Modeling the efficiency of top Arab banks: A DEA-neural network approach
This study investigates the efficiency of top Arab banks using two quantitative methodologies: data envelopment analysis and neural networks. The study uses a probabilistic neural network (PNN) and a traditional statistical classification method to model and classify the relative efficiency of top Arab 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 modeling algorithms. From a policy perspective, this study highlights the economic importance of encouraging increased efficiency throughout the banking industry in the Arab world. (C) 2007 Elsevier Ltd. All rights reserved.
arab banks data envelopment analysis probabilistic neural networks relative efficiency discriminant analysis benchmarking