Volume 13, Number 2

Credit Risk Management using Artificial Intelligence Techniques


Karim Amzile and Rajaa Amzile, Mohammed V University of Rabat, Morocco


Artificial intelligence techniques are still revealing their pros; however, several fields have benefited from these techniques. In this study we applied the Decision Tree (DT-CART) method derived from artificial intelligence techniques to the prediction of the creditworthy of bank customers, for this we used historical data of bank customers. However we have adopted the flowing process, for this purpose we started with a data preprocessing in which we clean the data and we deleted all rows with outliers or missing values, then we fixed the variable to be explained (dependent or Target) and we also thought to eliminate all explanatory (independent) variables that are not significant using univariate analysis as well as the correlation matrix, then we applied our CART decision tree method using the SPSS tool.

After completing our process of building our model (DT-CART), we started the process of evaluating and testing the performance of our model, by which we found that the accuracy and precision of our model is 71%, so we calculated the error ratios, and we found that the error rate equal to 29%, this allowed us to conclude that our model at a fairly good level in terms of precision, predictability and very precisely in predicting the solvency of our banking customers.


Data Mining, Credit Risk, Bank, Decision Tree, Artificial Intelligence, risk management.