Modeling SMEs Credit Default Risk: The Case of Saudi Arabia

Nermean  Senan; Tahar  Tayachi; Ahmed  BenSaïda

doi:10.6000/1929-7092.2022.11.04

Authors

Nermean Senan College of Business, Effat University, Jeddah, Saudi Arabia https://orcid.org/0000-0002-9199-9555
Tahar Tayachi College of Business Administration, American University in the Emirates, UAE and FSEG Mahdia, University of Monastir, Mahdia, Tunisia https://orcid.org/0000-0002-1391-7665
Ahmed BenSaïda College of Business, Effat University, Jeddah, Saudi Arabia https://orcid.org/0000-0001-5591-4393

DOI:

https://doi.org/10.6000/1929-7092.2022.11.04

Keywords:

Credit risk, SME, Probability of default, Risk assessment

Abstract

This study assesses the credit risk of small and medium-sized enterprises (SMEs) to minimize unexpected risk events. We construct a hybrid statistical model based on factor analysis and logistic regression to predict enterprise default on loans and determine the factors predicting SMEs default. We assess the credit risk of SMEs listed on the Saudi stock market. The results indicate that the SMEs acid-test ratios are the most influential factors in predicting SMEs credit risk. Therefore, the designed logistic model can be used by financial institutions during the decision-making process of granting loans to SMEs. This study sheds light on challenging access to bank credits due to the lack of financial transparency of most Saudi SMEs.

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