Evaluation of Credit Risk of Bank Customers with a Hybrid Approach of Data Mining Techniques

Year : 2024 | Volume : | : | Page : –
By

Mehrnaz Bagheri,

Dr. Mohammad Taghipour,

  1. Student ABA Institute of Higher Education Qazvin Iran
  2. Assistant Professor North Tehran Branch, Islamic Azad University Tehran Iran

Abstract

The 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.

Keywords: Credit risk, Data mining, Decision tree, Naïve Bayesian Algorithm, Support Vector Machine (SVM), Neural networks

How to cite this article: Mehrnaz Bagheri, Dr. Mohammad Taghipour. Evaluation of Credit Risk of Bank Customers with a Hybrid Approach of Data Mining Techniques. Recent Trends in Social Studies. 2024; ():-.
How to cite this URL: Mehrnaz Bagheri, Dr. Mohammad Taghipour. Evaluation of Credit Risk of Bank Customers with a Hybrid Approach of Data Mining Techniques. Recent Trends in Social Studies. 2024; ():-. Available from: https://journals.stmjournals.com/rtss/article=2024/view=156677

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Ahead of Print Subscription Original Research
Volume
Received June 4, 2024
Accepted June 18, 2024
Published July 17, 2024
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