StableAML: Machine Learning for Behavioral Wallet Detection in Stablecoin Anti-Money Laundering on Ethereum

TL;DR

This paper presents StableAML, a machine learning framework utilizing behavioral features and tree ensemble models to detect illicit stablecoin transactions on Ethereum, outperforming graph neural networks and aiding compliance.

Contribution

It introduces a novel AML detection approach based on behavioral features and interpretable models, addressing the fragmentation challenge faced by graph neural networks.

Findings

Tree ensemble models achieve higher Macro-F1 scores than GNNs.

Behavioral features enable detailed typology differentiation.

Framework supports regulatory compliance and reduces illicit activity.

Abstract

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.