Credit Card Fraud Detection using Machine Learning on IEEE-CIS Dataset

Abstract

Credit card fraud detection is widely recognized as a critical issue in digital financial systems due to the rapid growth of online transactions. However, existing approaches often fail to provide a comprehensive comparative framework and show limited effectiveness under highly imbalanced conditions. In this context, this study proposes a systematic machine learning pipeline to analyze fraudulent transactions using the IEEE-CIS dataset (590,000 records, 3–4% fraud). The approach integrates data preprocessing, feature encoding, and stratified sampling, followed by the evaluation of multiple supervised models including Logistic Regression, Decision Tree, Random Forest, SVM, Naive Bayes, and XGBoost. Performance is assessed using Accuracy, Precision, Recall, F1-score, and ROC-AUC. The results indicate that ensemble-based methods outperform traditional classifiers. In particular, XGBoost achieves the highest performance (accuracy 98.22%, ROC-AUC 0.9545, F1-score 0.6759), suggesting a strong ability to handle class imbalance. Nevertheless, no model fully optimizes both precision and recall, highlighting a key limitation in current fraud detection systems. Taken together, these findings suggest that ensemble learning provides a robust baseline, while further improvements through hybrid and cost-sensitive approaches are required for effective real-world deployment.