Detection of Crowdsourcing Cryptocurrency Laundering via Multi-Task Collaboration
Guang Li, Litong Sun, Jieying Zhou, and Weigang Wu

TL;DR
This paper presents MCCLD, a novel graph neural network framework that detects crowdsourcing cryptocurrency laundering by jointly analyzing laundering transactions and transaction groups, addressing diverse laundering patterns.
Contribution
Introduces the first method specifically designed for crowdsourcing laundering detection using multi-task learning and graph neural networks.
Findings
MCCLD outperforms baseline methods in detection accuracy.
Joint optimization improves detection of diverse laundering patterns.
Framework demonstrates strong generalization across datasets.
Abstract
USDT, a stablecoin pegged to dollar, has become a preferred choice for money laundering due to its stability, anonymity, and ease of use. Notably, a new form of money laundering on stablecoins -- we refer to as crowdsourcing laundering -- disperses funds through recruiting a large number of ordinary individuals, and has rapidly emerged as a significant threat. However, due to the refined division of labor, crowdsourcing laundering transactions exhibit diverse patterns and a polycentric structure, posing significant challenges for detection. In this paper, we introduce transaction group as auxiliary information, and propose the Multi-Task Collaborative Crowdsourcing Laundering Detection (MCCLD) framework. MCCLD employs an end-to-end graph neural network to realize collaboration between laundering transaction detection and transaction group detection tasks, enhancing detection performance…
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Taxonomy
TopicsCrime, Illicit Activities, and Governance · Blockchain Technology Applications and Security · Auction Theory and Applications
