Detection of Crowdsourcing Cryptocurrency Laundering via Multi-Task Collaboration

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 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.