Research On Bank Green Credit Risk Evaluation Based On BP Neural Network

Today,the decline in air quality has made people pay more and more attention to environmental issues.In order to solve the current pollution and prevent further deterioration of the environment,people in different fields are actively exploring solutions.At the macro level,relevant government departments have issued a number of corporate regulations and public opinions on green travel and consumption.However,the fastest and most effective solution to environmental problems still comes from the internal drive of the market economy.As the "blood" of enterprise operation,capital is the key to the survival of enterprises.The development of financial markets provides a flow of funds for the development of the real economy and also plays a role in redistributing market resources.Therefore,in order to solve environmental problems,re-adjust the industrial structure and carry out industrial upgrading,green finance has played a huge role as a brand-new financial instrument.Green finance is a broad concept that covers all aspects of financial markets.At the earliest,the most mature green financial products should be green credit.Banks relying on national credibility as the issuer of green credit have made green credit a fierce financing channel for enterprises.However,the uncertainty of the policy and the information asymmetry between banks and banks do pose risks to the bank's green credit.Effective prevention and reduction of information asymmetry risks become the key to reducing green credit losses.Information asymmetry risk as a non-systematic risk can be eliminated through effective technical means and supervision and inspection mechanisms.Therefore,this requires continuous improvement of the green credit evaluation system and strengthening of green credit risk monitoring and management.From the perspective of preventing the risk of asymmetry of green credit information,based on the research and analysis of the theory of green credit and the status of green credit development in China,this paper establishes a BP neural network model for deep learning and credit for two types of enterprises.The risk assessment and prediction provide a theoretical basis and technical support for effectively reducing the risk of green credit.(1)From the theory of social sustainable development and the theory of financial risk management,the background of green credit development is theoretically elaborated,which lays a theoretical foundation for the research of green credit risk evaluation.(2)Based on the introduction of the origin,characteristics and development status of green credit,the types and communication mechanisms of green credit risks are described in detail,and the relevant knowledge of green credit risk assessment and risk rating is explained.(3)Considering the availability of environmental risk indicators based on previous research,19 enterprise indicator were selected using factor analysis method and standard deviation rule to construct risk level indicators.(4)Constructing and predicting the deep learning BP neural network model,the results show that the method has higher accuracy for the evaluation of green credit risk level and the prediction result is better than the traditional BP neural network model of one layer of hidden layer.And the prediction results of a simple linear regression model.At the same time,it puts forward countermeasures and suggestions for the risk management of green credit.(5)The countermeasures and suggestions for the risk prevention and management of green credit in China are put forward,and the further development and improvement of the green credit risk evaluation system is prospected.