A Comprehensive Performance Comparison of Traditional and Ensemble Machine Learning Models for Online Fraud Detection

• URL: http://arxiv.org/abs/2509.17176v1
• Date: Sun, 21 Sep 2025 17:53:24 GMT
• Title: A Comprehensive Performance Comparison of Traditional and Ensemble Machine Learning Models for Online Fraud Detection
• Authors: Ganesh Khekare, Shivam Sunda, Yash Bothra,
• Abstract summary: Real-time fraud detection is essential for financial security but remains challenging due to high transaction volumes and the complexity of modern fraud patterns.<n>This study presents a comprehensive comparison between traditional machine learning models like Random Forest, SVM, Logistic Regression, and ensemble methods like Stacking and Voting.<n>The ensemble methods achieved an almost perfect precision of around 0.99, but traditional methods demonstrated superior performance in terms of recall.
• Score: 0.0
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: In the era of the digitally driven economy, where there has been an exponential surge in digital payment systems and other online activities, various forms of fraudulent activities have accompanied the digital growth, out of which credit card fraud has become an increasingly significant threat. To deal with this, real-time fraud detection is essential for financial security but remains challenging due to high transaction volumes and the complexity of modern fraud patterns. This study presents a comprehensive performance comparison between traditional machine learning models like Random Forest, SVM, Logistic Regression, XGBoost, and ensemble methods like Stacking and Voting Classifier for detecting credit card fraud on a heavily imbalanced public dataset, where the number of fraudulent transactions is 492 out of 284,807 total transactions. Application-specific preprocessing techniques were applied, and the models were evaluated using various performance metrics. The ensemble methods achieved an almost perfect precision of around 0.99, but traditional methods demonstrated superior performance in terms of recall, which highlights the trade-off between false positives and false negatives. The comprehensive comparison reveals distinct performance strengths and limitations for each algorithm, offering insights to guide practitioners in selecting the most effective model for robust fraud detection applications in real-world settings.

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