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@article{194142,
        author = {Shravani Gaikwad and Rigved Shelar and Parth Dolase and Prisha Sharma},
        title = {Phishing and UPI fraud detection using Machine Learning},
        journal = {International Journal of Innovative Research in Technology},
        year = {2026},
        volume = {12},
        number = {10},
        pages = {2460-2468},
        issn = {2349-6002},
        url = {https://ijirt.org/article?manuscript=194142},
        abstract = {The increasing prevalence of digital payments has led to a corresponding rise in fraudulent activities, necessitating robust detection mechanisms. Unified Payments Interface (UPI), a groundbreaking platform enabling instant financial transactions, has revolutionized the digital payment landscape but has also become a target for sophisticated fraud. This study presents an innovative fraud detection framework utilizing advanced machine learning techniques, behavioural analytics, and network-based anomaly detection. By analysing a heterogeneous dataset comprising authentic and fraudulent transactions, critical features such as transaction amount, timestamps, payer/payee details, and location information are engineered to enhance the model's performance. Time-sensitive and behavioural patterns are prioritized to identify anomalies effectively. The proposed system integrates both feature-based and network-based anomaly detection, leveraging the interaction between entities and their attributes to uncover hidden patterns associated with fraud. Real-time monitoring and alert mechanisms ensure immediate intervention, thereby safeguarding user trust and financial assets. Experimental results demonstrate the system's superior accuracy and adaptability compared to traditional methods, significantly reducing financial losses and enhancing the security of UPI transactions. This multi-faceted approach addresses diverse fraud scenarios, from transaction manipulation to money laundering, establishing a new benchmark in digital payment security.},
        keywords = {UPI, Fraud Detection, Machine Learning, Anomaly Detection, Behavioural Analytics, Network Analysis, Financial Security},
        month = {March},
        }