AMLGuard: Semantic-Aware Framework for Tracking Crypto Money Laundering Using LLMs
A new framework called AMLGuard is introduced to improve anti-money laundering (AML) in decentralized finance (DeFi) by combining static rule-based analysis with retrieval-augmented large language model (LLM) reasoning. AMLGuard infers high-level transaction semantics to track illicit fund flows, including across blockchains, and was evaluated on 82 real-world laundering cases involving over $1 billion in assets. The system demonstrated high precision and recall in reconstructing illicit fund-flow topologies for both single-chain and cross-chain transactions.
Why it matters: This work represents a notable advance in applying LLMs to financial security, offering improved tools for tracking and analyzing complex illicit cryptocurrency flows.
Full story at: arXiv Cryptography and Security ↗