Research On Mixed Model For Personal Credit Scoring

With the rapid development of Chinese financial industry, the scale of various con-sumer credit expands quickly. But, because of the low risk management level over the retail trade from the interior commercial banks, relatively backward management means and methods, lack of an effective personal credit evaluation method, all severely hindered the development of credit business of personal consume.Therefore, it is very important for the development of social economy to develop an evaluation method of personal credit scoring, which is suitable for the Chinese character and can effectively lower the credit risk. This research on the mixed personal credit scoring model can reach the goal, that is to effectively lower the credit risk of commercial banks and realize maximize of the bank profits.In this paper, Chapter 1 gives a brief introduction of credit scoring and researches that have been done before. Chapter 2 concerns about three single methods used to build the personal credit scoring model. Chapter 3 analyze concepts and methodologies to evaluate the predict power of the credit scoring model. In chapter 4, the empirical analysis for each method in Chapter 2 is conducted using the real world credit data. For each method, the error ratio is calculated. After that, this paper consider a mixed model of Logistic model and decision tree in Chapter 5. We can use decision tree to detect the interaction for Logistic model. Empirical analysis is also done to prove that the interactions exist in the model. So the mixed model can reach the goal, that is to detect the interactions by decision tree.The major contribution of this article is introduce random forest method to build credit scoring model, and the empirical result is good. Meanwhile, a mixed model of Logistic and decision tree is built to manage the credit risk. Finally, we can get the conclusion the decision tree can detect the interaction for Logistic model.