A discriminating study between three categories of banks based on statistical learning approaches

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

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