Credit Card Fraud Detection Using Machine Learning
DOI:
https://doi.org/10.15662/IJEETR.2026.0802194Keywords:
Credit Card Fraud Detection, Machine Learning, XGBoost, SMOTE, Class Imbalance, Transaction Analysis, Fraud Prediction, Financial SecurityAbstract
: Credit card fraud has become a critical issue with the rapid expansion of digital payment systems and online financial transactions, necessitating the development of intelligent and efficient fraud detection mechanisms. Traditional rule-based systems often fail to identify sophisticated and evolving fraud patterns due to their dependence on predefined rules and limited adaptability. In response, Machine Learning (ML) techniques have emerged as a powerful solution for detecting fraudulent transactions by analyzing large-scale transaction data and identifying hidden patterns and anomalies.
Recent advancements in ML have enabled the development of robust fraud detection systems capable of handling highly imbalanced datasets and improving detection accuracy. Techniques such as the Synthetic Minority Over-sampling Technique (SMOTE) are employed to address class imbalance by generating synthetic fraud samples, thereby enhancing the model’s ability to learn minority class patterns effectively. Furthermore, ensemble learning methods such as Extreme Gradient Boosting (XGBoost) have demonstrated superior performance by combining multiple weak learners to improve prediction accuracy and reduce false positives.
The proposed system utilizes machine learning algorithms including Logistic Regression, Decision Tree, and XGBoost to analyze transaction features and classify them as fraudulent or legitimate. The models are evaluated using performance metrics such as precision, recall, F1-score, and Area Under the ROC Curve (AUC), ensuring a comprehensive assessment beyond accuracy. Experimental results indicate that XGBoost outperforms other models in detecting fraudulent transactions with higher reliability.
This study highlights the effectiveness of machine learning-based approaches in enhancing financial security and provides a scalable solution for real-time credit card fraud detection in modern banking systems
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