Please use this identifier to cite or link to this item: https://hdl.handle.net/10419/310508 
Year of Publication: 
2012
Citation: 
[Journal:] Journal of Accounting and Management Information Systems (JAMIS) [ISSN:] 2559-6004 [Volume:] 11 [Issue:] 4 [Year:] 2012 [Pages:] 587-620
Publisher: 
Bucharest University of Economic Studies, Bucharest
Abstract: 
Credit risk evaluation or loan default risk evaluation is important to financial institutions which provide loans to businesses and individuals. Credit and loans have risk of being defaulted. To understand risk levels of credit users (corporations and individuals), credit providers (bankers) normally collect vast amount of information on borrowers. Statistical predictive analytic techniques can be used to analyze or to determine risk levels involved in loans. This study contributes to the credit risk evaluation literature in the Middle East and North Africa (MENA) region. We make a comparative analysis of two different statistical methods of classification (artificial neural network and Support Vector Machine). We use a multilayer neural network model and SVM methodology to predict if a particular applicant can be classified as solvent or bankrupt. We use a database of 1435 files of credits granted to industrial Tunisian companies by a Tunisian commercial bank in 2002, 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. The results show that Multilayers Neural Network models outperform the SVM models in terms of global good classification rates and of reduction of Error type I. In fact, the good classification rates are respectively 90.2% (NNM) and 70.13% (SVM) for the in-sample set and the error type I is of the order of 18.55% (NNM) and 29.91% (SVM).
Subjects: 
Banking sector
Accounting data
Credit risk assessment
Default risk Prediction
Neural network
SVM
classification
training
JEL: 
C45
Creative Commons License: 
cc-by Logo
Document Type: 
Article

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