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@article{173756,
        author = {M.krishna prasath and J.Srinath and Alshihab},
        title = {Fraud detection using machine learning},
        journal = {International Journal of Innovative Research in Technology},
        year = {2025},
        volume = {11},
        number = {10},
        pages = {4617-4626},
        issn = {2349-6002},
        url = {https://ijirt.org/article?manuscript=173756},
        abstract = {Fraud detection is a critical area of concern in financial institutions, e-commerce platforms, and other digital transaction-based industries. With the increasing number of online transactions, fraudulent activities have also evolved, making traditional rule-based fraud detection methods less effective. Machine learning (ML) provides an advanced and intelligent approach to detecting fraud in real-time by analyzing transactional data, identifying hidden patterns, and recognizing anomalies. This paper explores real-time fraud detection techniques using ML, including supervised learning (logistic regression, decision trees, random forests, neural networks), unsupervised learning (anomaly detection, clustering), and hybrid models that combine both approaches. We discuss feature engineering, model training, and the use of real-time streaming frameworks like Apache Kafka and Spark for fraud detection. Additionally, we highlight challenges such as imbalanced datasets, evolving fraud patterns, and computational efficiency.
By leveraging ML, businesses can enhance fraud detection accuracy, reduce false positives, and improve security in financial transactions.},
        keywords = {},
        month = {March},
        }