  The use of credit cards has recently increased, creating an essential need
for credit card assessment methods to minimize potential risks. This study
investigates the utilization of machine learning (ML) models for credit card
default prediction system. The main goal here is to investigate the
best-performing ML model for new proposed credit card scoring dataset. This new
dataset includes credit card transaction histories and customer profiles, is
proposed and tested using a variety of machine learning algorithms, including
logistic regression, decision trees, random forests, multi-layer perceptron
(MLP) neural network, XGBoost, and LightGBM. To prepare the data for machine
learning models, we perform data pre-processing, feature extraction, feature
selection, and data balancing techniques. Experimental results demonstrate that
MLP outperforms logistic regression, decision trees, random forests, LightGBM,
and XGBoost in terms of predictive performance in true positive rate, achieving
an impressive area under the curve (AUC) of 86.7% and an accuracy rate of
91.6%, with a recall rate exceeding 80%. These results indicate the superiority
of MLP in predicting the default customers and assessing the potential risks.
Furthermore, they help banks and other financial institutions in predicting
loan defaults at an earlier stage.
