AMLD6: Comfort in Your AML Controls or 10% Turnover Fines?

Beneficial Ownership Under AMLD6: Ownership and Control

What Financial Institutions Are Expected to Do

AMLD6 makes explicit that Ultimate Beneficial Owners must be identified through assessment of both ownership and control. Financial institutions must be able to:

• identify individuals or entities that own a customer through direct or indirect shareholdings;
• identify individuals or entities that control a customer through influence or decision-making authority;
• assess ownership and control across multiple layers of entities and arrangements;
• consider aggregated interests, including small or indirect holdings;
• identify structures designed to obscure ownership or control; and
• demonstrate how ownership and control were determined at a specific point in time.

The expectation is a reasonable, well-documented determination of who ultimately owns or controls a customer—and the ability to reach and explain that determination consistently when revisited.

How DataWalk Supports Ownership and Control Analysis

DataWalk enables ownership and control to be examined together within a single, coherent view. It allows financial institutions to:

• bring together ownership information from onboarding systems, registries, documentation, and third-party sources;
• reconstruct ownership and control across all relevant layers;
• assess ownership and influence in combination rather than in isolation;
• clearly surface complex or unusual structures; and
• retain full visibility into how conclusions were reached over time.

This supports consistent, explainable UBO identification aligned with AMLD6 expectations, even where ownership and control structures are layered, indirect, or change over time.

Cooperation With FIUs Under AMLD6

What Financial Institutions Are Expected to Do

AMLD6 strengthens cooperation between financial institutions, national Financial Intelligence Units, and other competent authorities. Institutions are expected to be able to:

• provide accurate, timely, and consistent information;
• explain the reasoning behind alerts, reports, and investigative conclusions;
• respond to follow-up requests without reconstructing analysis from scratch; and
• demonstrate internal consistency in how similar risks are assessed.

As authorities increasingly share typologies, risk indicators, and suspicious entities, institutions are also expected to act on this intelligence in a timely and controlled manner.

How DataWalk Supports Intelligence Collaborations

DataWalk preserves a continuous Chain of Evidence, ensuring that analytical reasoning remains traceable and reviewable. This enables financial institutions to:

• respond confidently to FIU requests;
• ensure shared information is grounded in consistent logic;
• quickly cross-check typologies, risk indicators, and suspicious entities shared by FIUs or government agencies against your institution’s customer base and transaction activity;
• reduce friction during follow-up engagement; and
• maintain clarity even as cases evolve.

The result is controlled, confident cooperation that strengthens regulatory trust without introducing inconsistency or ad hoc decision-making.

The Architectural Challenge Behind AMLD6

Many AML environments remain constrained by siloed systems designed for narrow detection tasks rather than holistic risk understanding. As AMLD6 increases expectations around contextual analysis and explainability, these silos become a structural limitation—making it difficult to demonstrate how risks were assessed across data, time, and organisational boundaries. The challenge is not replacing existing systems, but whether those systems can be connected in a way that preserves context and reasoning at the standard AMLD6 now expects.

A Unified Intelligence Layer for AML

DataWalk acts as an intelligence layer that sits above existing AML systems, bringing together alerts, customer data, transaction activity, and external information into a single analytical foundation. This allows financial institutions to:

• decouple intelligence from transaction processing;
• integrate new data sources without disrupting operations;
• adapt to emerging typologies without re-engineering pipelines; and
• maintain explainability as analysis evolves.

In doing so, it helps close the gap between regulatory expectations and what existing AML technology can realistically support on its own.