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The banking Industry play an appreciated role in promoting the economic development of any nation in the world. Primarily, banks focus on credit lending to borrowers in other to generate income, which are later invested into local, national, or international community. For some years now, banks have been experiencing financial crisis in credit lending due to the high level risk associated with improper loan decisions often made by credit officers. This risk includes, loss of principal and interest, disruption to cash flow in the banking system, and increased collection cost, which arises when borrowers fail to pay back acquired credit facility in accordance with the agreed terms of the bank. Several methods have been used in the time past for credit risk evaluation. For instance, the traditional method of granting credit to borrowers is based on judgmental concept using the experience of credit officers and the problems associated with this approach include: high cost of training loan officers; inappropriate decisions; longer period of time required to evaluate a risk; and the possibility of making different decision by different loan officers for the same case (Handzic and Aurum, 2001). To address these problems, methods such as credit scoring, discriminant analysis, logistic regression, and multiple regression were proposed to manage credit risk. However, common limitations of these methods are: the credit scoring methods attempted to correct the biasness of the traditional method but sometimes it misclassify applicants, has the possibility of indirect discrimination, it is not standardized and it varies from one market to another (Crook, 1996) and it does not easily accommodate new changes, discriminant analysis and logistic regression assume multivariate normality and homoscedastic that are often violated in the real world banking data (Giang, 2005; Huang H. et al., 2004), multiple regression require model selection which is based on trial and error process (Leondes, 2005).