Indirect exposure occurs when funds pass through one or more intermediary wallets before reaching a known illicit destination. This requires enhanced monitoring to capture risks that are not directly linked but are part of the transaction chain.

Attribution data involves linking blockchain addresses to real-world entities, which is derived from a combination of public sources (blockchain explorers, public databases) and non-public sources (law enforcement databases, commercial analytics, exchange records).
Relying solely on blockchain data (C) or darknet sources (D) is insufficient. Business records (A) are part of non-public sources.
DFSA and FATF AML guidance underscore the multi-source approach for effective forensic attribution.

Enhanced international cooperation in VA crime investigations is most effective when jurisdictions designate and empower Financial Intelligence Units (FIUs) that can share intelligence, coordinate cross-border investigations, and liaise with counterparts internationally.
While FATF membership (A) facilitates cooperation, the operational hub for investigations is the FIU. Establishing new agencies (B) or smart contracts for sharing (C) are not established or effective methods.
DFSA guidance and FATF recommendations stress the central role of FIUs in enhancing cooperation and intelligence sharing for AML/CFT, including virtual assets.

Fuzzy matches require further review to confirm whether the match is a true hit or a false positive, ensuring compliance accuracy.

Decentralized exchanges lack a central counterparty responsible for AML compliance, making it difficult to enforce KYC/CDD, monitor transactions, or implement controls. This structural characteristic increases inherent AML risk compared to centralized exchanges, which have accountable operators.
Transaction cost (A), validator nodes (B), or asset types (D) are less impactful in the compliance risk rating.