Credit Card Fraud Detection Using Random Forest and CART Algorithms: A Machine Learning Perspective

Abstract: The increasing adoption of online payments and e-commerce platforms has amplified the threat of credit card fraud. As fraudsters continuously develop advanced techniques to bypass traditional security systems, it becomes essential to deploy smart, adaptive solutions. This study focuses on leveraging machine learning—specifically Random Forest and Classification and Regression Trees (CART)—to build a high-performance fraud detection system. Using a publicly available dataset from Kaggle, the model analyzes transaction records to uncover patterns indicative of fraudulent behavior. Emphasis is placed on accuracy, scalability, and the potential for real-time deployment. The implemented model achieved an impressive accuracy of 99.78%, with strong precision and recall scores. The paper discusses the methodologies applied, evaluates the outcomes, and recommends directions for future development.

Fraud Detection, Random Forest, CART, Credit Card Transactions, Machine Learning, PCA, Supervised Learning, Data Imbalance

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