تقدير مخاطر الإئتمان المصرفي باستعمال نموذج الشبكة العصبية الإصطناعية ذات الدالة الشعاعية (RBF)‎ : دراسة حالة بنك القرض الشعبي الجزائري

الملخص EN

This study aims to estimate the credit risk in Algerian banks and make the right lending decision using the RBF radial basis function artificial neural network model.

To achieve this, we formed a database of financial and qualitative variables for 100 of borrowing institutions From the CPA bank, this sample divided into 50 good Borrower institutions and 50 other bad borrower institutions.

To create the RBF model, we used SPSS(V25) program.

The results of the study showed that the artificial neural networks model had shown an accuracy in classification at 100%, which would help Algerian banks to predict credit risks and make wise and speed lending's decision more than the classic models, but these modern approaches require robust technologies devices and quantitative and statistical methods.