基于信用评分模型的小微企业贷款的可获得性研究
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摘要
小微企业是维系“国计”和“民生”的重要支柱,应该得到大力的支持和长足的发展,但是其融资状况却与国民经济经济中的重要性形成鲜明对比。为了改善小微企业的融资状况,从国务院、发改委、工信部、金融监管部门到各级地方政府出台了一系列政策措施,尤其针对如何提高小微企业贷款可获得性、拓宽小微企业金融服务覆盖面的问题。而商业银行迫于外部竞争压力和内部利润增长驱动,也需要逐渐调整信贷结构,增加对小微企业贷款的力度。
     在中国小微企业贷款业务大发展的时机,深入剖析小微企业贷款可获得性的现状及主要障碍,借鉴国外小企业信用评分模型的做法和经验,探索中国小企业信用评分模型开发的技术和策略,有助于提高中国小微企业贷款的可获得性。
     首先,论文分析了中国小微企业贷款的可获得性不高的现状及原因。通过供给和需求比较得出:商业银行在贷款客户选择上偏爱规模较大的企业,冷落微型企业;小微企业贷款的可获得性不高。运用利润函数分析得出,银行在贷款客户选择上的厚此薄彼行为是基于追求利润最大化的动因,并指出应该从降低小微企业贷款的经营成本和贷款风险的角度入手,提高小微企业贷款的利润,解决信贷配给的问题。
     然后,论文从基本原理入手论述了小企业信用评分模型对于提高小微企业贷款可获得性的有效性,以Fair Isaac与RMA开发的SBCS模型为例介绍了其基本原理,紧接着运用富国银行的案例,证明了小企业信用评分可以有效降低运营成本和风险,并运用动态博弈模型,对比引入小企业信用评分前后的变化,论述了其在降低小微企业的道德风险,提高贷款的可获得性方面的有效性。
     在此基础上,论文从模型开发和应用两个方面具体论述了小企业信用评分模型如何实施。论文详细介绍了模型开发的基本流程和关键技术,包括样本的选择与变量的分组、模型的创建与检验、模型的实施与调整、模型的监测与跟踪;并且运用信息统计量进行变量的筛选和粗分组,结合Logistic回归构建了信用评分模型,并开发了简易、可操作性强的信用评分卡。
     最后,论文总结了美国、日本、意大利、俄罗斯等其他国家在开发和应用该模型时的经验和教训,并提出了对中国的借鉴意义。首先分析了中国商业银行应用小企业信用评分模型的现状、不足之处及有利条件,并从商业银行角度提出转变信用观念、合作开发等建议,从政府角度提出转变监管理念、做好数据服务和对小微企业贷款的动态监测等建议。
As an important pillar to maintain“national planning”and“livelihood”,small andmicro enterprises (abbreviated as SMEs) should receive strong support and achievedsignificant development. But its financing situation is contrast in stark to its important roleof national economy. In order to improve SMEs’ credit availability and access to financialservices,a series of policies and measures have been formulated by central and localgovernments,and financial regulatory authorities etc. Meanwhile commercial banks alsoneed to gradually adjust their credit structures and increase loans to SMEs driven byexternal competitive pressures and internal intentions to achieve profit growth.
     Under this background,it’s helpful for improving SMEs’ credit availability to carryout this study,whose objective is to explore how to developand apply small business creditscoring (abbreviated as SBCS) model in China,based on in-depth analysis of currentsituation of and main obstacles to SMEs’credit availability,and learning from foreignpractices and experience.
     Firstly,this paper analyzes the current situation and reasons of low credit availabilityof Chinese SMEs. Comparisons are carried between supply and demand, that commercialbanks prefer larger enterprise to micro-enterprises,and SMEs are not satisfied with loansthey can obtained. Reasons are found by profit function analysis that discriminatorybehavior on customer choice of bank loans is based on the motive to maximize profit,sothe way to solve credit rationing is to improve profits by reduce operating costs and defaultrisk of loans to SMEs.
     Secondly,after introduction to the basic principles of SBCS model with example ofFair Isaac and RMA,this paper discusses effectiveness of the SBCS model to improve thecredit availability of SMEs from two aspects. One is to prove SBCS can reduce operational costs and risks using the case of Wells Fargo. The other is to prove SBCS can reduce moralhazard by dynamic game model,and contrast SMEs’ repayment decision before and afterthe use of SBCS,so as to improve the credit availability of SME’s.
     Thirdly,this paper answers how to implement SBCS model in china from two anglesof development and application.The basic development process and key technologies aredetailed from sample selection and variable grouping,creating and testing,implementationand adjustment,monitoring and tracking of the model. In order to demonstrate aboveprocess,this paper continues to construct a credit scoring model and a simple,operativecredit score card through logistic regression,using information statistic to screen andcoarsely group variables.
     Lastly, this paper summarizes experiences and lessons about how to develop andapply SBCS model in the other countries,such as United States,Japan,Italy,Russia etc.,and discuss their reference value to China. Based analyzing SBCS model’s applicationstatus,deficiencies and favorable conditions in China,suggestions are made from theperspective of commercial banks and government respectively,around changing creditconcept and cooperative development for the previous,and supplying with better dataservice and dynamic monitoring of loans to SMEs for the latter.
引文
[1]巴曙松等.2013小微企业融资发展报告——中国现状及亚洲实践[R].博鳌观察、中国光大银行、中国中小企业发展促进中心,2013。
    [2]陈建.信用评分模型技术与应用[M].中国财经出版社,2005。
    [3]陈静.上市公司财务恶化预测的实证分析[J].会计研究,1999(4):31-38
    [4]陈蕾.信息不对称视角下的中小企业融资困境分析[J].投资研究,2011(10):56-64.
    [5]邓超、胡威、唐莹,国内外小企业信用评分研究动态[J].国际金融研究,2010(10):84-91.
    [6]邓超、胡威、唐莹.基于拒绝推论的小企业信用评分模型研究[J].国际金融研究,2011(4):68-76
    [7]杜淼淼.美国个人信用评分系统及其启示[J].南方金融,2008(8):63-66.
    [8]福建省金融学会、美国阿波罗金融科技公司联合课题组.信息结构与金融技术支撑:小企业信贷市场的实证研究[J].金融研究,2003(11):95-103.
    [9]国家统计局浙江调查总队课题组.正视小微企业融资难——浙江小微企业资金现状与发展策略研究[J].浙江经济,2012(20):34-35
    [10]湖北经济学院金融学院,中国社会科学院财贸所课题组.政策环境、金融结构与信贷技术——化解中小企业贷款难题的系统解决方案[J].财贸经济,2008(9):5-15.
    [11]湖北经济学院金融学院课题组.政策环境、金融结构与信贷技术——化解中小企业贷款难题的系统解决方案[J].财贸经济,2008(9):5-15
    [12]胡援成、田满文.上市公司财务困境预测模型的再比较[J].经济学(季刊),Vol.4增刊,2005(10):173-188
    [13]姜涛.小企业贷款与信息利用[J].银行家,2006(9):101-103。
    [14]姜涛.小企业信用评分模型的国际经验及其在中国的可行性研究[D].北京:中国人民银行金融研究所,2007.
    [15]林立.富国银行小微金融业务发展——金融功能观视角的分析[J].投资研究,2012(11):20-32.
    [16]林毅夫,李永军.中小金融机构发展与中小企业融资[J].经济研究,2001(1):10-18.
    [17]李扬,日本的主银行制度[J].金融研究,1996(5):59-62
    [18]黎玉华.信用评分卡模型的建立[J].科技信息,2010(13):464-465
    [19]李志赟.银行结构与中小企业融资[J].经济研究,2002(6):38-45.
    [20]卢志红.信用评分模型在我国企业贷款评估中的应用研究[D].天津大学管理学院,2004.
    [20]鲁丹,肖荣华.银行市场竞争结构、信息生产与中小企业融资[J].金融研究,2008(5):107-113.
    [21]潘慧.中国小企业信用评分的实践及建议[J].征信,2010(1):23-25
    [22]曲吉光,徐东风,姜春.纳什均衡:民营企业从国有商业银行取得贷款难的经济学解释[J].金融研究,2005(1):154-163.
    [23]史建平,杨如冰.信贷技术在中小企业金融服务中的应用[J].中央财经大学学报,2009(6):22-25.
    [24]施向军.我的名字叫“小微”——中国小微企业生存现状面面观[J].中国检验检疫,2012(8):4-8.
    [25]宋红晶.应用数据包络分析法对中国上市中小企业的信用评分研究[D].华东师范大学,2011.
    [26]苏存.信用缺失研究[J].金融研究,2005(10):162-170.
    [27]唐志鹏.基于评分法的小额信贷信用风险管理研究[D].西南财经大学,2011.
    [28]王凯,中小企业信用评估模型及应用[D].安徽农业大学.2007.
    [29]汪莉.基于Logistic回归模型的中小企业信用评分研究[D].合肥工业大学,2008
    [30]王兴娟.小微企业融资背景、困境及对策[J].学术交流,2012(7):118-121
    [31]吴德胜,梁树,杨力.不同模型在信用评价中的比较研究[J].预测,2004(2)
    [32]吴世农、卢贤义.中国上市公司财务困境的预测模型研究[J].经济研究,2001(9):46-55
    [33]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(双月刊),2011(7).
    [34]杨力,汪克亮,王建民.信用评分主要模型方法比较研究[J].经济管理,2008(6)
    [35]杨绍基,范闽.信用评分模型的拒绝偏差与Heckit纠正[J].南方金融,2007(5)
    [36]张玲.财务危机预警分析判别模型[J].数量经济技术经济研究,2000(3):49-51
    [37]赵岩青,何广文.声誉机制、信任机制与小额信贷[J].金融论坛,2008(1):33-40.
    [38]赵子铱,邹康.信用评分模型与中小企业贷款[J].财会月刊(综合),2006(2):34-35
    [39]曾翰文.小额信贷信用评分审批系统的研究[D].西南财经大学,2011.
    [40]朱艳敏,陈超. Z-score模型最优分割点的确定方法比较——基于违约风险预测能力的分析[J].南方金融,2013(8):74-77.
    [41]朱艳敏,王光伟.商业银行债券资产配置行为研究——基于上市银行面板数据的实证分析[J].金融论坛,2013(8):3-9.
    [42] A.J. Feelders.An Overview of Model Based Reject Inference for Credit Scoring[R].Working Paper,2003
    [43] A.J. Feelders. Credit Scoring and Reject Inference With Mixture Models[J].International Journal of Intelligent Systems in Accounting, Finance&ManagementInt. J. Intell. Sys. Acc. Fin. Mgmt.2000(9):1-8
    [44] Allen N. Berger, Adrian M. Cowan, and W. Scott Frame.The Surprising Use of CreditScoring in Small Business Lending by Community Banks and the Attendant Effectson Credit Availability and Risk[R]. Federal Reserve Bank of Atlanta WorkingPaper,2009March
    [45] Altman, E.I. Financial ratios, discriminant analysis and the prediction of corporatebankruptcy[J]. Journal of Finance1968, September:589-609.
    [46] Altman, E.I., GABRIELE SABATO. Modelling Credit Risk for SMEs: Evidencefrom the U.S. Market[J]. Working Paper, vol.43, no.32005December.
    [47] Altman, E.I., YOUNG Ho Eom. Failure Prediction:Evidence from Korea[J]. Journalof International Financial Management and Accounting,6:31995:230-248.
    [48] Antonio Blanco,Ana Irimia,Maria Dolores Oliver.Credit scoring model for smallfirms in the UK using logistic regression[D]. University of Seville,2011
    [49] Arito Ono. The Current Status of Small Business Credit Scoring in Japan: based uponsurvey evidence on its use by Japanese banks[R].Mizuho Research Paper, August2005.
    [50] ASCH, L.. How the RMA/Fair Isaac credit-scoring model was built[J].Journal ofCommercial Lending, June,2011(10):10-16.
    [51] ASCH, L.. Streamlined decision making in the small-business arena[R].Fair IsaacConference1994.
    [52] Banasik, J.L., Crook, J.N., Thomas, L.C.,. Sample selection bias in credit scoringmodels[J].Journal of the Operational Research Society,2003(54):822-832.
    [53] Banasik, J.L., Crook, J.N.,. Credit scoring, augmentation and lean models[J]. Journalof the Operational Research Society,2005(56),1072-1091.
    [54] Berger, Allen, and Gregory Udell. Relationship Lending and Lines of Credit in SmallFirm Finance[J].Journal of Business68:3,1995(July):351-81.
    [55] Berger, Allen N. and Gregory F. Udell: A more complete conceptual framework forSME finance[J]. Journal of Banking and Finance, Volume30, Issue11, November2006, Pages2945-2966
    [56] Berger, Allen N. and Gregory F. Udell. Small Business Credit Availability andrelationship Lending: The Importance of Bank Organizational Structure[J].EconomicJournal,2002(112), F32-F53.
    [57] Berger, Allen N., and Robert DeYoung.Technological Progress and the GeographicExpansion of the Banking Industry[J].Journal of Money, Credit, and Banking. Vol.38(6),2006September, pages483-1513.
    [58] Berger, Allen N., W. Scott Frame, and Nathan Miller. Credit Scoring and theAvailability, Price, and Risk of Small Business Credit[J]. Journal of Money, Credit,and Banking,2005(37),191-222.
    [59] Berger, Allen N., W. Scott Frame. Small Business Credit Availability[R]. FederalReserve Bank of Atlanta Working Paper,2005-10.
    [60] Charles D. Cowan and Adrian M. Cowan. A Survey Based Assessment of FinancialInstitution Use of Credit Scoring for Small Business Lending[R].SBA Office ofAdvocacy working paper,November,2006.
    [61] Crook, J.N., Banasik, J.L.,. Does reject inference really improve the performance ofapplication scoring models?[J]. Journal of Banking and Finance,2004(28):857-874.
    [62] David Snyder,Tim O’Brien.Recommendations on the Use of Credit Scoring for Microand SME Lending in Russia[R]. The Financial Services Volunteer Corps,April2011.
    [63] D.B. Rubin. Inference and missing data[J].Biometrika,1976,Volume63,Issue3, pages581-592.
    [64] D.C.Hsia.Credit Scoring and the Equal Credit Opportunity Act[J].Hastings Law,1978(30):371-448
    [65] Edward I. Altman,Anthony Saunders.,Credit risk measurement: Developments overthe last20years[J].Journal of Banking&Finance,1998(21):1721-1742
    [66] FAIR ISAAC. Small business scoring[R]. Fair Isaac and Company, Inc., September1993.
    [67]FAIR ISAAC. Small business scoring service-credit desk[R]. Fair Isaac and Company,Inc., April1995.
    [68] Feldman, Ronald,.Small Business Loans, Small Banks and a Big Change inTechnology Called Credit Scoring[J].Federal Reserve Bank of Minneapolis,1997.September,19-25.
    [69] Frame, W. Scott, Michael Padhi, and Lynn Woolsey.The Effect of Credit Scoring onSmall Business Lending in Low-and Moderate Income Areas[J].Financial Review,2004(39):35-54.
    [70] Frame, W.S., Srinivasan, A., and Woosley, L..The effect of credit scoring on smallbusiness lending[J].Journal of Money, Credit and Banking,2001(3), pp.813-825.
    [71] Gongyue Chen,Thomas stebro. A Maximum Likelihood Approach for RejectInference [J]. Rotman School of Management Working Paper No.07-05,September,2005
    [72] Hand, D.J. and Henley, W.E.. Can reject inference ever work?[J].IMA Journal ofMathematics Applied in Business and Industry,1993(5):45-55.
    [73] Heckman, J.. Sample selection bias as a specification error[J]. Econometric,1979(47),153-161.
    [74] Hussein Abdou, Ahmed El-Masry, John Pointon. ON THE APPLICABILITY OFCREDIT SCORING MODELS IN EGYPTIAN BANKS[J]. Banks and Bank Systems/Volume2, Issue1,2007:4-20
    [75] John Banasik,Jonathan Crook. Reject inference,augmentation, and sample selection[J]. European Journal of Operational Research,2007(183)1582-1594Joseph E. Stiglitz and Andrew Weiss,Credit Rationing in Markets with Imperfect
    [76] Information[J]. The American Economic Review,Vol.71, No.3(Jun.,1981), pp.393-410
    [77] Kallberg, J., and G. F. Udell. Private Information Exchange in the United States InCredit Reporting Systems and the International Economy[M]. M. Miller, ed.Cambridge, Mass.: MIT Press,2003.
    [78] Kwan and Tan. Credit Scoring for Commercial Loans:The Case of Singapore[J]. AsiaPacific Journal of Management, September1986
    [79] Lewis, E. M., An Introduction to Credit Scoring[M].Athena Press: San Rafael,1992.
    [80] Makuch,W. M. The Basics of A Better Application Score. In Credit Risk ModelingDesign and Application[M].ed E. Mays. Chicago: Glenlake Publishing,1998.
    [81] Marcello Bofondi and Francesca Lotti. Innovation in the Retail Banking Industry: theDiffusion of Credit Scoring [J].Review of Industrial Organization,2006(28):343-358
    [82] Margaret Miller,Dina Rojas. Improving Access to Credit for SMEs: An EmpiricalAnalysis of the Viability of Pooled Data SME Credit Scoring Models in Brazil,Colombia&Mexico[R].The World Bank Working Paper,October8,2004.pp.1891-1921
    [83] Meester, S. Reject Inference for Credit Scoring Model Development UsingExtrapolation[M]. Mimeo, New Jersey: CIT Group.,2000.
    [84] Mester, Loretta J.. What’s the Point of Credit Scoring?[J]. Federal Reserve Bank ofPhiladelphia Business Review, September/October1997,3-16.
    [85] Miller, M., Credit Reporting Systems and the International Economy[M]. Cambridge:MIT Press,2003.
    [86] Mohamad Iwan, Bankruptcy Prediction Model With ZetaCOptimal Cut—Off Score toCorrect Type I Errors[J]. Gadjah Mada International Journal of BusinessJanuary-April2005, Vol.7, No.1,pp.41-68.
    [87] Nakamura, Leonard. Recent Research in Commercial Banking: Information andLending[R].Federal Reserve Bank of Philadelphia Working Paper, Number93-24,September1993.
    [88] Oppenheim, Sara. Would Credit Scoring Backfire in a Recession?[J]. AmericanBanker, November18,1996,16.
    [89] R.J.A. Little and D.B. Rubin. Statistical analysis with missing data[M]. John Wiley&Sons, New York,1987.
    [90]RMA&FAIR ISAAC,. Small business scoring service[M]. Robert Morris Associates,1995.
    [91] Robert A. Eisenbeis. Recent developments in the application of credit-scoringtechniques to the evaluation of commercial loans[J]. IMA Journal of MathematicsApplied in Business&Industry,1996(7):271-290
    [92] Ryo Hasumi, Hideaki Hirata, and Arito Ono. Differentiated Use of Small BusinessCredit Scoring by Relationship Lenders and Transactional Lenders: Evidence fromFirm-Bank Matched Data in Japan[R].Working Paper,October,2011.
    [93] Ryo HASUMI,Hideaki HIRATA. Small Business Credit Scoring: Evidence fromJapan[R]. RIETI Discussion Paper Series10-E-029June2010
    [94] Stein,J.C., Information production and capital allocation: Decentralized vs.hierarchical firms [J].Journal of Finance,.2002(57)
    [95] Thomas, L. C., Edelman D. B. and Jonathan N. Crook, Credit Scoring and ItsApplication[J].SIAM monographs on mathematical modeling and Computation,Philadelphia2002.
    [96] William Greene.Sample selection in credit-scoring models[J]. Japan and the WorldEconomy,1998(10):299-316
    [97] William H·Beaver.Financial ratios as predictors of failure,Emprrical research inaccounting: Selected studies[J]. Journal of Accounting Reasearch,V,supplement1966.
    [98] Williamson, Oliver.The Economics of Defense Contracting: Incentives andPerformance [M].In Issues in Defense Economics, edited by R. McKean. New York:Columbia University Press,1967.
    [99] World Bank.Development of Financial Leasing in Lithuania[R]. World BankCorp.,December,2000.

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