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: |
Web page: http://www.erf.org.egEmail:
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 you have authored this item and are not yet registered with RePEc, we encourage you to do it here. This allows to link your profile to this item. It also allows you to accept potential citations to this item that we are uncertain about.
If references are entirely missing, you can add them using this form.
If the full references list an item that is present in RePEc, but the system did not link to it, you can help with this form.
If you know of missing items citing this one, you can help us creating those links by adding the relevant references in the same way as above, for each refering item. If you are a registered author of this item, you may also want to check the "citations" tab in your profile, as there may be some citations waiting for confirmation.
Please note that corrections may take a couple of weeks to filter through the various RePEc services.