Agentic Generative AI for Real-Time Fraud Detection: Integrating RAG, MLOps, and Behavioral Analytics in Financial Systems

Abstract

The growing digitalization of financial services has resulted in an explosion in terms of transaction volume, velocity and complexity which in turn has made the task of detecting and preventing fraud in real time all the more difficult. Although classic machine learning and rule-based systems have helped a great deal in mitigating fraud, they are not very effective in dealing with adaptive, multi-channel and context-sensitive fraudulent schemes. Recent developments in artificial intelligence, especially agentic generative AI, Retrieval-Augmented Generation (RAG), behavioral analytics, and explainable fraud detection systems have provided new opportunities to create intelligent, adaptive, and explainable fraud detection systems. This review has talked about how the approaches of detecting frauds has evolved and has come up with an integrated architecture that will integrate these emerging technologies into a single architecture. It is discussed that agentic AI systems, whose behavioral understanding and contextualizing grounded on retrieval can be constrained by a category, but also dynamically utilized to make decisions. In addition, continuous monitoring, retraining and governance are also provided by MLOps in a manner that these systems can be dynamic. Although these have been advanced there are still a number of research challenges such as explainability, preservation of privacy, adversarial robustness and interoperability of systems. These questions are crucial in the effective and secure application of AI-based fraud detection to the real finance. The paper will be valuable in that it will bring together the existing knowledge, develops a theoretical model and suggest major areas of future research that will be able to inform academic research and practice in this industry.