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@article{195537,
        author = {Umamaheswararao Mogili},
        title = {Real-Time Credit Card Fraud Detection Using Machine Learning With Interactive Web Dashboard},
        journal = {International Journal of Innovative Research in Technology},
        year = {2026},
        volume = {12},
        number = {11},
        pages = {777-784},
        issn = {2349-6002},
        url = {https://ijirt.org/article?manuscript=195537},
        abstract = {The rapid growth of digital payment systems has significantly increased the risk of credit card fraud, leading to substantial financial losses for banks and customers. Detecting fraudulent transactions in real time is challenging due to the large volume of transactions and the highly imbalanced nature of fraud datasets. This study proposes a machine learning–based credit card fraud detection system integrated with an interactive web application for analyzing and visualizing fraud patterns. The system processes transaction datasets through stages including data preprocessing, feature selection, model training, and evaluation. Multiple machine learning algorithms such as Isolation Forest, Linear Support Vector Machine, and Logistic Regression are implemented to identify suspicious transactions and classify them as fraudulent or legitimate. The proposed framework also includes a web-based dashboard that allows users to upload datasets, execute detection models, and visualize fraud statistics and performance metrics. Experimental results demonstrate that machine learning techniques can effectively identify fraudulent activities while maintaining a balance between detection accuracy and false alarm rates. The system provides a scalable and user-friendly solution that can assist financial institutions in improving transaction monitoring and reducing fraud related risks.},
        keywords = {Credit Card Fraud Detection, Machine Learning, Isolation Forest, Support Vector Machine, Logistic Regression.},
        month = {April},
        }