Research On Pre-loan Credit Risk Assessment Of Green Credit In Commercial Banks

In recent years,China’s green credit has developed rapidly,accounting for an increasing proportion of China’s credit balance,and playing an important role in promoting the sustainable development of the financial system.Green credit business is both an opportunity and a challenge for commercial banks.How to manage credit risk and make green credit business improve quality and efficiency for their own operations is crucial.However,the development of green credit in China started late,and as an important part of risk management,pre-loan credit risk assessment still has many problems.Based on the existing literature,the main issues discussed in this article are as follows: First,the selection of environmental factors indicators in the green credit pre-loan credit risk assessment indicator system excessively relies on environmental protection department information,which is only the most basic threshold.Secondly,the subjectivity of the pre-loan credit risk assessment method for green credit is strong,which reduces the accuracy of the assessment results.Thirdly,the pre-loan credit risk assessment results of green credit have not been effectively applied to their pre-loan risk management decisions,and are still limited to making decisions on whether to lend.By improving the efficiency of pre-loan credit risk assessment of green credit and making risk management decisions based on the assessment results to maximize the profits of commercial banks,it can promote the long-term development of green credit business.Therefore,studying this has certain theoretical and practical significance.In view of this,and in response to existing problems,this article has designed optimization ideas for the establishment of evaluation index systems,selection of evaluation methods,and application of evaluation results.Based on this,a model has been constructed for empirical testing,with the aim of improving the efficiency and accuracy of pre-loan credit risk assessment.This article uses the 2021 sample data of647 listed companies in the polluting industry as an example to establish an evaluation index system based on the three-dimensional dimensions of financial,non-financial,and environmental factors.Factor analysis is performed on the selected evaluation indicators to remove the impact of correlation between indicators,and the seven representative common factors extracted are used as input variables for the model.The “3σ” principle is to empirically analyze the risk rating results of the proposed loan enterprise as model output variables.The empirical results show that the BP neural network based on genetic algorithm optimization(GA-BP neural network)has smaller prediction mean square error and faster convergence speed compared to the standard BP neural network.The prediction accuracy of the training set is 99.34%,the prediction accuracy of the test set is 95.90%,and the overall prediction accuracy is 98.30%.The model has good applicability in practical operations.The contribution of this article is to design a pre-loan credit risk assessment indicator system for green credit based on the perspective of commercial banks.The selection of environmental factors indicators is no longer limited to the pre-evaluation and post punishment results provided by the environmental protection department.Secondly,GA-BP neural network method is introduced to construct a pre-loan credit risk assessment model for green credit,which broadens the selection of quantitative assessment methods for green credit credit risk.Thirdly,based on the risk assessment results,pre-loan risk management decisions for enterprises with different risk levels are proposed to remedy the shortcomings of existing literature that only focus on whether to make lending decisions.