A computer implemented method for performing artificial intelligence-based cryptocurrency regulatory analysis comprising the steps of:
- acquiring blockchain transaction data, block headers, and smart contract metadata from a plurality of distributed blockchain networks by interfacing with corresponding blockchain nodes;
- normalizing the acquired blockchain data into a unified data schema by performing timestamp alignment, value normalization, and deduplication of cryptographic address entries;
- constructing a transaction graph representation in which each node represents a distinct entity selected from wallet addresses, decentralized exchanges, or smart contracts, and each edge represents a transactional interaction characterized by transfer value, token identifier, and recurrence frequency;
- retrieving a set of jurisdiction-specific regulatory rule graphs from a regulatory knowledge base, wherein said rule graphs encode compliance obligations and dependencies between legal concepts in a machine-readable format;
- processing the constructed transaction graph using a trained artificial intelligence model to generate inferred compliance state for each entity and transaction;
- generating a compliance report containing detected violations, supporting evidence, and an explainable reasoning trace, and anchoring said compliance report cryptographically to a blockchain ledger for verifiable auditability, wherein constructing the transaction graph further comprises executing an entity disambiguation process in which addresses exhibiting fragmented or intentionally obfuscated identity patterns are analyzed using long-term behavioral signature extraction based on features including transaction burst intervals, smart contract interaction recurrence, token ecosystem diversity, and exchange endpoint geolocation inference derived from peer-to-peer latency analysis, and wherein the method dynamically merges nodes representing potentially identical beneficial owners by computing statistical similarity thresholds using unsupervised clustering on said behavioral signatures while concurrently preserving an uncertainty factor within each merged node that is propagated as a weighted attribute into subsequent artificial intelligence inference computations, thereby enabling continuous risk scoring adaptation based on evolving anonymization behaviors encountered across blockchain networks, and wherein processing the transaction graph using the artificial intelligence model comprises performing trust-constrained relational message propagation between nodes wherein each edge is continuously evaluated for integrity based on anomaly detection derived from transaction path irregularity, mixer-induced entropy spike measurements, and detection of excessive transaction subdivision relative to typical liquidity flow patterns, and wherein propagation attenuation coefficients are computed in real time such that trust-compromised edges exert progressively reduced influence over neighborhood-based relational embeddings produced by the graph neural model, thereby enforcing a technically controlled degradation of risk signal amplification from nodes implicated in potential obfuscation or criminal evasion strategies.