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@article{205799,
        author = {Nikita Bhandare and Prof.S.N.Gujar},
        title = {Intelligent Adaptive Big Data Fraud Detection System Using AI and Real-Time Analytics},
        journal = {International Journal of Innovative Research in Technology},
        year = {2026},
        volume = {13},
        number = {1},
        pages = {8405-8407},
        issn = {2349-6002},
        url = {https://ijirt.org/article?manuscript=205799},
        abstract = {The rapid growth of digital transactions has significantly increased the risk of financial fraud, making traditional rule-based detection systems inadequate. This paper proposes an intelligent adaptive fraud detection system leveraging Big Data technologies, Artificial Intelligence (AI), and real-time analytics. The system integrates distributed data processing frameworks with machine learning algorithms to analyze high-volume transactional data efficiently. A hybrid model combining supervised and unsupervised learning techniques is used to detect anomalies and predict fraudulent behavior. Real-time data streaming ensures immediate identification of suspicious activities, reducing financial losses. Experimental results demonstrate improved accuracy, reduced false positives, and enhanced scalability compared to traditional approaches. The proposed system is highly suitable for modern financial ecosystems, including banking and e-commerce platforms.},
        keywords = {Big Data, Fraud Detection, Machine Learning, Real-Time Analytics, Artificial Intelligence, Data Mining},
        month = {June},
        }