Credit card fraud has become a critical concern in the digital economy, with fraudulent transactions leading to significant financial losses for both consumers and financial institutions. The increasing volume of online transactions demands efficient and accurate fraud detection systems capable of identifying unusual patterns in real time. This research paper focuses on developing a machine learning–based model for credit card fraud detection using supervised learning techniques such as Logistic Regression, Random Forest, and Neural Networks. The proposed system analyzes transaction features—including amount, location, time, and user behavior—to distinguish between legitimate and fraudulent activities. To address data imbalance, techniques like Synthetic Minority Oversampling (SMOTE) are applied, improving the classifier’s sensitivity to rare fraud cases. The model’s performance is evaluated using metrics such as precision, recall, F1-score, and ROC-AUC, demonstrating that ensemble learning provides higher detection accuracy and reduced false positives compared to traditional methods. The study concludes that integrating advanced algorithms with real time monitoring and adaptive learning can significantly enhance the effectiveness of credit card fraud detection systems.
Aptikar et al. (2025) studied this question.