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@article{178524,
        author = {Shruthi M K and Ramesh B E and Harshitha N H and Pavitra D},
        title = {UPI FRAUD DETECTION USING MACHINE LEARNING},
        journal = {International Journal of Innovative Research in Technology},
        year = {2025},
        volume = {11},
        number = {12},
        pages = {4297-4301},
        issn = {2349-6002},
        url = {https://ijirt.org/article?manuscript=178524},
        abstract = {Unified Payments Interface (UPI) has revolutionized digital transactions in India by offering fast, convenient, and real-time money transfers. However, with the rapid growth of UPI usage, there has been a significant rise in fraudulent activities, including phishing, social engineering, fake apps, and unauthorized access. This project focuses on developing an intelligent UPI fraud detection system using data analytics and machine learning techniques to enhance transaction security. By analyzing transaction patterns, user behavior, and contextual data (e.g., frequency, location, and amount), the system can identify anomalous activities in real-time. The model leverages supervised learning algorithms like Random Forest, Logistic Regression, and XGBoost to classify transactions as legitimate or suspicious. Additionally, an alert mechanism is integrated to notify users and authorities of potential threats. This proactive approach aims to reduce financial losses, improve user trust, and strengthen the overall digital payment ecosystem. The proposed solution is scalable and can be integrated into existing UPI platforms to ensure safer and smarter transactions.},
        keywords = {UPI, Fraud Detection, Digital Payments, Machine Learning, Anomaly Detection, Cybersecurity, Real-time Monitoring, Transaction Analysis, User Behavior, Phishing, Financial Security, Pattern Recognition, Risk Assessment,},
        month = {May},
        }