International Journal of Scientific Research and Engineering Development

The rapid growth of digital financial transactions has significantly increased the risk and sophistication of fraudulent activities, necessitating advanced and adaptive security mechanisms. This paper proposes an Artificial Intelligence (AI)- driven fraud detection system designed to enhance the security of financial transactions in real time. The system leverages machine learning algorithms, including supervised and unsupervised models, to analyse large volumes of transactional data and identify anomalous patterns indicative of fraud. Key features such as behavioral analysis, transaction profiling, and adaptive learning enable the system to continuously improve detection accuracy while minimizing false positives. Additionally, the integration of deep learning techniques allows for the identification of complex and previously unseen fraud patterns. Experimental results demonstrate that the proposed model outperforms traditional rule-based systems in terms of detection rate, scalability, and response time. The study highlights the potential of AI-based solutions in strengthening financial security infrastructures and ensuring safe, reliable digital transactions in an increasingly interconnected economy.

Nidhi S. Mandwe,"AI- Driven Fraud Detection System for Secure Financial Transactions" International Journal of Scientific Research and Engineering Development, V9(3): Page(851-854) May-June 2026. ISSN: 2581-7175. www.ijsred.com. Published by Scientific and Academic Research Publishing.