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

Syed Saaduddin Azhaan, Syed Mohiuddin Jeelani Jaffri, Mirza Younus Ali Baig and Adeeba Anjum
Additional contact information
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

International Journal of Latest Technology in Engineering, Management & Applied Science, 2025, vol. 14, issue 4, 563-566

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.

Date: 2025
References: Add references at CitEc
Citations:

Downloads: (external link)
https://www.ijltemas.in/DigitalLibrary/Vol.14Issue4/563-566.pdf (application/pdf)
https://www.ijltemas.in/papers/volume-14-issue-4/563-566.html (text/html)

Related works:
This item may be available elsewhere in EconPapers: Search for items with the same title.

Export reference: BibTeX RIS (EndNote, ProCite, RefMan) HTML/Text

Persistent link: https://EconPapers.repec.org/RePEc:bjb:journl:v:14:y:2025:i:4:p:563-566

Access Statistics for this article

International Journal of Latest Technology in Engineering, Management & Applied Science is currently edited by Dr. Pawan Verma

More articles in International Journal of Latest Technology in Engineering, Management & Applied Science from International Journal of Latest Technology in Engineering, Management & Applied Science (IJLTEMAS)
Bibliographic data for series maintained by Dr. Pawan Verma ().