Open Science Research Excellence
%0 Journal Article
%A Nnamdi I. Nwulu and  Shola G. Oroja
%D 2011 
%J  International Journal of Mathematical, Computational, Physical, Electrical and Computer Engineering
%B World Academy of Science, Engineering and Technology
%I International Science Index 54, 2011
%T A Comparison of Different Soft Computing Models for Credit Scoring
%U http://waset.org/publications/11592
%V 54
%X It has become crucial over the years for nations to
improve their credit scoring methods and techniques in light of the
increasing volatility of the global economy. Statistical methods or
tools have been the favoured means for this; however artificial
intelligence or soft computing based techniques are becoming
increasingly preferred due to their proficient and precise nature and
relative simplicity. This work presents a comparison between Support
Vector Machines and Artificial Neural Networks two popular soft
computing models when applied to credit scoring. Amidst the
different criteria-s that can be used for comparisons; accuracy,
computational complexity and processing times are the selected
criteria used to evaluate both models. Furthermore the German credit
scoring dataset which is a real world dataset is used to train and test
both developed models. Experimental results obtained from our study
suggest that although both soft computing models could be used with
a high degree of accuracy, Artificial Neural Networks deliver better
results than Support Vector Machines.
%P 883 - 888