Credit Scoring and Default Risk Prediction: A Comparative Study between Discriminant Analysis & Logistic Regression

  •  Zaghdoudi Khemais    
  •  Djebali Nesrine    
  •  Mezni Mohamed    


This paper aims to develop models for foreseeing default risk of small and medium enterprises (SMEs) for one Tunisian commercial bank using two different methodologies (logistic regression and discriminant analysis). We used a database that consists of 195 credit files granted to Tunisian SMEs which are divided into five sectors “industry, agriculture, tourism, trade and services” for a period from 2012 to 2014. The empirical results that we found support the idea that these two scoring techniques have a statistically significant power in predicting default risk of enterprises. Logistic discrimination classifies enterprises correctly in their original groups with a rate of 76.7% against 76.4% in case of linear discrimination giving so a slight superiority to the first method.

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