The application of techniques derived from artificial intelligence to the prediction of the solvency of bank customers: case of the application of the cart type decision tree (dt)

TL;DR

This paper demonstrates the application of a CART decision tree model, derived from AI techniques, to predict bank customer solvency using data mining and preprocessing, achieving a 71% accuracy.

Contribution

It introduces a specific application of CART decision trees for bank solvency prediction, including data preprocessing and performance evaluation.

Findings

Model accuracy of 71% in predicting solvency

Error rate of 29% indicating fairly good performance

Effective use of data mining techniques for model preparation

Abstract

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.