RiskTagger: An LLM-based Agent for Automatic Annotation of Web3 Crypto Money Laundering Behaviors

• URL: http://arxiv.org/abs/2510.17848v1
• Date: Sun, 12 Oct 2025 08:54:28 GMT
• Title: RiskTagger: An LLM-based Agent for Automatic Annotation of Web3 Crypto Money Laundering Behaviors
• Authors: Dan Lin, Yanli Ding, Weipeng Zou, Jiachi Chen, Xiapu Luo, Jiajing Wu, Zibin Zheng,
• Abstract summary: RiskTagger is a large-language-model-based agent for the automatic annotation of crypto laundering behaviors in Web3.<n>RiskTagger is designed to replace or complement human annotators by addressing three key challenges: extracting clues from complex unstructured reports, reasoning over multichain transaction paths, and producing auditor-friendly explanations.
• Score: 65.80108147440863
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: While the rapid growth of Web3 has driven the development of decentralized finance, user anonymity and cross-chain asset flows make on-chain laundering behaviors more covert and complex. In this context, constructing high-quality anti-money laundering(AML) datasets has become essential for risk-control systems and on-chain forensic analysis, yet current practices still rely heavily on manual efforts with limited efficiency and coverage. In this paper, we introduce RiskTagger, a large-language-model-based agent for the automatic annotation of crypto laundering behaviors in Web3. RiskTagger is designed to replace or complement human annotators by addressing three key challenges: extracting clues from complex unstructured reports, reasoning over multichain transaction paths, and producing auditor-friendly explanations. RiskTagger implements an end-to-end multi-module agent, integrating a key-clue extractor, a multichain fetcher with a laundering-behavior reasoner, and a data explainer, forming a data annotation pipeline. Experiments on the real case Bybit Hack (with the highest stolen asset value) demonstrate that RiskTagger achieves 100% accuracy in clue extraction, 84.1% consistency with expert judgment, and 90% coverage in explanation generation. Overall, RiskTagger automates laundering behavior annotation while improving transparency and scalability in AML research.

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