Improvement payment loan concerning financial discipline and maximum gain in uncertainly
Roya Cheshmikhani
1
(
PhD student in Industrial Management, Islamic Azad University, Faculty of Science and Research, Tehran, Iran
)
Mohammadali Afsharkazemi
2
(
Associate Professor of Industrial Management, Islamic Azad University, Science and Research Unit, Tehran, Iran
)
Abbas Toloei Eshlaghy
3
(
Full Professor of Industrial Management, Islamic Azad University, Faculty of Science and Research, Tehran, Iran
)
Ezattollah Asgharizadeh
4
(
Associate Professor of Industrial Management, University of Tehran, Tehran, Iran
)
Keywords: Facilities, Data Mining, Clustering, Deep Learning, Convolutional Neural Network, CNN-LSTM. ,
Abstract :
Granting facilities is an important part of every bank's operations. This part of banking activities is economically important. With its operations, banks can provide the transfer of resources from those that have directly invested to those who need money, this repayment makes another people use these resources. Failure to repay facilities on time will cause the bank's resources to stagnate and in the long will cause the country's economic recession. It is important to monitor the correct allocation of resources because if bank resources are used and unfounded payments are made, the banks will not be able to pay the depositors and will become bankrupt. By examining the researches in the field of banking, it was found that most of the researches have focused on the optimal combination of the investment portfolio in the capital market, and less researchers have paid attention to the discussion of the optimal combination in the money market. Most of the researches that used operational and statistical research methods were related to industrial issues, and less in financial issues and operational research discussions were used. In the optimal portfolio of the combination of facilities or investments, mostly the genetic algorithm has been used and less than other fuzzy methods have been used in the conditions of uncertainty. Therefore, in this research, the improvement of facility payment modeling has been addressed by using convolutional neural networks and CNN-LSTM modeling.
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