Mohamad Javad Mohaghegh Nia; Ali Akbari Bavafa Gelyan
Abstract
Microfinance programs emerged in mid 1980s. So, microfinance can be labeled as a new phenomenon. The reason for emergence and rapid development of microfinance programs can be weakness of former development strategies, especially those of financial development. In this research by using a three-dimension ...
Read More
Microfinance programs emerged in mid 1980s. So, microfinance can be labeled as a new phenomenon. The reason for emergence and rapid development of microfinance programs can be weakness of former development strategies, especially those of financial development. In this research by using a three-dimension Delphi query, the important factors affecting microfinance at Iran have been explored. In the resources side, deposits are composed of Qard al-Hasan, donation, subrogation and saving. In the uses side, the loans are divided into consumption (general consumptions and emergencies) and investing purposes. The institutions active in microfinance are divided into two groups of banks (commercial banks and financial holdings) and financial institutions (for profit and non-profit). The group lending and solidarity responsibility were underlined at legal-jurisprudence. At the application aspects, emphasis on usage of electronic systems and separate report of loans by their types and contracts is noticeable.
Nahid Baharlou; Ali Akbar Aminbeydokhti; Mohammad Javad Mohagheghnia
Abstract
One of the main tasks of the financial institutions is to give loan to the customers. Prediction and evaluation of the credit risks due to loan and consequently managing this risk is one of the greatest ongoing challenges for the financial institutions. The main aim of this work is to provide an optimized ...
Read More
One of the main tasks of the financial institutions is to give loan to the customers. Prediction and evaluation of the credit risks due to loan and consequently managing this risk is one of the greatest ongoing challenges for the financial institutions. The main aim of this work is to provide an optimized logistic regression model for credit scoring of real customers. Here the effects of increasing the customer’s credit classification from two (binary) to four (multinomial) distinct groups on the results of the logistic regression has been investigated. Identification of the most important parameters in prediction of the real customers’ credit scoring is the other important outcome of this work. The results of both binary and multinomial logistic regression show the relative importance of the education level and the age of the customer compared with other independent variables. The results of this work show that either increasing the number of classification types of the dependent variable, real customer’s credit, to four distinct groups has no sharp effect on the results of the optimized models or this conclusion can be due to improper distribution of the number of customers in different groups.
