Identifying Fraudulent Credit Card Transactions Using Ensemble Learning

Identifying deceitful praise postcard relations remains a significant challenge for financial institutions. Even after passing authentication checks, fraudulent activities can still occur if attackers successfully impersonate legitimate cardholders. This research explores the effectiveness of ensemble learning techniques in categorizing praise card scam using two different datasets: a synthetic dataset generated by Sparkov and a real-world dataset from European Union consumers. The models employed include XGBoost, Random Forest, and Naive Bayes classifiers. These models are evaluated based on accuracy, precision, recall, and F1 score. Outcomes signpost that collective means yield high performance on the real-world dataset but show limited effectiveness on the synthetic one. The findings suggest that deterministic patterns in real-world transaction data can be easily learned by machine learning models, while the lack of randomness may inadvertently increase the risk of card information exposure. Keywords: Credit card fraud detection, ensemble learning, XGBoost, Random Forest, Naive Bayes, synthetic dataset, real-world dataset, fraudulent transactions, machine learning, financial security