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AMLA launches data collection exercise to test risk assessment models

AMLA has published the reporting package for its data collection and testing exercise. Sampled entities are invited to download the template below and access a recorded webinar addressing key questions.

  • 16. marts 2026
AMLA Data Collection Exercise

The exercise will test and calibrate AMLA's risk assessment models, which serve two purposes: to inform the selection, taking place in 2027, of up to 40 entities for AMLA's direct supervision starting in 2028, and to ensure that money laundering risks of credit and financial institutions are assessed consistently by supervisors across the EU.

Who participates

All entities taking part in this exercise have already been notified by their national competent authorities. If you have not been notified, you are not part of this exercise.

From development to collection

AMLA developed the reporting package in close cooperation with national supervisors and the sampled entities. Following an initial feedback round, AMLA circulated a draft version to sampled entities via national competent authorities, allowing them to familiarise themselves with the template and begin internal data mapping. The reporting package published today incorporates input from national supervisors and the private sector and serves as the reference package for the testing exercise.

What's available now

Please access here:

The reporting package for the testing exercise, including

The Interpretative Note 

The Template

A recorded webinar explaining the reporting requirements and next steps (see below)

The Webinar Slides

Timeline

Participating entities are requested to submit their data by 22 April 2026.

Why this matters

High-quality data from the private sector is essential to building a reliable selection model and developing a common EU-wide risk assessment methodology. The exercise will allow participating financial institutions to test and prepare their systems for future data collections, while AMLA will use the insights gained to optimise the data collection planned for the selection process.

AMLA