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@article{172381,
        author = {Aditya Prakash and Ravi Bhushan and P. Karthik and T. Vinay Kumar},
        title = {A Research Paper on Fraudulent Transaction Detection System},
        journal = {International Journal of Innovative Research in Technology},
        year = {2025},
        volume = {11},
        number = {8},
        pages = {3283-3289},
        issn = {2349-6002},
        url = {https://ijirt.org/article?manuscript=172381},
        abstract = {Fraudulent transactions pose a significant challenge in today's digital economy, impacting both financial institutions and individuals. This research paper explores the design and development of a Fraudulent Transaction Detection System that leverages advanced computational techniques to identify and mitigate fraudulent activities in real time. By employing machine learning algorithms, pattern recognition, and statistical methods, the system analyzes transaction data for anomalies indicative of fraud. The proposed framework integrates supervised and unsupervised learning models to enhance detection accuracy and minimize false positives. Furthermore, the study highlights the system's adaptability to evolving fraud patterns through continuous learning mechanisms. Experimental results demonstrate its effectiveness in processing large-scale datasets while ensuring timely and reliable detection of fraudulent transactions. This research contributes to the growing need for robust, scalable, and intelligent solutions to combat financial fraud, ensuring enhanced security and trust in digital financial ecosystems.},
        keywords = {Fraud detection, Anomaly Detection, Financial Security, Adaptive systems.},
        month = {January},
        }