dc.contributor.author |
SirElkhatim , Mohammed A. |
|
dc.contributor.author |
Salim , Naomie |
|
dc.date.accessioned |
2017-04-25T07:24:33Z |
|
dc.date.available |
2017-04-25T07:24:33Z |
|
dc.date.issued |
2015 |
|
dc.identifier.citation |
SirElkhatim , Mohammed A. . Prediction of Banks Financial Distress Naomie Salim A. , Mohammed A. SirElkhatim .- Journal of Engineering and Computer Sciences (ECS) .- vol 16 , no1.- 2015.- article |
en_US |
dc.identifier.issn |
ISSN 1605-427X |
|
dc.identifier.uri |
http://repository.sustech.edu/handle/123456789/16609 |
|
dc.description |
article |
en_US |
dc.description.abstract |
In this research we are conducting a comprehensive review on the existing literature of prediction techniques that have been used to assist on prediction of the bank distress. We categorized the review results on the groups depending on the prediction techniques method, our categorization started by firstly using time factors of the founded literature, so we mark the literature founded in the period (1990-2010) as history of prediction techniques, and after this period until 2013 as recent prediction techniques and then present the strengths and weaknesses of both. We come out by the fact that there is no specific type fit with all bank distress issue although we found that intelligent hybrid techniques consider the most candidates methods in term of accuracy and reputation. |
en_US |
dc.description.sponsorship |
Sudan University of Science and Technology |
en_US |
dc.language.iso |
en_US |
en_US |
dc.publisher |
Sudan University of Science and Technology |
en_US |
dc.subject |
Bank Distress , Banks Factors , Prediction techniques ,Text Mining, Data Mining. |
en_US |
dc.title |
Prediction of Banks Financial Distress |
en_US |
dc.type |
Article |
en_US |