One of the main tasks of credit rating is to distinguish different clients using loss given default (LGD) parameter. However, it is very common in reality that many credit rating systems seem properly if we only look at their indicators, customers with higher credit rating still have higher corresponding LGD in those systems. Therefore, an ideal credit rating result should meet the credit risk-rating match-up standard that the credit rating increases with the decreasing LGD. Secondly, the existing credit rating results don't match with the target profit point of commercial banks, so the credit rating approaches do not have the functions of credit decisions. Bankers or credit clerks cannot locate the qualified customers who can realize the bank's target profit.
To fill in the above gaps, this paper advances in three aspects. First, based on considering small private businesses' LGD and achieving banks' target profit, a novel credit rating ideal is put forward that the higher credit rating comes with the lower corresponding LGD. Second, a nonlinear programming credit rating model which is consisted of two objective functions and three constraint conditions has been created. The constraint condition 0 < LGD1 < LGD2 < … < LGD9 ≤ 1 can ensure that the credit rating result meets the credit risk-rating match-up standard. And the objective function max f=(N1 + N2 + … + Nj)/N can guarantee that the number of customers to get loans on the premise of achieving banks' goals profits is maximal. It reflects the inclusive finance concept that credit funds benefit more small private business. Third, by using a Chinese national commercial bank's 2157 small private business microfinance loan samples from 29 provinces, the proposed model has been verified. The empirical results show that by designing a reasonable credit rating mechanism, we can go out of the dilemma that commercial banks were reluctant to lend, the plight of honest merchants can not be got loans.
Our research not only has practical significance for credit risk assessment of the 2157 small private business microfinance, but also offers a new insight into credit risk decision evaluation of customers in other commercial banks in the world. In addition, a new reference is provided for CBRC and commercial banks practicing the inclusive finance.
SHI Bao-feng, WANG Jing, CHI Guo-tai
. The Inclusive Finance, Bank Loans and Financing of Small Private Business Microfinance Loan-Based on Matching Credit Risk and Credit Rating[J]. Chinese Journal of Management Science, 2017
, 25(9)
: 28
-36
.
DOI: 10.16381/j.cnki.issn1003-207x.2017.09.004
[1] 国家工商总局、教育部、工业和信息化部、中国社会科学院"个体私营经济与就业关系研究"课题组. 中国个体私营经济与就业关系研究报告[EB/OL]. (2015-10-26)[2016-10-09]. http://www.saic.gov.cn/zwgk/tjzl/zxtjzl/xxzx/201510/t20151030_163438.html.
[2] 中华人民共和国国务院政府信息公开专栏. 中共中央国务院关于加大改革创新力度加快农业现代化建设的若干意见[EB/OL]. (2015-02-01)[2016-10-10]. http://www.gov.cn/zhengce/2015-02/01/content_2813034.htm.
[3] 国务院新闻办公室网站. 国务院关于印发推进普惠金融发展规划(2016-2020年)的通知[EB/OL]. (2016-02-02)[2016-10-09].http://www.scio.gov.cn/xwfbh/xwbfbh/wqfbh/33978/34138/xgzc34144/Document/1467175/1467175.htm.
[4] 李东荣. 拉美小额信贷监管经验及对我国的启示[J]. 金融研究, 2011,(5):1-12.
[5] 刘西川,杨奇明,陈立辉. 农户信贷市场的正规部门与非正规部门:替代还是互补?[J]. 经济研究, 2014, 49(11):145-158+188.
[6] Bester H. The role of collateral in credit markets with imperfect information[J]. European Economic Review, 1987,31(4):887-899.
[7] 赵岳,谭之博. 电子商务、银行信贷与中小企业融资-一个基于信息经济学的理论模型[J]. 经济研究,2012,47(7):99-112.
[8] 孙天琦. 制度竞争、制度均衡与制度的本土化创新-商洛小额信贷扶贫模式变迁研究[J]. 经济研究, 2001, (6):78-84.
[9] 田剑英,黄春旭. 民间资本金融深化与农村经济发展的实证研究-基于浙江省小额贷款公司的试点[J]. 管理世界, 2013, (8):167-168.
[10] 汪昌云,钟腾,郑华懋. 金融市场化提高了农户信贷获得吗?-基于农户调查的实证研究[J]. 经济研究, 2014,49(10):33-45,178.
[11] 吴勇. 农村中小企业信贷融资问题博弈分析[J]. 管理世界, 2015, (1):171-172.
[12] 石宝峰,刘锋,王建军,等. 基于PROMETHEE-Ⅱ的商户小额贷款信用评级模型[J]. 运筹与管理, 2016, 26(8):1-11.
[13] Akkoc S. An empirical comparison of conventional techniques, neural networks and the three stage hybrid Adaptive Neuro Fuzzy Inference System (ANFIS) model for credit scoring analysis:The case of Turkish credit card data[J]. European Journal of Operational Research, 2012, 222(1):168-178.
[14] Ju Y H, Sohn S Y. Updating a credit-scoring model based on new attributes without realization of actual data[J]. European Journal of Operational Research, 2014, 234(1):119-126.
[15] 迟国泰,张亚京,石宝峰. 基于Probit回归的小企业债信评级模型及实证[J]. 管理科学学报, 2016, 19(6):136-156.
[16] Altman E I. Financial ratios, discrimination analysis and the prediction of corporate bankruptcy[J]. Journal of Finance, 1968, 23(4):589-609.
[17] 马九杰,郭宇辉,朱勇. 县域中小企业贷款违约行为与信用风险实证分析[J]. 管理世界, 2004, (5):58-66, 87.
[18] Baesens B, Setiono R, Muse C,et al. Using neural network rule extraction and decision tables for credit-risk evaluation[J]. Management Science, 2003, 49(3):312-329.
[19] Karlan D, Zinman J. Microcredit in theory and practice:Using randomized credit scoring for impact evaluation[J]. Science, 2011, 332(6035):1278-1284.
[20] 陆爱国,王珏,刘红卫. 基于改进的SVM学习算法及其在信用评分中的应用[J]. 系统工程理论与实践, 2012,32(3):515-521.
[21] Shi Baofeng, Chen Nan, Wang Jing. A credit rating model of microfinance based on fuzzy cluster analysis and fuzzy pattern recognition:Empirical evidence from Chinese 2157 small private businesses[J]. Journal of Intelligent & Fuzzy Systems, 2016, 31(6):3095-3102.
[22] 中国工商银行. 关于印发《中国工商银行小企业法人客户信用等级评定办法》的通知[R]. 工银发
[2005] 第78号, 2005:5-7, 13-21.
[23] Raquel F. Modelling of insurers' rating determinants:An application of machine learning techniques and statistical models[J]. European Journal of Operational Research, 2007, 183(3):1488-1512.
[24] Min J H, Lee Y. A practical approach to credit scoring[J]. Expert Systems with Applications, 2008, 35(4):1762-1770.
[25] 寇纲,娄春伟,彭怡,等. 基于时序多目标方法的主权信用违约风险研究[J]. 管理科学学报, 2012, 15(4):81-87.
[26] 迟国泰,潘明道,齐菲. 一个基于小样本的银行信用风险评级模型的设计及实证[J]. 数量经济技术经济研究, 2014,(6):102-116.
[27] Shi Baofeng, Wang Jing, Qi Junyan, et al. A novel imbalanced data classification approach based on logistic regression and Fisher discriminant[J]. Mathematical Problems in Engineering, 2015, 2015:1-12.
[28] 王鹏飞,李畅. 不确定多属性决策双目标组合赋权模型研究[J]. 中国管理科学, 2012, 20(4):104-108.
[29] 中国邮政储蓄银行,大连理工大学. 中国邮政储蓄银行商户小额贷款信用风险决策评价系统研究结项报告[R]. 中国邮政储蓄银行、大连理工大学, 2012.
[30] 中国邮政储蓄银行,大连理工大学. 中国邮政储蓄银行农户小额贷款信用风险决策评价系统研究结项报告[R]. 中国邮政储蓄银行、大连理工大学, 2012.
[31] 大连银行,大连理工大学. 大连银行小企业信用风险评价与贷款定价系统研究结项报告[R]. 大连银行、大连理工大学, 2015.
[32] 薛锋,柯孔林. 粗糙集-神经网络系统在商业银行贷款五级分类中的应用[J]. 系统工程理论与实践, 2008, 28(1):40-45, 55.
