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Credit Risk Management To Small And Micro Customers On The Basis Of Logistic Model And FAHP Method

Posted on:2015-04-26Degree:MasterType:Thesis
Country:ChinaCandidate:H J ZhuFull Text:PDF
GTID:2309330461957945Subject:Management Science and Engineering
Abstract/Summary:PDF Full Text Request
With the continuous development of China’s banking industry, the number of commercial bank is increasing. The competitive environment of domestic banking industry is increasingly fierce as well.So far, the main profits of domestic commercial bank is traditional credit business. Credit customers are not able to bring a steady income for commercial banks because of credit risk during the execution of the contract. Commercial banks have to take risks that credit customers can not repay the principal and interest,due to the dynamic changes in customer business risks and asymmetric information. Therefore, commercial banks must establish a set of accurate and reliable credit risk management system.According to the relevant theories of credit management, the article combines realistic conditions of domestic commercial banks establishes a credit indicators library for small and micro customers,applies Logistic return model thoroughly discussed the relation of our commercial banks credit risk with relevant elements.We try to offer some useful references for our commercial banks’credit risk management.The paper selected two groups of sample data of small and micro customers. It finds that many financial indicators of small and micro customers have significant effect on their credit risk. But it’s hard to judge the credit risk of those customers who are holding bank loans by using their financial statements. Because operation of the emergency is the reason of credit risk for smaller companies.Therefore, we process their behavior event, information of credit card and court proceedings into the risk assessment system by using FAHP. The results show that we can enhance the accuracy of the model to determine the credit risk of small and micro customers by analyzing their behavior event.
Keywords/Search Tags:Logistic model, FAHP, Credit risk, Probability of default
PDF Full Text Request
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