人信用体系发展个人信贷[J].河南金融管理干部学院学报,2003(6)：37-38
[28]张婷,建立“主银行”式的消费信货模式构想[J].财经科学,2004(3)：27-31
[29]刘志清.个人消费信贷业务的风险不容忽视[J].中国金融,2004(5)：45-46
[30]任欣.商业银行防范消费信贷风险的对策建议[J].财贸研究,2002(5)：36-38,74
[31]陈靓.基于数据挖掘的银行信用评分系统研究[D]：[硕士学位论文].沈阳：辽宁师范大学.2006
[32]陈建.信用评分模型综述[J].中国信用卡.2005年1月.46-47
[33]王静,王延清,何德权.基于多层前馈神经网络的个人信用评分模型[J].经济师,2004(12)：20-21
[34]李曙光.神经网络在消费者信用评分中的应用探讨[J].技术经济,2003(3)：50-52
[35]李瑞丽,沈薇.利用统计学方法建立消费信贷评分模型[J].经济与管理,2005(4)：23-26
[36]杨力,宋利等.信用评分的统计模型方法述评[J].知识丛林,2006(7)：141-142
[37]高国平,刘树安.基于径向基函数神经网络的信用评分模型研究[J].计算机技术与发展,2007(9)：11-14
[38]季峰,李勇等.基于SenV-RBF的个人信用评分模型[J].中国科学技术大学学报,2007(7)：767-772
[39]马海英.基于神经网络及Logistic回归的混合信用卡评分模型[J].华东理工大学学报 (社会科学版),2008(2)：49-52
[40]黄梦妮.浅析信用评分模型[J].商场现代化,2008(7)：143
[41]晏艳阳,莫如冰.基于多级模糊综合评判法的个人信用评分模型研究[J].财经理论与实践(双月刊),2009(3)：7-12
[42]易传和,彭江.基于FAHP的个人信用评分模型[J].统计与决策2009(15)：67-70
[43]石庆焱,靳云汇.多种个人信用评分模型在中国应用的比较研究[J].统计研究,2004(6)：43-47
[44]梁樑,吴德胜等.基于ANFIS和Elman网络的信用评价研究[J].管理工程学报,2005(1)：69-73
[45]杨力,童艳梅,阮守武,刘晓伟,吴德胜V-fold交叉验证和BP神经网络在信用评价中的应用[J].运筹与管理,2005(4),140-143
[46]朱晓明,程建等.基于反向传播算法神经网络的信用评分系统预测力研究[J].西安交通大学学报,2006(12)：1406-1409
[47]余文建,沈益昌,杜洋.基于Logistic模型的个人信用评分体系研究[J].海南金融,2007(3)
[48]张成虎,李育林.基于神经网络的个人信用评分模型研究[J].云南师范大学学报(哲学社会科学版),2008(11)：87-93
[49]廖绚,李兴绪.基于Logit模型的银行个人信贷风险管理评估[J].统计与决策,2008(21)：50-52
[50]张成虎,李育林,吴鸣.基于判别分析的个人信用评分模型研究与实证分析[J].大连理工大学学报(社会科学版)2009(1)：6-10
[51]杨建模,杨胜刚.基于数据包络分析的企业信用评分方法[J].生存力研究2009(24)：95-97
[52]尚立红,黄立平.消费信贷中的行为评分研究[J].山东理工大学学报(社会科学版),2004(4)：15-17
[53]傅继波,杨朝军.上市公司财务困境预测的信用评分模型[J].浙江工业大学学报,2005(10)：583-585
[54]杨文瀚,刘思峰.基于灰关联度硇企业信用风睑评分方法及应用[J].商业研究,2005(20)：22-24
[55]李猛,沈菊菊.基于数据挖掘的信用卡授卡风险评估模型[J].中国信用 卡,2006(11)：56-58
[56]刘昭亮.基于信用评分技术的信用卡风险管理策略[J].科技创业,2007(8)：949-8
[57]刘莉亚.商业银行个人信贷信用评分模型的构建与应用[J].财经研究,2007(2)：26-36
[58]林铮.试论国家助学贷款信用评分指标体系的构建[J].内江科技,2007(7)：41
[59]陈颖,鲍正新.零售信用风险内部评级法研究[J].金融风险管理.2008(6)：31-37
[60]张博文.电信运营客户信用评分建模研究[J].中国科技信息,2009(10)：113-114
[61]方磊.论信用评分在我国个人房贷违约分析中的应用[J].现代商贸工业.2009(3)
[62]林莉.国家助学贷款信用评分模型的构建及运用研究[J].高教探索2009(2)：131-134
[63]李孟来.我国个人信用评分模型的应用探讨[J].金融管理与研究2009(2)：51-53
[64]Yoba, M. B.,Crook, J. N.,&Ross, P.,Credit scoring using neural and evolutionary techniques, IMA Journal of Maths Applied in Business and Industry,1996,11:111-125
[65]Robert, B.,Paul,S,Consumer credit scoring:Do situational circumstances mater?,Journal of Banking & Finance,2004,28:835-856
[66]Crook, J. N.,Banasik, J.,Samples election bias in credit scoring models, Journal of Operational Research Society,2003,54:822-832
[67]Crook, J. N., Banasik, J.,Does reject inference really improve the performance of application scoring models?,Journal of Banking & Finance,2004,28:857-874
[68]党信涛.商业银行消费信贷个人客户信用评分探讨[D]：[硕士学位论文].贵阳：贵州大学.2006
[69]吴俊.银行个人信用评分方法及其应用研究[D]：[硕士学位论文].无锡：江南大学.2008
[70]尚立红.我国商业银行消费信贷中的个人信用评分方法及应用研究[D]：[硕士学位论文].上海：同济大学管理科学与工程,2005
[71]何振亚.中国消费信贷发展回顾与展望[J].上海金融2009(3)：16-19
[72]潘丽娟.我国商业银行发展消费信贷业务的策略分析[J].内蒙古科技与经济.2009(3)：35-38
[73]曾辉.基于决策树的银行个人客户信用评分模型的研究[D]：[硕士学位论文].北京：对外经济贸易大学.2007
[74]钟楚男.个人信用征信制度[M].北京：中国金融出版社.2002年11月
[2]邵福利.我国消费信贷发展现状及潜力[J].2006.8
[3]徐龙.我国个人信用管理体系发展的思考[J].商业经济,2010(2)：40-41
[4]修晶.我国个人消费贷款信用评级研究--模型与实证[D]：[硕士学位论文].杭州：浙江工商大学.2008
[5]尹文.信用评分卡在个人信贷中的应用研究[J].福建商业高等专科学校学报.2008,12(6)：37-39
[6]钱水土,黄震宇.信用评分模型在中小企业信贷评估中的应用[J].商业经济与管理,2004,(2)：57-61
[7]陈文华.信用评分模型与方法[J].中国信用卡,2007(3)：24-27
[8]Myers. J.,E.Forgy.The Development of Numerical Credit Evaluation Systems[J].Journal of the American Statistical Association,1963
[9]章彰.商业银行信用风险管理[M].北京：中国人民大学出版社,2002年11月
[10]朱忠明.金融风险管理学[M].北京：中国人民大学出版社,2004年12月
[11]R. J. Barro."The Loan Market, Collateral,and Rates of Interest,"Journal of Money, Credit and Banking,1976,8(4):439-456
[12]Joseph E.Stiglitz,Andrew Weiss.Credit Rationing in Markerts with Imperfect Information[J].The American Economic Review,1981(71),393-410
[13]Benjamin Klein,Keith B.Leffler.The Role of Market Forces in Assuring Contractual Performance[J].Journal of Political Economy,1981(89),615
[14]Yuk-Shee Chan,Geogre Kanatas.Asymmetric Valuations and the Role of Collateral in Loan Agreements[J].Journal of Money, Credit and Banking,1985(17),84-95
[15]Hayre,Lakhbir. Analyzing Prepayment and Credit Risk of Mortgage Securities[J].in Fixed-Income Investment:Recent Research.1995
[16]何爱武,孙海波.商业银行个人消费信贷风险分析及其对策[J].中国金融,2003(15)：33-3
[17]张亚珍.我国消费信贷的风险及其控制措施[J].商业研究,2003(20)：47-49
[18]陈杰.消费信贷中的潜在风险及成因[J].浙江金融,2006(3)：60-61
[19]任金政,陈宝峰,信息不对称与消费信贷信用风险管理[J].河南金融管理干部学院学报,2004(4)：16-18
[20]李可佳,蒋晓蓉.个人贷款业务风险不容忽视[J].西南金融,2004(8)：1-13
[21]杨华辉,健全信贷管理与防范消费信贷风险[J].当代经济科学,2006(1)120-121
[22]林均跃.消费者信用管理[M].北京：中国方正出版社,2002年8月
[23]钟波,肖智,刘朝林.基于LS-SVM的信用评价方法[J].统计研究,2005(11)29-31
[24]尚立红,黄立平.消费信贷中的行为评分研究[J].山东理工大学学报(社科版),2004(7)：15-17
[25]龙海明,邓杏秀.基于违约概率的消费信贷风险分析与度量[J].金融理论与实践,2006(1)：18-20
[26]王淳,国有商业银行消费信贷业务信贷风险管理现状分析及建议[J].现代商业银行导刊,2005(9)：22-25
[27]梁晓娟,建立个人信用体系发展个人信贷[J].河南金融管理干部学院学报,2003(6)：37-38
[28]张婷,建立“主银行”式的消费信货模式构想[J].财经科学,2004(3)：27-31
[29]刘志清.个人消费信贷业务的风险不容忽视[J].中国金融,2004(5)：45-46
[30]任欣.商业银行防范消费信贷风险的对策建议[J].财贸研究,2002(5)：36-38,74
[31]陈靓.基于数据挖掘的银行信用评分系统研究[D]：[硕士学位论文].沈阳：辽宁师范大学.2006
[32]陈建.信用评分模型综述[J].中国信用卡.2005年1月.46-47
[33]王静,王延清,何德权.基于多层前馈神经网络的个人信用评分模型[J].经济师,2004(12)：20-21
[34]李曙光.神经网络在消费者信用评分中的应用探讨[J].技术经济,2003(3)：50-52
[35]李瑞丽,沈薇.利用统计学方法建立消费信贷评分模型[J].经济与管理,2005(4)：23-26
[36]杨力,宋利等.信用评分的统计模型方法述评[J].知识丛林,2006(7)：141-142
[37]高国平,刘树安.基于径向基函数神经网络的信用评分模型研究[J].计算机技术与发展,2007(9)：11-14
[38]季峰,李勇等.基于SenV-RBF的个人信用评分模型[J].中国科学技术大学学报,2007(7)：767-772
[39]马海英.基于神经网络及Logistic回归的混合信用卡评分模型[J].华东理工大学学报 (社会科学版),2008(2)：49-52
[40]黄梦妮.浅析信用评分模型[J].商场现代化,2008(7)：143
[41]晏艳阳,莫如冰.基于多级模糊综合评判法的个人信用评分模型研究[J].财经理论与实践(双月刊),2009(3)：7-12
[42]易传和,彭江.基于FAHP的个人信用评分模型[J].统计与决策2009(15)：67-70
[43]石庆焱,靳云汇.多种个人信用评分模型在中国应用的比较研究[J].统计研究,2004(6)：43-47
[44]梁樑,吴德胜等.基于ANFIS和Elman网络的信用评价研究[J].管理工程学报,2005(1)：69-73
[45]杨力,童艳梅,阮守武,刘晓伟,吴德胜V-fold交叉验证和BP神经网络在信用评价中的应用[J].运筹与管理,2005(4),140-143
[46]朱晓明,程建等.基于反向传播算法神经网络的信用评分系统预测力研究[J].西安交通大学学报,2006(12)：1406-1409
[47]余文建,沈益昌,杜洋.基于Logistic模型的个人信用评分体系研究[J].海南金融,2007(3)
[48]张成虎,李育林.基于神经网络的个人信用评分模型研究[J].云南师范大学学报(哲学社会科学版),2008(11)：87-93
[49]廖绚,李兴绪.基于Logit模型的银行个人信贷风险管理评估[J].统计与决策,2008(21)：50-52
[50]张成虎,李育林,吴鸣.基于判别分析的个人信用评分模型研究与实证分析[J].大连理工大学学报(社会科学版)2009(1)：6-10
[51]杨建模,杨胜刚.基于数据包络分析的企业信用评分方法[J].生存力研究2009(24)：95-97
[52]尚立红,黄立平.消费信贷中的行为评分研究[J].山东理工大学学报(社会科学版),2004(4)：15-17
[53]傅继波,杨朝军.上市公司财务困境预测的信用评分模型[J].浙江工业大学学报,2005(10)：583-585
[54]杨文瀚,刘思峰.基于灰关联度硇企业信用风睑评分方法及应用[J].商业研究,2005(20)：22-24
[55]李猛,沈菊菊.基于数据挖掘的信用卡授卡风险评估模型[J].中国信用 卡,2006(11)：56-58
[56]刘昭亮.基于信用评分技术的信用卡风险管理策略[J].科技创业,2007(8)：949-8
[57]刘莉亚.商业银行个人信贷信用评分模型的构建与应用[J].财经研究,2007(2)：26-36
[58]林铮.试论国家助学贷款信用评分指标体系的构建[J].内江科技,2007(7)：41
[59]陈颖,鲍正新.零售信用风险内部评级法研究[J].金融风险管理.2008(6)：31-37
[60]张博文.电信运营客户信用评分建模研究[J].中国科技信息,2009(10)：113-114
[61]方磊.论信用评分在我国个人房贷违约分析中的应用[J].现代商贸工业.2009(3)
[62]林莉.国家助学贷款信用评分模型的构建及运用研究[J].高教探索2009(2)：131-134
[63]李孟来.我国个人信用评分模型的应用探讨[J].金融管理与研究2009(2)：51-53
[64]Yoba, M. B.,Crook, J. N.,&Ross, P.,Credit scoring using neural and evolutionary techniques, IMA Journal of Maths Applied in Business and Industry,1996,11:111-125
[65]Robert, B.,Paul,S,Consumer credit scoring:Do situational circumstances mater?,Journal of Banking & Finance,2004,28:835-856
[66]Crook, J. N.,Banasik, J.,Samples election bias in credit scoring models, Journal of Operational Research Society,2003,54:822-832
[67]Crook, J. N., Banasik, J.,Does reject inference really improve the performance of application scoring models?,Journal of Banking & Finance,2004,28:857-874
[68]党信涛.商业银行消费信贷个人客户信用评分探讨[D]：[硕士学位论文].贵阳：贵州大学.2006
[69]吴俊.银行个人信用评分方法及其应用研究[D]：[硕士学位论文].无锡：江南大学.2008
[70]尚立红.我国商业银行消费信贷中的个人信用评分方法及应用研究[D]：[硕士学位论文].上海：同济大学管理科学与工程,2005
[71]何振亚.中国消费信贷发展回顾与展望[J].上海金融2009(3)：16-19
[72]潘丽娟.我国商业银行发展消费信贷业务的策略分析[J].内蒙古科技与经济.2009(3)：35-38
[73]曾辉.基于决策树的银行个人客户信用评分模型的研究[D]：[硕士学位论文].北京：对外经济贸易大学.2007
[74]钟楚男.个人信用征信制度[M].北京：中国金融出版社.2002年11月