  In this study we applied the CART-type Decision Tree (DT-CART) method derived
from artificial intelligence technique to the prediction of the solvency of
bank customers, for this we used historical data of bank customers. However we
have adopted the process of Data Mining techniques, 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 as well as rows with empty columns, 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 (AD-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.
