Title: A probabilistic neural network approach for modelling and classifying efficiency of GCC banks

Authors: Mohamed M. Mostafa

Addresses: College of Business, Auburn University, 242 Lowder Business Building, Auburn, AL 36849, USA

Abstract: Understanding the efficiency levels is crucial for understanding the competitive structure of a market and/or segments of a market. This study uses two artificial Neural Networks (NN) and a traditional statistical classification method to model and classify the relative efficiency of top Gulf Cooperation Council (GCC) banks. Accuracy indices are used to assess the classification accuracy of the models. Results indicate that the predictive accuracy of NN models is quite similar to that of traditional statistical methods. The study shows that the NN models have a great potential for the classification of banks| relative efficiency due to their robustness and flexibility of modelling algorithms. The implications of these results for potential efficiency programmes are discussed.

Keywords: data envelopment analysis; DEA; probabilistic neural networks; discriminant analysis; relative efficiency; benchmarking; Gulf Cooperation Council; GCC banks; modelling; artificial neural networks; ANNs; banking efficiency; efficiency classification.

DOI: 10.1504/IJBPM.2009.024373

International Journal of Business Performance Management, 2009 Vol.11 No.3, pp.236 - 258

Published online: 02 Apr 2009 *

Full-text access for editors Access for subscribers Purchase this article Comment on this article