Autors: Ujkani B., Mustafa S., Minkovska, D. V.
Title: Performance Comparison of Supervised Machine Learning Algorithms for Credit Card Fraud Detection
Keywords: fraud detection, machine learning, Random Forest, Smote, XGBoost

Abstract: This paper presents a comparative study of machine learning models for credit card fraud detection using an imbalanced dataset. After cleaning, normalization, balancing with SMOTE and Tomek Links, classifiers including Logistic Regression, Decision Tree, Random Forest, XGBoost, k-NN, Naive Bayes, and Neural Networks were trained and evaluated. XGBoost was tested with real-time detection and temporal drift analysis. Simulated federated learning demonstrated privacy-preserving collaboration. Results show ensemble methods, especially Random Forest and XGBoost, achieve top accuracy and AUC, minimizing fraud losses.

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Issue

2025 34th International Scientific Conference Electronics, ET 2025 - Proceedings, 2025, Albania, https://doi.org/10.1109/ET66806.2025.11204037

Вид: публикация в международен форум, публикация в реферирано издание, индексирана в Scopus