Pass the ACAMS AML Certifications CCAS Questions and answers with CertsForce

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.

Hash rate measures computational power in Proof-of-Work blockchains; higher hash rates mean more secure networks against 51% attacks.

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

Private blockchains are controlled by a single organization with full access restrictions. This model is often used for internal record-keeping but lacks the decentralized trust of public chains.

The risk-based approach requires tailoring AML/CFT controls to the level of assessed risk, enhancing due diligence for higher-risk customers.

Ongoing monitoring is the continuous analysis of customer activity to detect unusual or suspicious patterns over time.

Smart contracts execute predetermined conditions automatically on blockchain, enabling decentralized applications and services.

Misconfigured or poorly designed smart contracts can enable rug pull scams, where developers create fraudulent decentralized finance (DeFi) projects or tokens and then withdraw liquidity or funds abruptly, leaving investors with worthless assets.

Phishing (A) and SIM attacks (B) relate to social engineering and telecom fraud, respectively, and ransomware (D) is malware demanding payment. Rug pulls specifically exploit smart contract vulnerabilities.

The DFSA and AML thematic reviews on crypto highlight rug pull scams as a key operational and financial crime risk linked to smart contract vulnerabilities.

National risk assessments conducted across various jurisdictions consistently report that money laundering risks related to cryptoasset activities are rising. The growing adoption, complexity, and use of cryptoassets for illicit purposes contribute to elevated risk levels.

While geography (B), awareness (C), and digital banking adoption (D) can influence risk factors, the overarching trend is an increase in ML risks tied to cryptoassets.

This conclusion is supported by FATF’s global guidance and numerous national risk assessment reports reviewed by the DFSA and related authorities

Proof-of-Stake (PoS) replaces the energy-intensive mining process of Proof-of-Work by allowing validators to secure the network based on the amount of cryptocurrency they “stake” as collateral. Validators are rewarded for correctly validating transactions and risk losing their stake if they act dishonestly. Regulatory AML/CFT programs must consider validator concentration risks and the jurisdictional exposure of validators in PoS systems.