Cryptocurrency has become a hotbed for scams, leaving victims feeling lost and helpless. From elaborate investment schemes to romance baiting, criminals exploit the digital landscape to steal millions. But what if we could fight back with the same technology they use? New research explores how Large Language Models (LLMs), the brains behind AI chatbots, can be used to automatically classify scam reports, sorting through thousands of cases to identify the bad actors and their tactics. Researchers analyzed nearly 300,000 cryptocurrency abuse reports from platforms like BitcoinAbuse and the Better Business Bureau's Scam Tracker, creating a detailed taxonomy of 19 common crypto scams. They then designed an LLM-powered system that analyzes the text of abuse reports, matching them to the correct scam category. The results are impressive: this AI-driven approach achieves a remarkable 89% accuracy in correctly classifying scam reports – even those it's never seen before. This outperforms traditional machine-learning methods, which often struggle to adapt to new variations of scams. This innovative tool has significant implications for fighting crypto crime. By automating scam classification, investigators can quickly identify trends, track stolen funds, and even predict future attacks. It allows organizations like the BBB to analyze financial losses across specific scam types, giving them valuable data to warn consumers and help prevent future victimization. The research also exposes a critical flaw in existing abuse reporting: many reports are inaccurate, either due to user error or intentional spam. This highlights the need for better education for victims, urging them to focus on reporting the addresses they directly interacted with, rather than trying to track funds themselves, a task best left to experts. While the cost of using powerful LLMs like GPT-4 remains a challenge, the potential for this technology to revolutionize the fight against crypto scams is immense. As AI technology evolves and becomes more affordable, this automated classification approach promises to be a powerful weapon in the ongoing battle against digital deception.