%0 Journal Article
	%A D. A. Farinde
	%D 2013
	%J International Journal of Mechanical and Industrial Engineering
	%B World Academy of Science, Engineering and Technology
	%I Open Science Index 82, 2013
	%T A Statistical Prediction of Likely Distress in Nigeria Banking Sector Using a Neural Network Approach
	%U https://publications.waset.org/pdf/17098
	%V 82
	%X One of the most significant threats to the economy of a nation is the bankruptcy of its banks. This study evaluates the susceptibility of Nigerian banks to failure with a view to identifying ratios and financial data that are sensitive to solvency of the bank. Further, a predictive model is generated to guide all stakeholders in the industry. Thirty quoted banks that had published Annual Reports for the year preceding the consolidation i.e. year 2004 were selected. They were examined for distress using the Multilayer Perceptron Neural Network Analysis. The model was used to analyze further reforms by the Central Bank of Nigeria using published Annual Reports of twenty quoted banks for the year 2008 and 2011. The model can thus be used for future prediction of failure in the Nigerian banking system.

	%P 2720 - 2725