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@article{173820,
        author = {Cheeraboyina Karthik and Sai Balakrishna Sikhakolli and Bavirisetti Venkata Mahesh and Garimella Vijay Bhaskar and Akiri Devesh},
        title = {Leveraging Deep Learning for Real-Time Financial Fraud Prevention},
        journal = {International Journal of Innovative Research in Technology},
        year = {2025},
        volume = {11},
        number = {10},
        pages = {1624-1628},
        issn = {2349-6002},
        url = {https://ijirt.org/article?manuscript=173820},
        abstract = {In today’s digital economy, financial transactions are the backbone of commerce, but their growing volume and complexity have increased the risk of fraudulent activities. Traditional rule-based fraud detection systems struggle to adapt to evolving patterns and sophisticated techniques used by fraudsters. This project proposes an AI-driven approach that leverages Deep Learning to detect anomalies in financial transactions, enabling more efficient, accurate, and proactive fraud detection. The system leverages deep learning models, including Autoencoders and Recurrent Neural Networks (RNNs), to analyze transaction data and identify fraudulent activities. These neural networks are trained on a rich dataset of transaction records, utilizing features such as transaction amounts, frequencies, user behavior, and potentially geolocation patterns. By learning from large volumes of historical transaction data, the system can recognize complex patterns and detect deviations from typical transaction behaviors, enabling real-time fraud detection with minimal human intervention. This research highlights the transformative potential of Deep Learning in financial security. The proposed solution not only enhances detection accuracy but also minimizes false positives, reducing unnecessary disruptions for legitimate users. Future extensions of this project could include integrating blockchain technology for decentralized fraud prevention, expanding datasets to encompass global financial trends, and incorporating adaptive learning to counter emerging threats.},
        keywords = {Financial Fraud Detection, Deep Learning, Autoencoders, Recurrent Neural Networks (RNNs), Anomaly Detection, Transaction Analysis, Fraud Prevention, Geolocation Patterns, Machine Learning in Finance, Adaptive Learning.},
        month = {March},
        }