Dynamic Anti-Money Laundering Risk Assessment and Decentralized Governance Compliance Auditing in the Web3 Ecosystem

Abstract

The rapid proliferation of Web3 architectures, while fostering financial innovation has paradoxically exacerbated regulatory vulnerabilities through complex cross-chain bridges and obfuscated decentralized autonomous organization fiscal operations. This paper establishes a dual-layered framework designed to synthesize dynamic anti-money laundering risk assessment with decentralized compliance auditing. By utilizing graph neural networks alongside temporal transaction analysis, the proposed model attempts to capture non-linear, evolving financial anomalies across disparate smart contracts; however, data asymmetry and the inherent opacity of zero-knowledge proofs introduce significant academic uncertainty regarding complete system predictability. Methodologically, embedding programmable compliance into DAO voting mechanisms and treasury protocols reveals a delicate friction between decentralization ideals and regulatory imperatives, a challenge further complicated by potential algorithmic biases during historical data calibration. Preliminary simulations indicate that while this framework possibly mitigates malicious treasury takeovers to some extent, the fluid nature of global regulatory standards means that the boundary between member privacy and effective enforcement remains highly contingent. Ultimately, this research shifts the paradigm from static post-hoc monitoring toward continuous, automated resilience, though further empirical research is needed to validate its scalability across heterogeneous blockchain layers.