<![CDATA[Financial fraud detection is a critical challenge for banking institutions facing increasingly sophisticated threats in digital transaction environments. This study investigates the application of the Random Forest algorithm for detecting fraudulent credit card transactions using the publicly available benchmark dataset from the Université Libre de Bruxelles (284,807 transactions, 0.172% fraud prevalence). Pre-processing includes QuantileTransformer normalization and SMOTE oversampling applied exclusively to the training set to address class imbalance. The model (n_estimators = 200) is validated using a stratified 70/30 split combined with 10-fold cross-validation to ensure robustness and prevent overfitting. Results yield an accuracy of 97%, ROC-AUC of 97%, precision of 95%, recall of 78%, and F1-score of 86%. Comparative evaluation against Logistic Regression, Support Vector Machine, and Gradient Boosting confirms that Random Forest provides the best balance between detection performance and computational efficiency (training: 45 s; inference: 0.3 ms per transaction). Feature importance analysis identifies transaction amount and PCA components V14 and V17 as the most discriminative variables. Confusion matrix analysis reveals 68 False Negatives and 142 False Positives out of 85,443 test samples. Despite these results, limitations include reduced feature interpretability due to PCA transformation, potential geographic data bias, and real-time production deployment challenges. This work confirms the relevance of Random Forest for financial fraud detection and opens perspectives toward hybrid deep learning and graph-based architectures.]]>
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