Lasso Selection Of Loan Indicators And Copula Assessment Of Small And Micro Businesses In Minsheng Bank

Small and micro businesses is one of the three core customers in Minsheng Bank.Minsheng Bank has become the world’s largest small and micro financial serviceproviders. High credit risk and high labor costs are the main characters in small andmicro business. How to reduce the non-performing loan ratio is the key for sustainabledevelopment. A guarantee is to select high quality customers from numerous applicant.Based on applicant data, conclusion was gained to provide reference and help of to thedevelopment of small and micro finance by quantitative analysis.24indicators were complied from application materials of Commercial Sense inMinsheng Bank TJ Branch. Credit indicator was taken as the dependent variable andother variables as independent variables. Three algorithms, Least Angle Regression,Elastic Network and Group Lasso were chosen. The results found that Lasso had thestrongest capacity of variable selection, Elastic Network was the middle, and GroupLasso was the weakest. In a whole, enterprises industry had the greatest efect on credit.Industries of general construction and transportation equipment were easy to get loans.Meanwhile, enterprises engaged in metal and daily necessities were difcult to get moreloans.The Lasso regression showed that engaged industry of small and micro enterpriseshas the greatest influence for loan. It happened to have the same view with the strategyof”building professional branch” in Minsheng Bank. Successful examples were FZbranch and QZ branch. The imperfection of traditional small and micro business wasanalyzed and upgrading version characteristics were put forward. Details of structureoptimization measures, guarantee conditions, small and micro business risk counter-measure, internal system construction and after-sales management came up to improvesmall and micro finance.Copulas is actually a multivariate distribution function. It is mainly used for mul-tivariate time series modeling of independent identically distributed case. We originallyused Coupla as comprehensive evaluation. According to the experience, four indica-tors, marital status, existing lines, total assets and engaged industry were chosen asevaluation indexes system for loan applicant ability. Each index’s distribution was fit-ted separately and the cumulative distribution function was taken as standardization. Standardized value obeyed uniform distribution of [0,1] interval. Based on a goodness-of-fit test from experience copula, five copula, Normal Copula, t Copula, Gumbel Cop-ula, Frank Copulas and Clayton Copula, were taken as candidates and the optimum tCopula was t Copula. Parameters was estimated by.Maximum likelihood Estimates.Finally Copula distribution function was taken as evaluation value which gave capacityranking of applicant lending. The order conformed to the actual credit ranking results.According to Minsheng bank summary data, saving and loan indicators were se-lected for efciency evaluation. Accumulate DEA, Bootstrap DEA, Dual DEA, SuperDEA were applied to calculate efciencies of each branch. The results of four modelwere almost the same. CG branch had the lowest efciency. and WH branch followed.The efciencies of TY, KM, JN, TJ, ZZ and NC branches distributed between0.8and0.9. ST branch’s efciency was close to one and had the highest efcient in all thetechnical inefciencies. Finally the ranking of each branch was computed by SFA.