CAMS Questions Bank

Artificial intelligence and machine learning technologies support the risk-based approach (RBA) to AML compliance by enhancing an institution’s ability to identify, assess, and prioritize financial crime risks. Regulatory guidance emphasizes that these technologies should augment—not replace—human judgment.

One key benefit is advanced customer risk assessment. AI and ML can synthesize large volumes of customer data, including background information, transaction behavior, and external risk indicators, allowing institutions to develop more accurate and dynamic risk profiles.

AI and ML also enable the identification of complex networks among seemingly unrelated clients. Through network analytics and pattern recognition, these tools can uncover hidden relationships used in layering and structuring activities.

Additionally, AI-driven systems excel at detecting complex money laundering patterns within transaction data that may not trigger traditional rule-based alerts, improving effectiveness and efficiency.

Automatically generating SARs or adapting thresholds without human oversight is inconsistent with regulatory expectations. Human review and governance remain essential components of AML compliance.

Residual risk represents the remaining level of risk after considering the effectiveness of controls. Under a risk-based approach, strong controls reduce risk, while weak or ineffective controls increase it.

In this scenario, the inherent risk is assessed as moderate, meaning the underlying exposure is neither low nor extreme. However, the controls are evaluated as less than satisfactory, indicating that existing measures are not adequately mitigating the identified risks.

When controls are weak, residual risk increases above the inherent risk level, as vulnerabilities remain unaddressed. Regulators expect financial institutions to escalate residual risk ratings and implement remediation plans in such cases.

Therefore, the most likely residual risk conclusion is high, reflecting the combination of moderate inherent risk and ineffective controls. This outcome aligns with AML best practice and regulatory expectations.

The First Line of Defense (1LoD) consists of customer-facing business units (e.g., relationship managers, front-office staff, and operational teams). Their primary responsibility in financial crime risk management is to implement AML/CFT controls as part of daily operations. This includes Collecting complete customer information.

The First Line of Defense is responsible for conducting Know Your Customer (KYC) and Customer Due Diligence (CDD) during onboarding and throughout the customer relationship. Ensuring that all relevant customer details (e.g., identity, business purpose, ownership structure) are accurately collected and documented is crucial for mitigating financial crime risks.

Effective sanctions screening depends heavily on strong list management governance, as emphasized in sanctions compliance guidance from regulators such as OFAC and FATF.

First, restricting list modification to authorized individuals is critical. This ensures proper governance, auditability, and control over updates, reducing the risk of errors, unauthorized changes, or manipulation of screening outcomes.

Second, sanctions and watchlists may be sourced internally or from reputable third-party vendors. Many financial institutions supplement regulatory lists with internal watchlists, law enforcement requests, or commercially curated databases to enhance detection capabilities. This flexibility supports a risk-based approach.

Using only regulatory lists may be insufficient for identifying broader sanctions evasion risks, while applying all lists in all jurisdictions may lead to legal conflicts and over-screening. Institutions must tailor list usage based on jurisdictional applicability and legal requirements.

Together, controlled access and flexible list sourcing form the foundation of effective sanctions list management.