%Aigaion2 BibTeX export van HES SO Valais Publications
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@ARTICLE{,
    author = {Trerotola, Mario and Parente, Mimmo and Calvaresi, Davide},
  keywords = {compliance automation, crypto-asset markets, Explainable AI, fraud detection, Large Language Models, markets in Crypto- Assets Regulation (MiCAR), Multi-Agent Systems, off-chain due diligence, regulatory technology (RegTech)},
     month = mar,
     title = {A Hybrid Multi-Agent System for Early Scam Detection in Crypto-Assets},
   journal = {Applied Sciences - MDPI},
      year = {2026},
  abstract = {The rapid expansion of crypto-asset markets and the introduction of the Markets in Crypto-Assets Regulation (MiCAR) pose novel supervisory challenges. Existing blockchain intelligence platforms focus predominantly on on-chain surveillance, leaving gaps in off-chain documentary due diligence automation. This paper presents a Multi-Agent System (MAS) integrating Large Language Model (LLM) capabilities with rule-based compliance frameworks. The architecture comprises seven specialized agents: a Coordinator Agent for orchestration; data acquisition agents (Searcher, Crawler); three parallel analytical agents—Heuristic Agent (LLM-powered qualitative risk assessment), Compliance Agent (hybrid-AI MiCAR asset classification and regulatory requirement verification), and On-Chain Agent (machine learning-based fraud detection); and a Reconciliator Agent synthesizing findings into unified alerts. Component-level empirical validation on 150 projects indicates 95\% output reproducibility (identical alert tier and score deviation ≤0.05 across five reruns) and 210 s mean latency, providing proof-of-concept evidence for the integrated pipeline. A pilot user evaluation (six researchers/master students and two experts from regulatory authorities) provides preliminary usability evidence and surfaces domain-specific feedback from regulatory-authority experts. The architecture advances proactive regulatory technology by enabling scalable analysis, combining off-chain documentary evidence with
on-chain forensics.}
}