Optimized Credit Card Fraud Detection Leveraging Ensemble Machine Learning Methods
Received: 19 January 2025 | Revised: 13 February 2025 and 19 February 2025 | Accepted: 27 February 2025 | Online: 23 March 2025
Corresponding author: Mohamed Abdulnabi
Abstract
The increasing number of online financial transactions, particularly those involving credit cards, underscores the urgent need for robust security systems to mitigate financial losses due to fraud. This paper presents a novel machine learning-based approach to credit card fraud detection that addresses the growing demand for enhanced security. Unlike traditional single-model approaches, the proposed ensemble model uniquely combines random forest, logistic regression, and AdaBoost techniques to accurately distinguish between legitimate and fraudulent transactions. The novelty of this work lies in its innovative soft voting mechanism, which aggregates the strengths of these diverse algorithms to achieve superior classification accuracy and minimize false positives. By leveraging the complementary strengths of each model, the ensemble approach provides a robust and adaptive framework capable of detecting emerging fraud patterns in real-time. The results demonstrate that the proposed ensemble model outperforms individual models, achieving an accuracy of 99.96%, a precision of 99.53%, and a recall of 100%, with an F1 score of 0.99 and an Area Under the Curve (AUC) of 1.0. These findings highlight the model's ability to significantly reduce false positives and negatives, making it a highly reliable solution for fraud detection. Furthermore, the model's performance was validated on the PaySim dataset, a large-scale synthetic dataset, where it achieved a 99.97% accuracy, demonstrating its generalizability and robustness in handling complex, real-world scenarios. This research contributes to the field by introducing a highly extensible and adaptable fraud detection framework that improves current solutions and provides a foundation for future advancements in ensemble learning for fraud detection. The proposed model's ability to integrate multiple classifiers while maintaining interpretability and computational efficiency sets it apart from previous studies, offering a promising direction for enhancing the security of online financial transactions.
Keywords:
credit card fraud detection, machine learning, ensemble learning, logistic regression, AdaBoost, random forestDownloads
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Copyright (c) 2025 Al-Anood Al-Maari, Mohamed Abdulnabi, Yogeswaran Nathan, Aitizaz Ali, Uzair Ali, Maqbool Khan
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