Designing of a early warning system for credit risk in the banking system

Alimohammadi, Meysam; Fallah Shams, Mirfeiz; Ebadi, Khadijeh

doi:10.22034/jifb.2022.327339.1329

Designing of a early warning system for credit risk in the banking system

Document Type : Original Article

Authors

meysam alimohammadi ¹

Mirfeiz Fallah Shams ²

khadijeh ebadi ³

¹ Financial expert of the General Department of Research and Planning of Bank Saderat Iran

² Department of Finance, Central Tehran Branch, Islamic Azad University, Tehran, Iran

³ Department of Financial Management, Al-Zahra University, Tehran

https://doi.org/10.22034/jifb.2022.327339.1329

Abstract

Banks play an important role in the country's economy, which includes equipping resources, intermediation and facilitating the flow of payments and allocating credit to the recipient of facilities. In this way, the bank faces many risks that can be classified into four general categories: credit, liquidity, operations and investment.
The most important risk that banks face is credit risk. Credit risk arises due to the possibility of non-timely repayment of facilities and interest accrued to it.
Banks have always faced the challenge of how and based on what criteria and methods to evaluate credit applicants (facilities and liabilities)? This can be done through a comprehensive, structured system and selection of techniques. This research also seeks an expert proposal and solution to solve this problem in order to be able to categorize the applicants of financial credits well and reduce the possibility of non-repayment of the granted facilities. Obviously, the implementation of these methods and techniques can lead to the design of rapid alert systems for banks to be aware of the status of their credit portfolio and facilities and have knowledge of their customers.
In this article, using logit and probit models (regression models), the factors affecting the risk of customer default are investigated. Research findings indicate that the Probit model is more accurate in predicting customer default risk than the Logit mode.

Keywords

credit risk

scoring

early warning system. non performing loan