Using A Neural Network-Based Methodology for Credit–Risk Evaluation of A Tunisian Bank
Credit–risk evaluation is a very important and challenging problem for financial institutions. Many classification methods have been suggested in the literature to tackle this problem. Neural networks have especially received a lot of attention because of their universal approximation property. This study contributes to the credit risk evaluation literature in the MENA region. We use a multilayer neural network model to predict if a particular applicant can be classified as solvent or bankrupt. We use a database of 1100 files of loans granted to commercial and industrial Tunisian companies by a commercial bank in 2002 and 2003. Our main results are: a good capacity prediction of 97.1% in the training set and 71% in the validation set for the non cash-flow network. The introduction of cash-flow variables improves the prediction quality to 97.25% and 90% respectively both in the in-sample and out-of-sample sets. Introduction of collateral in the model substantially improves the prediction capacity to 99.5% in the training dataset and to 95.3% in the validation dataset.
|Date of creation:||Jun 2008|
|Date of revision:||Jun 2008|
|Publication status:||Published by The Economic Research Forum (ERF)|
|Contact details of provider:|| Postal: 21 Al-Sad Al Aaly St. Dokki, Giza|
Web page: http://www.erf.org.eg
More information through EDIRC
When requesting a correction, please mention this item's handle: RePEc:erg:wpaper:408. See general information about how to correct material in RePEc.
For technical questions regarding this item, or to correct its authors, title, abstract, bibliographic or download information, contact: (Namees Nabeel)
If references are entirely missing, you can add them using this form.