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 on diverse patterns within crowdsourcing laundering group. These two tasks are jointly optimized through a shared classifier, with a shared feature encoder that fuses multi-level feature embeddings to provide rich transaction semantics and potential group information. Extensive experiments on both crowdsourcing and general laundering demonstrate MCCLD's effectiveness and generalization. To the best of our knowledge, this is the first work on crowdsourcing laundering detection.

翻译：USDT作为一种与美元挂钩的稳定币，因其稳定性、匿名性和易用性已成为洗钱活动的首选工具。值得注意的是，稳定币上一种新型的洗钱形式——我们称之为众包洗钱——通过招募大量普通个体分散资金，已迅速成为重大威胁。然而，由于精细化的分工，众包洗钱交易呈现出多样化的模式和多中心结构，给检测带来了巨大挑战。本文引入交易群组作为辅助信息，提出了多任务协同众包洗钱检测（MCCLD）框架。MCCLD采用端到端的图神经网络，实现洗钱交易检测与交易群组检测任务之间的协作，提升对众包洗钱群组内多样化模式的检测性能。这两个任务通过共享分类器联合优化，并利用共享特征编码器融合多层级特征嵌入，以提供丰富的交易语义和潜在的群组信息。在众包洗钱和通用洗钱场景上的大量实验验证了MCCLD的有效性和泛化能力。据我们所知，这是首个针对众包洗钱检测的研究工作。