ML Risk Assessment Services
A business-wide ML Risk Assessment is not a decorative appendix to your AML manual.
It is the architecture of your entire AML/CFT compliance framework.
Under the UAE AML Law, Federal Decree Law No. 10 of 2025 and Cabinet Resolution No. 134 of 2025, firms are required to adopt a risk-based approach. That phrase sounds gentle. In practice, it is mathematical, documented, and very specific.
In the UAE regulatory environment, a defensible risk assessment must:
- Identify inherent ML, FT and PF risks across customers, products, services, geographies and delivery channels
- Reflect sector-specific exposure under the VARA Rulebooks, the SCA virtual asset framework, the ADGM FSRA Virtual Asset Framework and the DFSA Crypto Token Regime
- Incorporate the FATF Virtual Asset Guidance where virtual assets are involved
- Consider the IOSCO Crypto Asset Principles for market-facing firms
- Align with expectations from the Central Bank of the UAE AML CFT Rulebook
- Address guidance issued by the Ministry of Economy and Tourism and the Ministry of Justice for DNFBPs
- Distinguish clearly between inherent risk and residual risk
- Demonstrate how control effectiveness reduces exposure
- Link directly to transaction monitoring thresholds, EDD triggers and reporting decisions
Would Your ML Risk Assessment Survive Supervisory Challenge?
Let’s Get into a Technical Review Aligned to UAE AML Legislation and Supervisory Expectations
The Standards UAE Regulators Apply
In the United Arab Emirates, a Business-Wide ML/FT/PF Risk Assessment is examined with far more seriousness than many firms anticipate. Regulators are no longer satisfied with policy statements that declare a “risk-based approach.” They want to see the machinery beneath the statement.
Under the UAE AML Law, Federal Decree Law No. 10 of 2025 and Cabinet Resolution No. 134 of 2025, every regulated entity must identify, assess and mitigate money laundering, terrorist financing and proliferation financing risk in a structured and demonstrable manner. This obligation applies across financial institutions, VASPs, DNFBPs and professional services firms. The principle is universal. The depth of scrutiny depends on your sector.
For:
Firms licensed by VARA, the expectation is explicit. Your ML/FT/PF risk assessment must reflect the realities of virtual asset business models. It should incorporate the VARA Rulebooks, consider wallet structures, custody arrangements, token typologies, cross-border exposure and decentralised elements.
Entities authorised by ADGM FSRA under the FSRA Virtual Asset Framework are expected to demonstrate the same intellectual discipline. The assessment must clearly distinguish inherent and residual risk, quantify control effectiveness and show how the board oversees the framework.
Firms regulated by DIFC DFSA under the DFSA Crypto Token Regime and the DIFC Digital Assets Law must ensure that their ML/FT/PF methodology aligns with the broader AML module requirements.
For institutions supervised by the Central Bank of the UAE or the Securities and Commodities Authority, alignment with the CB UAE AML CFT Rulebook and the SCA virtual asset framework is critical.
DNFBPs supervised by the Ministry of Economy and Tourism and the Ministry of Justice face a different but equally precise expectation. Their ML risk assessments must reflect sector realities such as beneficial ownership opacity, high-value transactions, cross-border structuring and politically exposed person exposure.
Across all sectors, UAE regulators are increasingly analytical. They review methodology, challenge scoring assumptions and request supporting data. A risk assessment must demonstrate how control effectiveness reduces exposure.
Where ML Risk Assessments Collapse
In inspection rooms across the UAE, the same weaknesses surface again and again. Not reckless. Not scandalous. Just structurally unsound.
Common failures in Business-Wide ML/FT/PF Risk Assessments include:
- Imported group templates with no UAE legislative alignment.
- Inherent, residual and control effectiveness are confused or unsupported by testing.
- Risk scoring is disconnected from EDD triggers and monitoring thresholds.
- Proliferation financing is absorbed into generic ML scoring.
- Board approval recorded without demonstrable challenge.
- A risk assessment that exists on paper but does not alter operational behaviour.
When risk scoring does not change onboarding, monitoring, escalation or reporting, regulators begin to ask the uncomfortable questions.
Hope Is Not a Control
Strengthen Your ML Risk Assessment before Supervisory Scrutiny Does It for You
Six Movements of a Defensible ML Risk Assessment
Think of an effective ML/FT/PF Risk Assessment as a disciplined investigation — one that starts with curiosity and ends with a defensible, regulator-ready conclusion. Our approach is neither academic nor formulaic. It is built from years of inspection rooms, from questions that expose weakness, and from frameworks that withstand scrutiny.
We follow a structured, six-stage process that transforms disparate risk data into a coherent picture of your real exposure and how you mitigate it. Each step is designed to be audit-ready, defendable and tailored to the UAE regulatory landscape, including VARA, ADGM FSRA, DIFC DFSA and Central Bank expectations.
Risk Identification — Understand Your Reality
We gather information across your entire business, its customers, products, services, geographies, delivery channels, technologies and new digital offerings to identify where ML/FT/PF exposures may emerge. We start with your business as it operates today and map it against the risk factors that regulators expect to see.
This phase also captures emerging risks, virtual asset typologies, decentralised behaviours, and new payment flows that might not yet be fully reflected in your controls.
Inherent Risk Assessment
We assess inherent risk first, the level of ML/FT/PF exposure your business would have before any controls are applied. This is critical because it forms the benchmark against which controls must demonstrate real impact.
Inherent risk is always context-driven: a cross-border payment channel pandemic, a high-velocity digital asset wallet structure, and complex ownership chains are all catalogued and weighted.
Control Mapping and Assessment
We take identified risks and test your controls against them: customer due diligence, transaction monitoring, EDD triggers, sanctions screening, blockchain analytics integration, governance oversight, escalation processes, and more. Each control is evaluated for effectiveness, consistency and operationalisation.
Residual Risk Calculation
A risk assessment that stops at inherent risk is incomplete.
We measure residual risk, the risk that remains after controls are applied. This is where the framework meets reality. Some risks may be fully mitigated. Others may require additional controls. Some exposures may exceed your risk appetite.
This is also where regulators land, asking whether what remains has been properly assessed, documented and escalated.
Linkage to AML Controls and Monitoring
An ML risk assessment should not live in isolation.
We then ensure that the outputs, scoring, weightings, risk ratings, directly influence your AML programme: onboarding logic, monitoring thresholds, EDD triggers, reporting criteria and escalation paths.
Board Approval, Documentation & Review Cycle
We prepare a complete evidence pack, including methodology narrative, scoring logic, data tables, control testing results, risk appetite articulation and board minutes templates. The risk assessment is then presented for board approval with appropriate governance artefacts.
Importantly, this is not a one-off. We embed a review and refinement cycle so that your ML risk assessment evolves with changes in business, regulatory expectations and emerging typologies.
Let’s Outlast Assumptions
An ML risk assessment reveals more about a firm’s discipline than its policies ever could.
When regulators test methodology, they are testing governance itself. Strength lies in structure. Confidence lies in defensibility. Here is how GRC Advisory Services strengthens your ML Risk Assessment.
- ML Risk Assessments are engineered around how risk genuinely manifests inside UAE-regulated business models, not how global templates describe it.
- Clear mathematical separation between inherent and residual risk, supported by documented control effectiveness testing.
- Risk scoring calibrated to influence onboarding logic, transaction monitoring thresholds and escalation protocols.
- Proliferation financing risk is treated as a distinct exposure category in line with UAE legislative expectations.
- Latent vulnerabilities are identified early, before supervisory dialogue turns adversarial.
When the Arithmetic Is Questioned, Will It Hold?
Structured Review of Inherent Risk, Residual Risk and Control Effectiveness across UAE-Regulated Entities