Over $3.1 trillion in illicit money moves through the global financial system yearly, and much of it now uses stablecoins, which launderers prefer for their liquidity. Decentralized protocols increasingly hide transaction patterns with zero-knowledge proofs, while centralized stablecoins remain visible. To preserve the ability to convert to fiat, they must maintain an auditable record, which makes them natural points of compliance oversight. Using this visibility, we create an Ethereum dataset of Tether USD (USDT) and USD Coin (USDC) wallet transfers and establish a baseline for behavioral anti-money-laundering (AML) detection. We compare linear models, tree ensembles, deep networks,  and graph neural networks. Tree ensembles achieve the best Macro-F1 score, while the graph neural networks lose accuracy as the transaction network fragments. The models separate distinct typologies rather than only flagging suspicion: the fast, dispersed movement of cybercrime wallets is distinguished from the constrained, static footprint of sanctioned or frozen wallets. These results align with the industry shift toward deterministic verification and address the auditability and compliance requirements now forming under regulations such as the EU’s Markets in Crypto-Assets (MiCA) and the U.S. Guiding and Establishing National Innovation for U.S. Stablecoins Act (GENIUS Act), while limiting unjustified  asset freezes. A high-precision behavioral classification of suspicious wallets raises the economic cost of financial misconduct and informs compliance practice under emerging stablecoin rules.