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@article{205337,
        author = {Dr.J.Jensy Rajakumari and Ms. K. Jeyapriya},
        title = {MACHINE LEARNING BASED UPI FRAUD DETECTION SYSTEM},
        journal = {International Journal of Innovative Research in Technology},
        year = {2026},
        volume = {13},
        number = {1},
        pages = {6247-6250},
        issn = {2349-6002},
        url = {https://ijirt.org/article?manuscript=205337},
        abstract = {In recent years, online payment methods such as UPI, mobile banking, and digital wallets have become very popular because they make transactions faster and more convenient. At the same time, the number of fraud cases, including phishing, unauthorized access, and suspicious transactions, has also increased.
This project focuses on developing a machine learning-based system to detect fraudulent UPI transactions. In our approach, we use machine learning algorithms like Random Forest, XGBoost, and Logistic Regression to classify whether a transaction is genuine or fraudulent.
Before training the model, the transaction data is processed through several steps such as data cleaning, normalization, and feature engineering. We also apply anomaly detection techniques to identify unusual patterns. The performance of the model is evaluated using metrics like accuracy, precision, recall, F1-score, and ROC-AUC.
The proposed system can detect fraud in real-time and provides a scalable and efficient solution to improve the security of digital payments.},
        keywords = {UPI Fraud Detection, Machine Learning, Online Payments, Cyber Security, Random Forest, XGBoost},
        month = {June},
        }