Credit Risk Prediction: A Comparative Study between Discriminant Analysis and the Neural Network Approach

Sihem Khemakhem () and Younes Boujelbene
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Younes Boujelbene: University of Sfax, Tunisia

Journal of Accounting and Management Information Systems, 2015, vol. 14, issue 1, 60-78

Abstract: Banks are concerned with the assessment of the risk of financial distress before giving out a loan. Many researchers proposed the use of models based on the Neural Networks in order to help the banker better make a decision. The objective of this paper is to explore a new practical way based on the Neural Networks that would help the banker to predict the non payment risk the companies asking for a loan. This work is motivated by the insufficiency of traditional prevision models. The sample consists of 86 Tunisian companies and 15 financial ratios were calculated, over the period from 2005 to 2007. The results were compared with those of discriminant analysis. They show that the neural networks technique is more accurate in term of predictability.

Keywords: credit risk; prediction; discriminant analysis; artificial neural networks (search for similar items in EconPapers)
JEL-codes: B41 C14 C45 C53 (search for similar items in EconPapers)
Date: 2015
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Citations: View citations in EconPapers (10)

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