<![CDATA[The development of digital technology has motivated rapid growth in online transactions, so the increase in the volume of digital transactions also increases the risk of credit card fraud, particularly in transactions where a card is not present. By employing the Extreme Gradient Boosting (XGBoost) method in conjunction with the Synthetic Minority Over-sampling Technique (SMOTE) to solve class imbalance and fine-tuning model parameters using GridSearchCV, this study aims to improve a fraud detection system. The dataset, which consists of anonymized credit card transactions, presents a stark imbalance with fraudulent cases accounting for only 0.172% of the data. The study involves several stages: preprocessing the data, balancing class distribution, training the model, and evaluating its performance through metrics such as F1-score, precision, recall, accuracy, and AUC-ROC. Implementation of SMOTE proved effective in enhancing the representation of rare fraud cases without introducing overfitting, while GridSearchCV identified the most effective parameter configuration. The resulting model achieved top-tier performance with 100% accuracy, 0.81 precision, 0.85 recall, an F1-score of 0.83, and an AUC-ROC of 0.979, indicating strong capability in distinguishing fraudulent from legitimate transactions. The novelty of this study lies in the systematic integration of SMOTE, XGBoost, and GridSearchCV into a unified pipeline designed to address extreme class imbalance in real-world credit card transactions. Unlike previous studies that focused solely on algorithm comparison or hyperparameter tuning, this research emphasizes reducing false negatives, which pose the greatest financial and reputational risks. The findings not only demonstrate superior performance metrics but also provide practical contributions for financial institutions, regulators, and e-commerce platforms in developing scalable, reliable, and adaptive fraud detection systems]]>
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