GEN-PAY: Leveraging Generative AI and RAG for Securing Payment Gateways with Fraud Detection Systemsustomer Experience with Progressive Web Apps

In an era marked by rapid digital transformation, securing payment gateways against fraudulent activities is of paramount importance. GEN-PAY introduces a novel approach by integrating generative artificial intelligence with Retrieval-Augmented Generation (RAG) techniques to create an adaptive fraud detection system. This framework leverages generative AI to simulate diverse fraudulent scenarios, thereby enriching training datasets and enabling the detection of subtle, emerging patterns that conventional systems might overlook. Simultaneously, the RAG component retrieves and integrates contextual historical data, enhancing the system’s capability to distinguish legitimate transactions from suspicious ones. The combined methodology elevates both the precision and scalability of fraud detection in dynamic payment environments. Empirical studies demonstrate that GEN-PAY reduces false positives while significantly improving detection rates, thus fostering a secure ecosystem for digital financial transactions. The architecture is designed to evolve continuously with emerging cyber threats, ensuring ongoing protection without compromising transaction efficiency. This paper outlines the integration of generative AI and RAG, detailing the underlying algorithms, system architecture, and the challenges encountered during real-world implementation.