Study On Bank Green Credit Risk Assessment Based On Multi-angle

With the development of credit risk research in China,the problem of green credit risk is becoming more and more important as well as enjoying the results.Based on the data analysis to the hot issue of green credit,combined with the knowledge,using hierarchical clustering analysis and principal component analysis of characteristics of engineering methods,while using supervised learning BP neural network model based on machine learning,and then get more scientific results.Credit risk is one of the most important risks of bank institutions in enterprise loans,and risk prevention and control plays an important role in the long-term development of institutions.The green credit mentioned in this paper refers to the re evaluation of the credit capacity of banks and other financial institutions in the aspects of ecological protection,construction and development in the aspect of ecological credit protection,construction and development.Throughout the literature,it can be found that many scholars at home and abroad have done a lot of research on credit risk,but for the new hot issue of green credit,it is still in the stage of exploration,and its development is not yet mature.This paper explores two data mining models,which are AHP based green environment rating model and PCA based BP neural network green credit evaluation risk model.First of all,according to the green credit in China is still at the initial stage,the lack of relevant data,this paper consults the related literature and research,combined with the existing bank credit risk system,constructing the evaluation index system of green credit risk level by AHP.Then,the extraction of basic data of more than 2000 listed companies through the Northeast Securities flush,select type of enterprise integrated enterprise as the sample data,using cluster analysis of 10 indicators for risk evaluation of the original analysis,excluding high-risk enterprises.For enterprises to increase after screening 4 green risk index,economic and green risk index as an index,under the 14 level two indexes,10 of which is based on financial indicators,4 level index analysis method to construct the criterion layer,model and data processing by using principal component analysis method by PCA to 14 an index for processing,obtained 6 common factors.Finally,BP neural network is used for simulation analysis(matlab2012a)to verify its feasibility.The method of quantitative and qualitative analysis can provide reference for the selection and application of green credit risk assessment.