Volume 6, Issue 24 (Fall 2018)                   IUESA 2018, 6(24): 17-33 | Back to browse issues page

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Bagheri N, Haghshenas Kashani F. Credit Risk Assessment of Urban Cooperatives Using Neural Network Method. IUESA. 2018; 6 (24) :17-33
URL: http://iueam.ir/article-1-967-en.html
1- Faculty of Management, Central Tehran Branch , Islamic Azad University, Tehran, Iran.
2- Faculty of Management, Central Tehran Branch, Islamic Azad University, Tehran, Iran. , fahaghshenaskashani_93@yahoo.com
Abstract:   (1275 Views)
One of the most important issues for institutions that provide financial facilities to others is the issue of credit risk. One of the ways to quantify and measure credit risk and, consequently, its proper management, is to use a credit rating. Credit rating is a model for measuring the performance of facility recipients, which is based on quantitative criteria such as corporate financial information, in order to allow prospective clients to obtain a similar profile with facilities and customers with a proper and inappropriate credit position. To be identified.
According to the mission of the organization, the Central :::::union::::: of Cooperative of Workers of Iran has numerous member cooperatives from across the country, which provides their products there. In this research, the effect of each of the factors involved in determining the risk of credit in this cooperative has been tested first. Then, using the self-organizing mapping algorithm, we will cluster the data to exclude clusters that are very remote and far-reaching. The credit risk of each of the cooperatives has been calculated through the algorithm of the multi-layer perceptron neural networks in MATLAB software and a model for predicting credit risk in the future. The main purpose of this research was to use this algorithm to classify cooperatives by calculating credit risk numbers and use it to predict the future credit risk of the future cooperatives.

Full-Text [PDF 851 kb]   (163 Downloads)    
Type of Study: Research | Subject: Special
Received: 2017/05/10 | Accepted: 2018/08/7 | Published: 2018/08/7

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