Credit–risk evaluation of a Tunisian commercial bank: logistic regression vs neural network modelling
Vol. 9, Nr. 1/2010 , p92..119
Author(s):
Hamadi MATOUSSI
Aida KRICHENE ABDELMOULA
Keywords:
Banking sector, Accounting data, Credit risk evaluation, Default risk Prediction, Neural network Models
Abstract:
This paper addresses the question of default prediction of short term loans for a Tunisian commercial bank. We make a comparative analysis of two different statistical method of classification (artificial neural network and linear logistic regression with panel data). We use a database of 1434 files of credits granted to industrial Tunisian companies by a commercial bank in 2003, 2004, 2005 and 2006. The results show that the best prediction model is the multilayer neural network model and the best information set is the one combining accrual, cash-flow and collateral variables. We got a good classification rate of 97% in the training data set and 89.8% in the validation data set. Moreover, our investigation shows that the bank could have reduced the percentage of loss (failure on short term credits) from 18.7% to 12% during 2007.
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