Copyright © 2026 Authors retain the copyright of this article. This article is an open access article distributed under the Creative Commons Attribution License which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.

@article{193728,
        author = {Mrs C. Rekha and A Varsha and D Sindhu and P Tejesh and A Yaswanth Reddy},
        title = {CREDIT CARD FRAUD ANALYSIS USING MACHINE LEARNING MODELS},
        journal = {International Journal of Innovative Research in Technology},
        year = {2026},
        volume = {12},
        number = {10},
        pages = {1628-1635},
        issn = {2349-6002},
        url = {https://ijirt.org/article?manuscript=193728},
        abstract = {With the rapid growth of digital financial services, fraudulent credit card transactions have become a major concern in the banking and finance sector. Traditional rule-based fraud detection systems are no longer efficient due to the increasing complexity and large volume of transaction data. As digital payments and cashless transactions are widely encouraged, the risk of fraud has also increased. Many users remain unaware of how their card details can be misused by attackers to perform unauthorized transactions, leading to significant financial losses every year. To address this issue, machine learning techniques provide an effective solution by analyzing transaction patterns and identifying suspicious activities in real time. These intelligent models improve fraud detection accuracy and assist financial institutions in minimizing losses while enhancing customer security.},
        keywords = {Credit Card Fraud, Digital Payments, Financial Security, Fraud Detection, Machine Learning, Online Transactions},
        month = {March},
        }