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@article{200576,
        author = {Dhairya Dev and Gaurav Kumar and Deepak Gupta},
        title = {A Cloud-Enabled Machine Learning Framework for Fraud},
        journal = {International Journal of Innovative Research in Technology},
        year = {2026},
        volume = {12},
        number = {12},
        pages = {1370-1373},
        issn = {2349-6002},
        url = {https://ijirt.org/article?manuscript=200576},
        abstract = {Online financial ecosystems have grown rapidly, exposing more online transactions to fraud. Conventional rule-based approaches to fraud detection are becoming obsolete in the face of dynamic and evolving fraud schemes. This paper proposes a machine learning system leveraging cloud computing to improve fraud detection with scalable processing and smart data analytics. Our approach combines supervised and unsupervised learning methods to identify known and unknown fraudulent transactions in real time. The design uses cloud computing to achieve high-throughput, low-latency and scalable processing. The research proposes a system architecture, approach, testing and evaluation methods and performance results without including synthetic data. The architecture seeks to deliver a viable solution for today's financial systems that need to adaptively, efficiently and scalably detect fraud. [7], [8].},
        keywords = {Fraud Detection, Cloud Computing, Machine Learning, Anomaly Detection, Financial [2] Security, Real-Time Systems.},
        month = {May},
        }