Personal Credit Evaluation Model Is A Comparative Study

With the development of consumer credit in domestic commercial banks, personal credit is attached more and more value in our country. Under the situation that there is no personal credit system, how to develop reasonable and effective methods to evaluate customers' credit in the internal of commercial banks is critical to realize risk control before granting and to further consumer credit.Based on former studies and conclusions, this thesis makes a qualitative analysis and quantitative analysis. Combining the special conditions in our country and using analytical comparison, the dissertation establishes the models, and presents the new conclusion that should be considered in domestic banks' credit rating.From the point of a qualitative analysis view, first it uses three hypotheses in economics theory to analyze personal credit action and analyze the effect factor of personal credit. The hypotheses is most profit, finite senses and opportunism;secondly, it goes deep into analysis with "5C ";Finally, it compares foreign credit model to our country's, and discusses these similarities and differences.From the point of a quantitative analysis view, according to the point of a qualitative analysis view, the thesis establishes a set of index system of personal credit evaluation model, including: sex, age, income, education and so on. Based on a set of car loan sample of Chinese commercial bank, a systemically comparative study of various statistical credit rating models was made in China. The comparative study indicated that every model has its own strength and weakness.The strengths of linear discriminant analysis, and logistic regression are that these models are explainable and their outputs can be a linear score card (so can be easily implemented). But these models have higher misclassification rate comparing with others. Neural network and classification tree models have a higher predict accuracy, but may be over fitted, and their outputs are hard to be explained. As for the data set of this dissertation, we finally come to the conclusion that Neural network model is relatively the best of the four models, classification tree model ranks the second, and Logistic regression method is not fit for the data we have used.