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@article{175302,
        author = {Hrushikesh Khairnar and Shree Gaikar and Devesh Shingole and Yukta Patil},
        title = {CREDIT CARD FRAUD DETECTION},
        journal = {International Journal of Innovative Research in Technology},
        year = {2025},
        volume = {11},
        number = {11},
        pages = {3004-3009},
        issn = {2349-6002},
        url = {https://ijirt.org/article?manuscript=175302},
        abstract = {Credit card fraud detection is a vital area in ensuring the security of financial transactions in an increasingly digital world. With the rise of online shopping and digital payments, fraud has become a major concern for both consumers and financial institutions. This project aims to develop a machine learning-based model that detects fraudulent credit card transactions in real-time. By leveraging various classification algorithms such as logistic regression, decision trees, and neural networks, the model is trained on a comprehensive dataset of historical transaction data. It analyses transaction patterns to identify unusual activities such as sudden spikes in spending, international transactions, or atypical purchasing behaviour.
The goal of this project is to create an efficient and accurate fraud detection system that balances the need for minimizing false positives while quickly identifying actual fraudulent cases. By improving the speed and precision of fraud detection, the system aims to enhance security for cardholders and reduce financial losses due to fraud. Ultimately, this project contributes to the ongoing efforts in the banking and financial sector to improve fraud detection technologies and ensure safer, more secure transactions for consumers worldwide.},
        keywords = {},
        month = {April},
        }