The increasing sophistication of AI-generated misinformation poses a significant challenge, particularly in Vietnamese digital environments, where scam tactics are evolving rapidly. In response, this paper presents FraudTrace, an automated, multi-agent verification system designed to detect and explain misinformation and online fraud. Our key contributions include (1) the construction of a diverse and realistic dataset comprising 4,221 labeled samples, (2) the introduction of a URC (Understandable Response Clarity) metric for evaluating model explainability, and (3) the deployment of a modular, role-based multi-agent architecture integrating fine-tuned Large Language Models (LLMs), Retrieval-Augmented Generation (RAG), Google Search APIs, and scam detection frameworks. Experimental results indicate that fine-tuned LLMs significantly outperform zero-shot baselines, with the best-performing model achieving over 91% in both accuracy and F1-score, while avoiding invalid responses entirely. FraudTrace not only classifies input information effectively, but also maps flagged outputs to known scam patterns using a Tactics, Techniques, and Procedures (TTP)-based reasoning approach, offering transparent and trustworthy feedback. This research establishes a strong foundation for scalable, real-time misinformation verification tailored to the Vietnamese context.