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@article{197330,
        author = {Snehal N. Katgube and Shahid R. Khan and Yash A. Pande and Praful S. Pandey and Kshitija R. Muneshwar},
        title = {UPI Fraud Detection using Machine Learning},
        journal = {International Journal of Innovative Research in Technology},
        year = {2026},
        volume = {12},
        number = {11},
        pages = {6524-6529},
        issn = {2349-6002},
        url = {https://ijirt.org/article?manuscript=197330},
        abstract = {The widespread adoption of the Unified Payments Interface (UPI) has made digital transactions faster and more convenient, but it has also increased the risk of online payment fraud. Conventional rule-based security mechanisms are often ineffective in identifying sophisticated and evolving fraud patterns. This paper presents a machine learning-based approach for real-time detection of fraudulent UPI transactions. The proposed system analyzes historical transaction data and extracts key features such as transaction amount, frequency, and account activity to identify abnormal behavior. Multiple machine learning algorithms are trained and evaluated to classify transactions as either legitimate or fraudulent. The system assigns a risk score to each transaction and can automatically flag or block suspicious activities before they are completed. Experimental results demonstrate that the proposed approach improves detection accuracy and reduces false positives compared to traditional methods. Overall, the framework enhances the security of digital payment systems and strengthens user confidence in online financial transactions.},
        keywords = {UPI Fraud Detection, Machine Learning, XGBoost, Random Forest, Logistic Regression, Behavioral Analysis.},
        month = {April},
        }