Presenting the smart pattern of credit risk of the real banks’ customers using machine learning algorithm.

Subject Areas : Financial Accounting

Hojjat Tajik ¹ , Ghodratollah Talebnia ² , Hamid Reza Vakili Fard ³ , Faegh Ahmadi ⁴

1 - Department of Accounting. Qeshm branch, Islamic Azad University. Qeshm, Iran.
2 - Department of Accounting. Science and research branch, Islamic Azad University. Tehran, Iran.
3 - Department of Accounting. Science and research branch, Islamic Azad University. Tehran, Iran.
4 - Department of Accounting. Science and research branch, Islamic Azad University. Tehran, Iran.

Received: 2022-01-27 Accepted : 2022-04-10 Published : 2023-09-01

Keywords: bank customers’ risk, Smart pattern, Credit Risk, Random forest algorithm, Machine Learning,

Abstract :

In the past, deciding over granting loans to bank customers in Iran would be made traditionally and based on personal judgments over the risk of repayment. However, increase in demands on banking facilities by economic enterprises and families on the one side, and increased as well as extended commercial competitions among banks and financial and credit institutions in the country for reduction of facility repayment risk on the other side, have caused application of novel methods such as some statistical ones in this context. Now to predict the risk of negligence in banking facility repayment and classification of the candidates, bankers use their customers’ credit ranking. Time efficiency, cost effectiveness, avoidance from personal judgments, and further accuracy in examining the candidates who apply for various funds are of its salient merits of this new combined method. Various statistical methods including biased analysis, logistic regression, non-parametric parallelism, and also some others such as neural networks have been employed for credit ranking. In this research, given the random forest metaheuristic algorithm-based smart pattern of real bank customers’ credit risk (case study: Bank Tejarat) was presented. According to the value of skewness, the data could be stated to have a normal distribution. Based on the observed results, the lowest mean was related to the variable of type of facility and its maximum value, to the amount of facility.

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