Investigating the Risk of Paying Loans to Public and Private Companies Using the Logit Model and Comparing it with Altman Z (Case Study: A Private Bank in Iran)

Document Type : Original Article


1 PhD Candidate in Finance, Kish International Branch, Islamic Azad University, Kish Island, Iran.

2 Associate Professor, Member of the Accounting Department and member of the Young Researchers Club, Islamic Azad University of Islamshahr Branch Tehran, Iran (Corresponding Author)


The design of a credit risk measurement model in the monetary and banking system will play an important role in increasing the profitability of banking resources. This article attempts to use two models of Logit and Z Altman to determine and predict the credit risk of facilities provided to legal entities at a private bank in Iran. The variables studied in this research include qualitative variables (company life, financial credit document, experience of managers, type of company) and financial variables (working capital in total assets, book value of equity to book value of debt, total sales to total assets, accumulated profits to total assets, profit before interest and taxes on total assets). The results of this research show that the use of validation models, despite all the technical and statistical considerations, can accurately determine the credit status and credit risk of customers. Both models used more than 80% of the correct predictions, which are a significant figure in the real business environment. But in the Logit model with a slightly better difference than the Z-Altman model, about 83% of its predictions were correct.


1)     Amini. A., Haghighat A., Hemati F., (2010), Analyzing and Surveying NPL's in Qazvin's Network Banks,  Economic Journal, Vol. 9 & 10 , pp 71-86.
2)     Bank for international Settlements. (2011). Long-term Rating Scales Comparison. Retrieved 1 June 2011. Available at
3)     Bellemare. Charles , Kröger. Sabine, Sossou. Kouamé Marius, (October 2018), Reporting probabilistic expectations with dynamic uncertainty about possible distributions, Journal of Risk and Uncertainty, Volume 57, Issue 2, pp 153–176.
4)     Keshavarz Haddad G R, Ayati Gazar H., (2008), A Comparison between Logit Model and Classification Regression Trees (CART) in Customer Credit Scoring Systems. QJER. 7 (4) :71-97
5)     Kordloei H.R., Nori. P, Shamsiyan S., Credit & liquidity Risk in Banks, Termeh Publising,
6)     Schoen. Edward J., The 2007–2009 Financial Crisis: An Erosion of Ethics: A Case Study, Journal of Business Ethics, Volume 146, Issue 4, pp 805–830.  
7)     Singh .A., (2015), Performance of Credit Risk Management in Indian Commercial Banks, IJMBR, Volume 5, Issue 3, , Page 169-188.
8)     Springer. Christine G. , (2009), Strategic management of three critical levels of risk, The American Society for Public Administration,
9)      Weissova. Ivana, Kollar. Boris, Siekelova. Anna, (2015), Rating as a Useful Tool for Credit Risk Measurement, Procedia Economics and Finance, Volume 26, pp 278-285.