Global illicit fund flows exceed an estimated $3.1 trillion annually, with stablecoins emerging as a preferred laundering medium due to their liquidity. While decentralized protocols increasingly adopt zero-knowledge proofs to obfuscate transaction graphs, centralized stablecoins remain critical "transparent choke points" for compliance. Leveraging this persistent visibility, this study analyzes an Ethereum dataset and uses behavioral features to develop a robust AML framework. Our findings demonstrate that domain-informed tree ensemble models achieve higher Macro-F1 score, significantly outperforming graph neural networks, which struggle with the increasing fragmentation of transaction networks. The model's interpretability goes beyond binary detection, successfully dissecting distinct typologies: it differentiates the complex, high-velocity dispersion of cybercrime syndicates from the constrained, static footprints left by sanctioned entities. This framework aligns with the industry shift toward deterministic verification, satisfying the auditability and compliance expectations under regulations such as the EU's MiCA and the U.S. GENIUS Act while minimizing unjustified asset freezes. By automating high-precision detection, we propose an approach that effectively raises the economic cost of financial misconduct without stifling innovation.

翻译：全球非法资金流动每年估计超过3.1万亿美元，稳定币因其流动性正成为日益流行的洗钱媒介。尽管去中心化协议越来越多地采用零知识证明来模糊交易图谱，中心化稳定币仍是合规监管关键的"透明阻塞点"。本研究利用这一持续可见性，通过分析以太坊数据集并采用行为特征，构建了一个稳健的反洗钱框架。我们的研究结果表明，融入领域知识的树集成模型获得了更高的宏观F1分数，显著优于图神经网络——后者在处理日益碎片化的交易网络时表现不佳。该模型的可解释性超越了二元检测，成功解析出不同的行为模式：它能区分网络犯罪集团复杂、高速的资金分散模式与被制裁实体受限、静态的交易特征。本框架顺应行业向确定性验证转型的趋势，既满足欧盟《加密资产市场法规》（MiCA）和美国《GENIUS法案》等监管要求下的可审计性与合规预期，又最大限度减少不当资产冻结。通过实现高精度自动化检测，我们提出的方法能有效提高金融不当行为的经济成本，同时避免抑制创新活力。