The most critical information enabling FIUs to address anonymity risks is data linking a virtual address to the real-world identity of its owner. Without this association, blockchain addresses remain pseudonymous, hindering effective AML efforts.
While transaction timing (A), identity of receiver (B), and transaction-to-address mapping (C) are useful, ownership linkage (D) is essential to break anonymity.
FATF and DFSA guidance prioritize obtaining ownership information through KYC and intelligence sharing.

Unhosted wallets are self-custody wallets controlled directly by the user without third-party oversight, posing higher anonymity and AML risks.

Chain hopping involves moving between blockchains to make tracing harder, often exploiting regulatory gaps.

Machine learning (ML) and artificial intelligence (AI) are applied within compliance frameworks to enhance the efficiency of monitoring and detection processes and to allow skilled compliance resources to focus on higher-value activities such as complex investigations and strategic decision-making. ML/AI tools can process vast amounts of transaction data to identify suspicious patterns faster than manual processes.
They do not reduce the fundamental requirement for risk assessment (A) nor are they intended primarily to reduce headcount (C), but rather to optimize resource allocation.
AML and DFSA guidance emphasize leveraging technology to improve the effectiveness and efficiency of AML controls while maintaining robust risk management.

FATF Recommendation 20 mandates that STRs be filed whenever there is suspicion or reasonable grounds to suspect criminal proceeds, regardless of the transaction value. This is mirrored in DFSA and FSRA AML regulations, ensuring that the reporting obligation is triggered by suspicion, not just thresholds.