Design And Implement Of Credit Risk Early Warning System Based On Logistic Regression Model

Credit risk has been the main object and core content of being prevented andcontrolled by financial institutions and supervision departments for a long time.Particularly, after the reform and opening-up policy, global finance integrates witheach other step by step. Therefore, this brings a huge fluctuation to domestic financialmarket. These financial institutions are gotten an unprecedented challenge inunsecured loan, no matter in banks or investment organizations. By researching crisisof global financial institutions, results show that the principal reason that results inbank bankrupt attributes to credit risk. It is an urgent affair to intensifycomprehensive credit risk prevention, if it wants to win victory in increasingly drasticmarket competition. As a result, strategy research on credit risk prevention of eachbank not only has theoretical discussion value, but also has practical significance.By using regression analysis method to apply to credit risk prevention, recordingand conducting statistics on clients’ credit history, the paper predicts the future breachof contract for clients. By using regression analysis method, the paper conductspreliminary treatment on required data mining, including data cleaning, data selection,data integration and data change, etc. four parts. By combining mining data with riskearly warning system, the paper quantizes credit risk and passes crisis value at risk toupper layer for qualitative analysis and research, after quantification, as well asreports decision-making back to credit risk early warning system.The paper tries to adopt simple model and Logistic regression method to satisfyupdate requirements of client dynamics, deal with continuous variables and solveproblems of huge data volume and higher speed requirements in credit riskforewarning model. By taking full advantage of data mining, etc. advanced technology,especially for regression analysis method and empirical analysis, which are applied tocredit risk pre-warning, the paper predicts the future breach of contract for clients, soas to realize global, securing and high-efficient loan management for banks.