Study On Personal Mortgaged Loan Risk Evaluation Of C Rural Commercial Bank

In the past,in the personal credit risk assessment of housing loans,the bank staff based on the borrower's credit status and consider the value of real estate,based on the analysis of the business staff to determine whether to grant loans.In the process,it is often easy to reduce the quality of loans due to human negligence,misjudgment,peer competition and internal fraud,increase the default risk,reduce external interference and establish a set of standard credit risk assessment Model to reduce the default risk of the borrower is an important issue for bank risk control.In order to solve the above problems,this paper takes C-firm as the research object,and draws a sample of 389 cases of individual-home mortgages issued from 2007 to 2016 as the research sample,346 of whom are regular repayments and 43 are default clients We choose five main factors and 21 index variables,including personal attributes,loan attributes,real estate attributes,regional economic attributes,and bank relationship attributes of the borrower.We construct a credit risk assessment index system and use KS test to select 10 significant Of the explanatory variables used to establish a credit risk assessment model,and the use of Logistic regression model and discriminant analysis model empirical research,and finally the use of ROC curve analysis of the effectiveness of the two models are compared.The empirical results show that there are four variables associated with the risk of default significantly related to the use of Logistic regression model,income repayment ratio,age,repayment method and the risk of default is positively correlated,while the number of business transactions and the risk of default is negatively correlated.There are five variables in the discriminant analysis model which are significantly correlated with the default risk.The variables entering the model have one more risk factor than the Logistic regression model,namely,the number of the banks in service and the rest are the same;meanwhile,the absolute value of the coefficient of the loan-The second is the number of business transactions between banks,repayment methods,building age,and the bank's business from the negative coefficient of the negative and the absolute minimum.The results of ROC curve analysis show that the predictive accuracy of Logistic regression model and discriminant analysis model are both above 90%,but the prediction ability of the former is higher than the latter.Therefore,C-firm can reduce credit risk by controlling key risk factors,such as reducing income-to-loan ratio and restricting housing loans for the elderly.At the same time,it should build a scientific risk evaluation system,establish credit customer information database,improve risk evaluation index system and establish credit risk.In addition,the internal control of banks should be strengthened,such as strict internal control system,operational risk control,and training system.