A Comparative Study On Consumer Credit Assessment Methodology Using Zhima Credit

Withthe continuous rise and vigorous development of consumer finance in recent years,micro-credit has been provided to borrowers from different social classes,which has effectively stimulated the growth of the consumptive market.However,due to the asymmetry of information and the lack of a sound credit system,the credit risk of borrowers is inevitable,which may infringe stakeholder's rights and increase the burden of consumer finance companies.Thus,anincreasing number of consumer finance companies choose to cooperate with third-party technology companies to conduct risk assessment,such as Zhima Credit,Tongdun Credit,Miyuan Credit,etc.Third-party technology companies would cross-check borrowers' credit through multiple information sources,and assess their credit status.The method is a good complement to the credit system of the People's Bank of China.With the support of third-party data,the main question of this dissertation is which assessment method can better identify the defaults of borrowers and diminish the risk.Based on the question,this dissertation focuses on different indicators of borrowers,and adopts qualitative analysis to compare and classify these indicators,and to identify how credit risk is influenced by these indicators.This dissertation potentially provides an innovative and comprehensive assessment methodology for future qualitative analysis.This dissertation is divided into three sections.First,consumer finance and current consumer credit assessment method would be reviewed in section one.Second,in section two,default is selected as an independent variable,and 14 dependent variables are selected,including the customer's job,income,education,city,loan term,interest rate,gender,marital status,owned property,age,lengthofemployment,loan amount,receiving channels,and Zhima Credit.A descriptive statistical analysis is adopted with the 14 indicators,and the results show that divorced male borrowers aged between 26-35,with junior high school education or below,working in business or trade industry for 5-10 years,and with no real estate and low Zhima Credit have the highest risk of default.Third,in section three,a machine learning method is used to screen 2513 sample data,and five often used assessment models-Logistic Regression,Random Forest Model,Support Vector Machine,Artificial Aeural Network,and Naive Bayesian algorithm,are used to evaluate and compare the risk of default.General accuracy rate,loan default accuracy rate and model misjudgment are used to verify the model,and AUC is used to test the model's accuracy.The dissertation has found that the random forest model could increase the accuracy rate in the credit assessment of borrowers,and the support vector machine model can better identify the risk of default and misjudgment,providing empirical evidence for credit assessment and management.