Digital payments have revolutionized how we handle money, offering unparalleled convenience. But this ease of use has also attracted scammers looking to exploit vulnerabilities. Traditional methods of scam detection, like machine learning models relying on structured data and pre-defined rules, often struggle to keep up with scammers' evolving tactics. This is where Large Language Models (LLMs) like Google’s Gemini offer a powerful new approach. Researchers are exploring how LLMs can analyze the nuances of digital transactions to identify fraudulent activity. Imagine an AI that can understand not just the numbers in a transaction, but also the context—the descriptions, messages, and even emojis—to flag suspicious patterns. This research, focusing on the Unified Payments Interface (UPI) system in India and Google Pay as a case study, found that Gemini Ultra achieved a 93.33% accuracy in classifying scams. What makes LLMs particularly exciting is their potential to act as digital assistants for human reviewers. By processing vast amounts of data and learning from expert knowledge, these AI assistants can provide reviewers with the top reasons why a transaction might be fraudulent, streamlining the review process and improving accuracy. Remarkably, the LLM even identified 32% new accurate reasons for suspected scams that human reviewers had overlooked. This suggests that AI can not only augment human capabilities but also uncover hidden patterns that might otherwise go unnoticed. While this research focuses on GPay in India, the implications are global. This LLM-powered approach can be adapted to various payment platforms and even extended to other domains like e-commerce and B2B payments. The ability of LLMs to understand natural language makes them a versatile tool in the ongoing fight against fraud. However, challenges remain, such as the need to constantly update the models with the latest scam tactics and the computational costs associated with LLM inference. As AI technology evolves, we can expect even more sophisticated solutions that will help create a safer and more secure digital financial landscape.