Affiliations: [a] Université de Tunis, ISGT, LR99ES04 BESTMOD, Le Bardo, Tunisia | [b] College of Economics and Management of Sfax, University of Sfax, Sfax, Tunisia
Correspondence:
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Corresponding author: Anis Ben Ishak, Université de Tunis, ISGT, LR99ES04 BESTMOD, 2000, Le Bardo, Tunisia. Tel.: +216 97 549 940; Fax: +216 71 568 767; E-mail:anis_isg@yahoo.fr
Abstract: This work addresses the problem of knowledge extraction within the banking
domain using statistical learning systems. Our main goal is to assess the
power of the accounting ratios to discriminate between Islamic, mixed and
conventional banks in the Gulf Cooperation Council (GCC) region. To this
end, we have used the two popular statistical learning methods, namely
Support Vector Machines (SVM) and Random Forests (RF). An intensive
comparative study is performed between them for the purpose of variable
ranking and selection within a nonlinear multiclass framework. The
experiments conducted on different simulated datasets and on the real
dataset show that RF are slightly better than SVM. In the real application, we had recourse to the financial semantics based on
experts' domain knowledge to decide between the competitive approaches. The
results show the importance of the mutual financial information between some
ratios to distinguish between the three categories of banks. Moreover, we
have demonstrated that mixed banks are more akin to conventional ones.
Finally, it was shown that RF are more robust to the selection bias problem
and classification accuracy is slightly improved by the ratios selection.
Keywords: Nonlinear multiclass classification, support vector machines, random forests, variable selection, knowledge discovery, islamic banking
