Amir Khorrami; Mohammad Taghi Taghavifard; Seyed Mohammad Ali Khatami Firouzabadi
Abstract
Credit risk assessment is one of the key issues for banks and financial institutions and various models have been developed for this. This study uses Case Based reasoning (CBR) Model and considers a database of bank credit customers to assess the credit risk of bank applicants. For this, 9 criteria were ...
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Credit risk assessment is one of the key issues for banks and financial institutions and various models have been developed for this. This study uses Case Based reasoning (CBR) Model and considers a database of bank credit customers to assess the credit risk of bank applicants. For this, 9 criteria were selected based on the experts' opinion and were weighted using the Fuzzy Analytical Hierarchy Process (FAHP). Return check, housing situation and income level are the most important criteria for credit risk assessment of the bank applicants. Then, using the TOPSIS Technique, we could evaluates the similarity of the new item with actual past cases or evaluate the new applicant with the ideal option, and uses a case-based reasoning model to predict the likelihood of default or non-default applicants. Survey research was applied for this study and the research community was the records of previous bank applicants between 1390-94 years. This research is an applied and descriptive and descriptive study. The results show that the accuracy of the CBR model is higher than other validation and ranking methods of bank customers. The use of the CBR model in order to authenticate customers has obtained results far better than the performance of the credit sector experts, which led to the judgment of default or non-default of customers, indicating the high performance of the model used in comparison to the model used by bank and validation experts. CBR leads to the design an expert, specialized and intelligent system which addition to storing data in a database, stores models and templates for use.
