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Timeline for AMLA'S Direct Supervision and What This Means for RMC Wise Clients

Dec 2025 – Jan 2026 — Harmonising Supervision

  • 18 Dec 2025: AMLA publishes draft Regulatory Technical Standards (RTS) defining:
    • A common risk assessment methodology for all supervisors across the EU;
    • Criteria for selecting which institutions might be directly supervised by AMLA.

These instruments will set out how AMLA and national supervisors will assess money laundering and terrorist financing risks and work together in the process of AMLA selecting the entities it will directly supervise.

  • 26 Jan 2026: AMLA announces a data-collection exercise to test and calibrate its risk-assessment models, designed to feed into the direct supervision selection model for 2027.

Mar – Dec 2026 — Model Calibration & National Data

  • Mar 2026: National supervisors begin collecting detailed risk data from selected financial institutions

  1. those likely to be eligible for AMLA direct supervision

    and

  2. a representative sample of other institutions. AMLA uses this to test and refine its models.

 

  • Ongoing 2026: AMLA finalises risk assessment methodology and coordinates with Member State supervisors.

2027 — Selection of Supervised Entities

  • Early 2027: After model calibration:
    • National supervisors collect final comprehensive data from institutions eligible for direct supervision;
    • AMLA applies its risk models to select up to 40 financial institutions or groups for direct supervision starting in 2028.

2028 — Direct Supervision Begins

  • From 2028: AMLA becomes fully operational in its direct supervisory mandate — directly overseeing those selected institutions identified as having the highest money-laundering and terrorist-financing risks.

📌 What This Means for RMCWise Clients

RMCWise is a Risk, AML and compliance solutions provider — and AMLA’s supervision regime impacts clients in several concrete ways:

🧠 1. Stronger, Standardised Risk Assessment Expectations

AMLA is building harmonised risk models that national supervisors and AMLA will use to assess money-laundering and terrorist-financing risk consistently across the entire EU. This implies:

  • Increased demand for high-quality risk data from institutions.
  • Greater emphasis on structured, consistent risk scoring and reporting standards.

📊 2. Data-Driven Model Calibration — Clients May Be Involved

With national supervisors collecting data from a sample of institutions (including larger ones), expectations about:

  • Granularity of data;
  • Quality of risk indicators;
  • Automated reporting pipelines;

will intensify.

Clients of RMCWise should be prepared to:

  • Respond to data calls from their supervisors;
  • Implement compliance tools that can capture and structure the needed data elements.

📈 3. Preparations for Direct Supervision

Institutions that may fall under AMLA’s direct supervision (those cross-border or high risk) will face:

  • More stringent AML/CFT supervision;
  • Regular interaction with AMLA’s risk models;
  • Potentially deeper audits and expectations for demonstrating effective controls.

While much starts in 2028, the data collection in 2026 and selection in 2027 are significant preparatory checkpoints.

RMCWise can help institutions anticipate and satisfy these requirements.

📅 4. Strategic Planning Horizon

RMCWise clients should view:

  • 2026–2027 as preparation years — not just for data reporting, but for embedding risk measurement frameworks into core governance and monitoring.
  • 2028 onward as a new supervisory environment where data quality and risk modelling become central to ongoing regulatory engagement.

🧠 In Summary

Phase

Key Activity

Impact on RMCWise Clients

2025–2026

AMLA tests risk models via data collection

Need for robust reporting and data readiness

2026–2027

Calibration and selection process begins

Increased demand for harmonised risk metrics

2028 onward

Direct supervision under AMLA

Heightened scrutiny, consistent EU-wide standards

 

 

 

https://www.amla.europa.eu/amla-launch-data-collection-exercise-test-risk-assessment-models-financial-sector_en

EXPLAINER: 5aa923cc-eece-4cff-a9dd-4f687e88962b_en

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