Enhancing credit card transaction fraud detection with random forest and robust scaling Available to Purchase

In an era where credit card fraud poses an ever-increasing threat to financial institutions and consumers, the precise detection of fraudulent transactions is paramount. This study delves into the realm of data science and machine learning to fortify the defenses against credit card fraud. We evaluate the performance of three distinct machine learning models—decision trees, random forests, and logistic regression—in classifying, predicting, and detecting fraudulent credit card transactions. Our findings reveal that the Random Forest model emerged as the standout performer, achieving an impressive accuracy rate of 99% and boasting an AUC (Area Under the Curve) of 98.5% in the identification and prediction of fraudulent credit card transactions. This remarkable accuracy, combined with superior precision, recall, and F1-score, positions Random Forest as the optimal choice for the critical task of credit card fraud detection. Furthermore, we emphasize the importance of employing the RobustScaler preprocessing technique, which contributed significantly to enhancing the robustness and overall performance of our machine learning models. The study underscores the applicability of Random Forest for precise and equitable categorization, particularly for the minority class, making it a compelling choice for real-world applications. As fraudsters continue to evolve their tactics, the use of advanced machine learning techniques, exemplified by Random Forest, becomes increasingly crucial in safeguarding the integrity of credit card transactions. This research offers valuable insights into the frontlines of fraud detection, providing a foundation for enhanced security in the payment ecosystem.