The Research On Commercial Bank Credit Risk Identification Based On Neural Network

Agriculture is the basic industry of the national economy, which plays animportant role in China’s economic development process. With the acceleratedindustrialization and urbanization process in China, the share of agriculture inthe three industries is declining, but the development of agriculture and otherindustries is more closely。As a vulnerable industry, agricultural development throughindustrialization to support the development of countries is actively taking measuresto focus on resolving the "Three Rural Issues". Agricultural economic developmentneeds of countries and financial institutions sector to increase capital investment, tomake agricultural credit projects effective credit support. The credit business is atraditional business of commercial banks, which is one of the present and future for along time commercial bank’s main source of profit. However, in recent years, China’sagriculture industry, the credit defaults which has been in the industry, as commercialbanks focus on supporting agricultural development, agricultural credit risk earlywarning, assessment and prevention is their main problem. How to build a credit riskassessment model suited to China’s actual situation of agricultural enterprises,commercial bank agricultural credit risk management, strengthening the commercialbank credit decision-making and better prepared for and respond to credit risk is themain problem to be addressed in this article.With the development of Computer and network technology, we are entering theera of information and also facing a series of questions. The increasing complexityof the relationship between the growing amount of data and data required the useof new information technology to conduct a comprehensive analysis of the data toexplore the valuable information inherent in these data, data mining techniques haveemerged. With ongoing research and application of data mining technology in variousfields, there have been many excellent and practical data mining techniques, neuralnetwork is one of them. Early in the credit assessment methods are vulnerableto subjective factors, the neural network model for credit risk assessment process,the need to create a model, the qualitative and quantitative factors into account, therelevant data input neural network can be data the relationship between the summary,and neural network processing of data has a good adaptability and a strong learning,imitation, anti-jamming capability, the use of neural network model with theflexibility to deal with the complex environment of multi-variable, effectively expressed the nonlinear relationship between credit indicators and credit rating.Weuse Agricultural listed companies to construct the specific indicators of credit riskassessment, combined with data mining techniques and neural network proposedagricultural enterprise credit risk assessment model based on BP neural network, andmodify the model so as to commercial banks providing the basis to reach tocircumvent the credit risk of the agricultural enterprises, reducing bank operatingcosts of payment of agricultural loans.