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

2026-02-19

AI Summary

This study addresses the money laundering risks inherent in Ethereum stablecoin transactions by proposing a domain-aware tree ensemble model that integrates expert knowledge to construct an efficient and interpretable anti–money laundering detection framework. Leveraging behavioral characteristics of stablecoin transaction patterns, the approach effectively distinguishes operational modes between cybercriminal syndicates and sanctioned entities. It significantly outperforms graph neural networks in Macro-F1 score while achieving high-precision identification of suspicious wallets with low false-positive rates. Designed with regulatory compliance in mind, the model supports auditability under frameworks such as MiCA and GENIUS, offering a practical tool for enhancing on-chain financial security.

Technology Category

• Machine Learning: Efficient ML / Green AI
• Philosophy and Ethics of AI: Safety, Robustness & Trustworthiness
• Natural Language Processing: Ethics — Bias, Fairness, Transparency & Privacy

Application Category

• Economics, Online Markets and Human Computation: Economic aspects of blockchain and cryptocurrencies
• User Modeling, Personalization and Recommendation: Accountability, Transparency, and Ethics for personalization
• Graph Algorithms and Modeling for the Web: Foundation models and LLMs for Web-related graphs

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.

Problem

• anti-money laundering
• stablecoin
• behavioral wallet detection
• Ethereum
• financial crime

Innovation

• behavioral wallet detection
• tree ensemble models
• stablecoin AML
• transaction network fragmentation
• interpretable machine learning