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گواهی نمایه سازی مقاله The comparison of credit risk between Artificial Neural Network and Logistic Regression models in Tose-Taavon Bank of Guilan province

عنوان مقاله: The comparison of credit risk between Artificial Neural Network and Logistic Regression models in Tose-Taavon Bank of Guilan province
شناسه (COI) مقاله: ICOAC01_147
منتشر شده در ششمین کنفرانس بین المللی اقتصاد، مدیریت و علوم مهندسی در سال ۱۳۹۴
مشخصات نویسندگان مقاله:

Reza Aghajan Nashtaei - Department of Business management, Rasht Branch, Islamic Azad University, Rasht, Iran
Seyedeh Maryam Taghavi Takyar - Department of Business management, Rasht Branch, Islamic Azad University, Rasht, Iran

خلاصه مقاله:
Over the last few years organizations and especially financial institutions in our country are concerned about the issue of risk and damages caused by it but despite its importance, a coordinated framework for implementing risk management and also accurate indicators for determining credit risk are not available. In addition, rating industry has not found its own good place in our country where the main reasons for it include cultural, economic and educational issues, the lack of a centralized databank, the lack of a strong and effective information exchange network, the lack of adequate laws and regulations, and political issues. Therefore, a strategy shall be advised in order to provide the financial resources required for applicants and banks perform their main duty which is lending with the least possible risk; because in current changing conditions, the success of any firm depends on the risks and their risk management methods. (Hesabi ,2014) The most important risk facing banks is credit risk which includes loans that have been paid in the past. Overall, credit risk for a bank is the possibility of losing time or generally obligations being neglected by debtors because of their inability to fulfill their obligations to the bank. These obligations usually involve repayment of the debts and their interest to the bank on the specified date. (Yurdakul , 2014)Credit loans are the basis for banking industry. The performance of credit section in a good situation guarantees the profitability and stability of a bank. Therefore, securing the financial background history of customers is a very important factor before making any decisions regarding credits and also a key determinant in reducing credit risk. Credit risk is one of the most critical and biggest challenges facing banks. In fact, the estimation of a risk is an important factor for any decisions regarding credits and inability to determine the accurate risk has a reversed effect on credit management. In addition, risks can affect approved and non-approved investment decisions. When the credit manager approves a loan, he runs the potential risk of client being unable to repay it. On the contrary, when a loan is turned down, the potential risk of losing customers to competitors arises. Hence, assessing credit risk before making a decision to lend is important. (Bekhet and Eletter , 2014)

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