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@article{181750,
        author = {shital pawar and Vaibhavi Dhapate and Aniket Lahane and Prof. Sonali Shewale},
        title = {CREDIT CARD FRAUD DETECTION USING MACHINE LEARNING},
        journal = {International Journal of Innovative Research in Technology},
        year = {2025},
        volume = {12},
        number = {1},
        pages = {5428-5431},
        issn = {2349-6002},
        url = {https://ijirt.org/article?manuscript=181750},
        abstract = {Credit card fraud poses a significant concern for both financial institutions and their customers. To combat this issue, researchers have explored numerous techniques, including machine learning and deep learning, to develop effective credit card fraud detection systems.[6] credit card fraud detection system using machine learning, thereby addressing the growing financial risks associated with increased transaction volumes. 
Utilizing a supervised learning approach on historical transaction data, the methodology encompasses data preprocessing, model selection. 
 Techniques to handle imbalanced datasets, such as SMOTE, are employed. The expected outcome is a high-accuracy model that effectively distinguishes fraudulent transactions, minimizing false positives and negatives, and improving overall fraud detection rates."},
        keywords = {},
        month = {June},
        }