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@article{206722,
        author = {Karthik and Archana K and Deepa Gopal Devadiga and Arun Badiger and Prof. Aishwariya P},
        title = {AI-Powered Fraud Detection System},
        journal = {International Journal of Innovative Research in Technology},
        year = {2026},
        volume = {13},
        number = {no},
        pages = {257-263},
        issn = {2349-6002},
        url = {https://ijirt.org/article?manuscript=206722},
        abstract = {In an era where digital transactions and online interactions are rapidly increasing, the need for secure and intelligent fraud prevention systems has become essential. The integration of Artificial Intelligence into fraud detection offers a transformative solution to identify suspicious activities, reduce financial losses, and enhance trust in digital platforms. This paper presents a robust and efficient AI-Powered Fraud Detection System designed to analyze transaction patterns and detect fraudulent behavior in real time. Leveraging the capabilities of Machine Learning, Deep Learning, and data analytics, the system minimizes manual intervention, improves detection accuracy, and ensures fast response to threats. A secure web-based framework using Python and Flask integrates trained models to automate critical operations including transaction monitoring, anomaly detection, risk scoring, and alert generation. A user-friendly interface with role-based access control (RBAC) allows customers, administrators, and security teams to securely monitor and manage fraud cases. Evaluation results demonstrate near-real-time fraud detection, high prediction accuracy, reduced false positives, and resilience against identity theft, unauthorized transactions, and data manipulation. The system represents a scalable, intelligent, and secure solution for modernizing fraud prevention in the digital era.},
        keywords = {.},
        month = {July},
        }