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Mehrnaz Bagheri, Dr. Mohammad Taghipour
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- Student, Assistant Professor ABA Institute of Higher Education, North Tehran Branch, Islamic Azad University Qazvin, Tehran Iran, Iran
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Abstract
nThe most significant risk to financial and monetary institutions is credit risk. The banks give their loans to the customers with high return and low risk. This is fulfilled if the banks can identify their credit customers including real and legal entities and classify them based on tendency to full pay out of obligations using appropriate financial and non-financial criteria. The main purpose of this study is the evaluation of effective factors on evaluation of credit of customers of bank with hybrid approach of data mining to improve decision making. To do this, the financial and qualitative data of customers as 1000 samples are taken from UCI University site and 24 explanatory variables are applied. To achieve this purpose, decision tree techniques, naïve Bayesian algorithm, support vector machine (SVM), neural networks and its combination are used to predict non-payout risk of loans by the bank. In addition, the effective factors on validation of customers were ranked from this aspect. The results showed that naïve Bayesian had better performance compared to decision tree in prediction of giving loans to customers. Additionally, the important variables influencing consumer validation were ranked using these techniques. The results demonstrated the higher efficacy of the naïve Bayesian algorithm in assisting banks choices about credit issuance, as it outperformed the decision tree method in properly predicting loan payback risks.
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Keywords: Credit risk, Data mining, Decision tree, Naïve Bayesian Algorithm, Support Vector Machine (SVM), Neural networks
n[if 424 equals=”Regular Issue”][This article belongs to Recent Trends in Social Studies(rtss)]
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| Volume | ||
| [if 424 equals=”Regular Issue”]Issue[/if 424][if 424 equals=”Special Issue”]Special Issue[/if 424] [if 424 equals=”Conference”][/if 424] | ||
| Received | June 4, 2024 | |
| Accepted | June 18, 2024 | |
| Published | July 17, 2024 |
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