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@article{184930,
        author = {SHAIK FIAZA TAZEEN and D.MURALI},
        title = {AI in Finance: Enhancing Credit Card Fraud Detection Using Machine Learning for Improving Accuracy, Real-Time Prevention and Increasing Financial Security},
        journal = {International Journal of Innovative Research in Technology},
        year = {2025},
        volume = {12},
        number = {4},
        pages = {3460-3466},
        issn = {2349-6002},
        url = {https://ijirt.org/article?manuscript=184930},
        abstract = {In the realm of digital finance, credit card fraud has become a growing concern due to the surge in online transactions. This project offers a machine learning-based solution to detect fraudulent transactions, leveraging Random Forests to differentiate between legitimate and fraudulent activities in real time. The web application provides users with an intuitive interface for loading datasets, training models, making predictions, visualizing fraud patterns, and generating performance reports. The methodology includes preprocessing steps like encoding transaction types, scaling numeric features, and splitting the data into training and testing sets. A Random Forest classifier is optimized using GridSearchCV to maximize accuracy. The project also features a fraud detection simulation, allowing users to input transaction details and receive predictions on potential fraud via an interactive interface. While traditional methods like logistic regression and decision trees have been widely used, this project focuses on Random Forests for enhanced accuracy and efficiency. The model excels in handling high-dimensional data and minimizing overfitting. Comprehensive visualizations, such as histograms and pie charts, provide deep insights into transaction patterns, offering a clear understanding of fraud trends and system performance.},
        keywords = {credit card fraud, online transactions, machine learning, Random Forest, data preprocessing, fraud detection, GridSearchCV, real- time prediction, transaction behavior, model performance, hyperparameter tuning, interactive interface, financial losses, fraud trends, visualizations, accuracy, computational efficiency.},
        month = {September},
        }