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

Advances in Mathematical Finance and Applications

مقاله 17، دوره 8، شماره 4، آذر 2023، صفحه 1409-1428   

نوع مقاله: َApplied-Research Paper

شناسه دیجیتال (DOI): 10.22034/amfa.2022.1950520.1689

نویسندگان

Hojjat Tajik¹؛ Ghodratollah Talebnia^{* 2}؛ Hamid Reza Vakili Fard²؛ Faegh Ahmadi²

¹Department of Accounting. Qeshm branch, Islamic Azad University. Qeshm, Iran.

²Department of Accounting. Science and research branch, Islamic Azad University. Tehran, Iran.

چکیده

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.

کلیدواژه‌ها

Smart pattern؛ bank customers’ risk؛ credit risk؛ machine learning؛ random forest algorithm

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