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

arXiv:2602.17842 · cs.CR, cs.CE · Submitted 2026-02-19 · Read on arXiv

cs.CR, cs.CE

Submitted: 2026-02-19

Updated: 2026-07-14

Journal ref: Blockchain 4(2) (2026) 0007, 1-7

DOI: 10.55092/blockchain20260007

Code: https://github.com/finos-labs/dtcch-2025-OpenAML

License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/

Terminology

• AUROC — 16× in this paper · explained in 2 episodes
• F1 Score — 14× in this paper · explained in 3 episodes
• Anti-Money Laundering — 10× in this paper
• Actor — 9× in this paper · explained in 3 episodes
• Benchmark — 9× in this paper · explained in 2 episodes
• Accuracy — 8× in this paper · explained in 1 episode
• Random Forest — 8× in this paper
• Feature Engineering — 6× in this paper · explained in 3 episodes
• Interpretability — 6× in this paper · explained in 15 episodes
• Robustness — 6× in this paper · explained in 5 episodes
• Sparsity — 6× in this paper · explained in 4 episodes
• Deep Neural Network — 5× in this paper
• Ablation — 4× in this paper · explained in 1 episode
• Embeddings — 4× in this paper · explained in 2 episodes
• Regularization — 4× in this paper · explained in 3 episodes
• Aggregation — 3× in this paper · explained in 1 episode
• GNNs — 3× in this paper · explained in 2 episodes
• Generalization — 3× in this paper · explained in 13 episodes
• Provenance — 3× in this paper · explained in 4 episodes
• Verification — 3× in this paper · explained in 2 episodes
• Ablation study — 2× in this paper · explained in 1 episode
• Feature Extraction — 2× in this paper · explained in 2 episodes
• Model-Agnostic — 2× in this paper · explained in 2 episodes
• SHAP values — 2× in this paper · explained in 2 episodes
• Velocity Dispersion — 2× in this paper · explained in 2 episodes
• AUC — 1× in this paper · explained in 2 episodes
• Activation Function — 1× in this paper · explained in 1 episode
• Adam — 1× in this paper · explained in 2 episodes
• Adaptability — 1× in this paper · explained in 1 episode
• Adaptation — 1× in this paper · explained in 1 episode
• Adjacency Matrix — 1× in this paper · explained in 1 episode
• Aggregation Function — 1× in this paper · explained in 1 episode
• Area Under the Receiver Operating Characteristic — 1× in this paper
• Benchmarking — 1× in this paper · explained in 3 episodes
• Clustering — 1× in this paper · explained in 2 episodes
• Computational overhead — 1× in this paper · explained in 2 episodes
• Consistency — 1× in this paper · explained in 3 episodes
• Core — 1× in this paper · explained in 2 episodes
• Ensemble Learning — 1× in this paper · explained in 2 episodes
• Graph Construction — 1× in this paper · explained in 3 episodes
• Graph Neural Networks (GNNs) — 1× in this paper · explained in 7 episodes
• Long Short-Term Memory — 1× in this paper
• Loss Function — 1× in this paper · explained in 2 episodes
• Markets in Crypto-Assets — 1× in this paper
• Objective Function — 1× in this paper · explained in 2 episodes
• Office of Foreign Assets Control — 1× in this paper
• Representation Learning — 1× in this paper · explained in 2 episodes
• Synthetic Data Generation — 1× in this paper · explained in 5 episodes
• Trace — 1× in this paper · explained in 2 episodes
• Zero-Knowledge Proofs (ZKPs) — 1× in this paper · explained in 3 episodes

Sources

• Inspection-L: Self-Supervised GNN Node Embeddings for Money Laundering Detection in Bitcoin
• Temporal Graph Networks for Deep Learning on Dynamic Graphs
• Anti-Money Laundering in Bitcoin: Experimenting with Graph Convolutional Networks for Financial Forensics

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