Comparative Evaluation of Machine Learning Models for Credit Card Approval Prediction Using Class Imbalance Handling Methods in the PyCaret AutoML Framework
Year: 2026
Type: article
Abstract: Credit card approval processes are often performed manually, leading to inefficiencies, higher operational costs, and inconsistent decision-making. This study comparatively evaluates several machine learning models under different class imbalance handling strategies to predict credit card approval using the PyCaret AutoML framework. Two experimental scenarios were conducted on a dataset of 25,128 credit card applications, namely PyCaret's built-in SMOTE-based imbalance handling and random undersampling. In the SMOTE-based scenario, the Random Forest model achieved high accuracy, but its recall for the minority class was only 36.11%. In contrast, random undersampling produced more balanced performance, with the Gradient Boosting model achieving 100% recall. These findings indicate that class imbalance handling plays a critical role in credit card approval prediction, and that random undersampling provides more reliable minority-class detection than PyCaret's built-in SMOTE approach for the evaluated dataset.
Institution: Satya Wacana Christian University
Language: English
Subfield: Artificial Intelligence
Field: Computer Science
Domain: Physical Sciences
SDG: Reduced inequalities
Open Access status: diamond
OpenAlex ID: W7225393454