Default Predicting of Facilities given to Enterprises: clients of non-bank depositary institutions

Subject Areas : Financial Knowledge of Securities Analysis

Shadanloo Ameri Siahoee ¹ , HAMIDREZA KORDLOUIE ² , Seyed Mohammad Abdollahi Keyvani ³

1 - PhD student of Financial Management, Islamic Azad university of Kish branch, Kish, Iran
2 - Department of Finance, Eslamshahr branch, Islamic Azad University, Eslamshahr, Tehran, IRAN
3 - Department of Financial Management, Islamic Azad University, North Tehran, Iran

Received: 2021-06-12 Accepted : 2021-06-12 Published : 2021-05-22

Keywords: credit risk, default probability, Linear Regression Model, logistic regression, Probit, Z Altman,

Abstract :

One of the most important risks of the banking system is the credit risk. Considering the portfolios of institutes and the mutual relationship of their items, any tension in refunding the overdone facilities can lead to essential problems such as liquidity risk, interest rate, and even bankruptcy. In this way, finance and credit institutes look for models, achieving experience, and improving credit evaluation models they use to validate the credit of their credit customers. However, the list of the important variables for credit validation and selection of the more appropriate and effective model has been a crucial question for many of these institutes. The present study investigates the LOGIT, PROBIT, and Z Altman models, using a set of qualitative and quantitative variables of the legal customers of depository institutions. The findings prove that qualitative and financial variables of the legal customers for this institute proved to be explanatory for the credit risk probability at a high degree of confidence. These models successfully predicted credit risk for 80% of the facilities and LOGIT was more successful than other models.

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