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

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  • Syed Saaduddin Azhaan

    (UG Scholar , Lords Institute of Engineering and Technology)

  • Syed Mohiuddin Jeelani Jaffri

    (UG Scholar, Lords Institute of Engineering and Technology)

  • Mirza Younus Ali Baig

    (Assistant Professor, Lords Institute of Engineering and Technology)

  • Adeeba Anjum

    (Assistant Professor, Lords Institute of Engineering and Technology)

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.

Suggested Citation

  • Syed Saaduddin Azhaan & Syed Mohiuddin Jeelani Jaffri & Mirza Younus Ali Baig & Adeeba Anjum, 2025. "Credit Card Fraud Detection Using Random Forest and CART Algorithms: A Machine Learning Perspective," International Journal of Latest Technology in Engineering, Management & Applied Science, International Journal of Latest Technology in Engineering, Management & Applied Science (IJLTEMAS), vol. 14(4), pages 563-566, April.
    • Handle: RePEc:bjb:journl:v:14:y:2025:i:4:p:563-566
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