Anti-Money Laundering & Cyber Fraud Database

The Entity Resolution & Attribution Framework enables AIAML to link pseudonymous blockchain wallets to real-world entities, creating a detailed mapping of illicit financial networks. This module uses advanced algorithms to cluster wallets with similar transactional behaviors and identify potential real-world actors through cross-source correlations.

AI-Driven Wallet Clustering: AI models automatically cluster wallet addresses based on behavioral patterns, such as the timing of transactions, amount patterns, and interaction with known addresses. This allows for the identification of potential networks and key players in a laundering operation. Human Verification: Although AI does a significant portion of the heavy lifting in clustering and analysis, human analysts are crucial for verifying whether a particular cluster corresponds to a real-world entity.

In cases where the AI is unsure (e.g., when addresses appear to be proxies or used by multiple individuals), human verification is required. Analysts may cross-check with intelligence databases, leaked KYC data, and external sources to refine entity attribution, ensuring the AI’s conclusions are accurate. This collaborative process between AI and human experts improves the system's ability to correctly identify and map complex laundering schemes, ensuring that high-risk entities are accurately flagged for investigation.