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@article{191889,
        author = {Ballatigi Anvitha and Uppara Sumaharika and Dhotray Kalyani and B . Devi and Dr C V Madhusudhan Reddy and N.Jayamma},
        title = {UPI Fraud Detection Using ML},
        journal = {International Journal of Innovative Research in Technology},
        year = {2026},
        volume = {12},
        number = {8},
        pages = {8056-8060},
        issn = {2349-6002},
        url = {https://ijirt.org/article?manuscript=191889},
        abstract = {Rapid growth in Unified Payment Interface (UPI) systems made digital payments much more convenient, but this growth also brought with it cases of fraudulent activity in digital transactions. Conventional rule-based techniques to handle fraudulent activity are often found to be ineffective in detecting subtle changes in fraudulent activity, often resulting in misleading results. In this regard, this present research proposes that machine learning-based techniques be used to generate a fraudulent activity detector system for digital transactions made through Unified Payment Interface systems, which accurately identifies fraudulent activity by employing machine learning algorithms that analyze transaction activity. Unlike conventional techniques, which often fail to produce accurate results, machine learning techniques can accurately distinguish between fraudulent activity undertaken in Unified Payment Interface digital transactions. For this purpose, this present research proposes that Logistic Regression, Random Forest, and XGBoost machine learning techniques be employed to produce accurate results, as these machine learning techniques are quite accurate in detecting subtle changes in fraudulent activity, which makes this proposed model much superior to conventional techniques for detecting fraudulent activity in Unified Payment Interface digital transactions.},
        keywords = {UPI Fraud Detection, Machine Learning, Digital Payment Security, Financial Fraud Analysis, Transaction Monitoring, Supervised Learning, Anomaly Detection},
        month = {January},
        }