Research On The Models For The Credit Risk Of Small Loan Company

Recently,the internet finance industry has developed rapidly.However,the small-loan company must concern the credit risk of the lenders.We do the research both on the theory and practice for the credit risk models of the small-loan company.Firstly,we analyze the risk composition of the small-loan company,introduce some evaluation methodologies of the credit risk.At last,they all get the conclusion that personal credit risk is the most important risk of the small-loan company.We introduce the variable stability index,prediction ability index,fitting effect index.Secondly,we develop the credit risk models by using the data of the Bai Qian Finance and Unionpay,which is the empirical research.We use the logistic model and the GBDT model to construct the credit risk models.The development of models contains many complicate steps,such as the question preparation,data acquisition & variable construction,exploratory data analysis,variable selection,model development,model evaluation.We gradually construct object and scientific models.At last,we contrast the logistic risk model and GBDT risk model and formulate debit and credit strategy.There are some similarities in the distribution of default rates resulting from these two models.The default rates of both models are concentrated in 0.3%-8.9% and the customers whose default rates are higher than 8.9% account for 16% of the total population.We can select these customers to control risk.The two credit risk models both have advantages and disadvantages.We can find that the distinction ability about good and bad customers of the logistic model is better than that of the GBDT model from the KS pictures.The GBDT model is easier to cause overfitting than the logistic model.Moreover,the iterative process of the GBDT model is in the black box,which we can't understand the process clearly.Of course,the GBDT model has some advantages,the fitting effect of the logistic model is worse than that of the GBDT.The logistic model needs to do a lot of human interventions in the step of variable selection.As the popularization of the machine learning,the modern enterprises have the trend to utilize the machine learning technique on the development of credit risk models.It can make up the shortage of the traditional risk models.