Presenting the smart pattern of credit risk of the real banks’ customers using machine learning algorithm.
Subject Areas : Financial Accounting
Hojjat Tajik
1
(
Department of Accounting. Qeshm branch, Islamic Azad University. Qeshm, Iran.
)
Ghodratollah Talebnia
2
(
Department of Accounting. Science and research branch, Islamic Azad University. Tehran, Iran.
)
Hamid Reza Vakili Fard
3
(
Department of Accounting. Science and research branch, Islamic Azad University. Tehran, Iran.
)
Faegh Ahmadi
4
(
Department of Accounting. Science and research branch, Islamic Azad University. Tehran, Iran.
)
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.
Doi: 10.1016/j.procs.2020.06.069.
Doi: 10.3390/ijfs9030039.
Doi: 10.1016/j.eswa.2012.01.134.
Doi: 10.1016/j.jjimei.2021.100037.
Doi: 10.1016/j.jneumeth.2013.08.024.
Doi: 10.4018/IJIIT.2016010103.
Doi: 10.1016/j.ins.2017.10.045.
of the bank using logistic regression and neural network (case study of Tourism Bank Branch),
The third World Conference on Management, Economics of Accounting and Humanities at the beginning of the third millennium, Shiraz, in cooperation with the Institute of Higher Education Institute of Allameh Khoyi University, Zarghan University of Research in the Conference, 2016.
Neural Network Model, Journal of Agricultural Economics, 2015, 9(2), P. 103-119.
the bank using fuzzy hierarchical analysis and high grade neural network, International Journal
of Industrial Engineering and Production Management, 2012, 23(1), P. 44-54.
Doi: org/10.1016/j.techfore.2018.03.007.
Doi:10.22034/AMFA.2018.540829.
[41] Izadikhah, M. Financial Assessment of Banks and Financial Institutes in Stock Exchange by Means of an Enhanced Two stage DEA Model. Advances in Mathematical Finance and Applications, 2021, 6(2), P. 207-232. Doi: 10.22034/amfa.2020.1910507.1491
[42] Kalantari, N., Rahmatolah Mohammadi Pour, R., Seidi, A., Shiri, A., Azizkhani M., Fuzzy Goal Programming Model to Rolling Performance Based Budgeting by Productivity Approach (Case Study: Gas Refiner-ies in Iran, Advances in Mathematical Finance and Applications, 2018, 3(3), P. 95-107.
Doi: 10.22034/AM FA.2018.544952.