Leveraging Large Language Models (LLMs), Retrieval-Augmented Generation (RAG), and Agentic AI for Enhanced Healthcare Fraud Dete

This study compares rule-based and standalone machine learning methods to Large Language Models (LLMs), Retrieval-Augmented Generation (RAG) systems, and agentic AI frameworks for healthcare insurance fraud detection. A comprehensive literature analysis and analytical synthesis examined 27 2020\u20132025 peer-reviewed sources, technical documentation, and actual case studies. Different technologies were compared for precision, recall, F1-score, and processing efficiency. The analysis included standard detection approaches, AI technologies, and multi-modal frameworks. RAG-enhanced systems outperformed AI-based methods with 92% detection accuracy and USD 48 million annual savings in Saudi Arabia. Multi-agent systems reduced false positives by 30% and arrived at claim processing latencies of 150 milliseconds, while LLMs improved detection rates by ~75% over statistical models, consistent with reported 85\u201395% accuracy. Blockchain-integrated approaches had 97.20% accuracy, 97.50% recall, and 96.00% F1-scores. Combining LLM-RAG-agentic architectures can identify intricate fraud schemes that traditional systems missed by examining structured billing data and unstructured claim narratives. LLMs, RAG systems, and agentic AI work together to detect healthcare fraud more accurately, quickly, and explicably than traditional or individual AI methods.  Data privacy, algorithmic bias, computing costs, and regulatory compliance must be addressed for successful adoption.  Next-generation fraud detection systems that balance operational efficiency with ethical and regulatory standards in healthcare insurance can be built on the findings.