The integration of Artificial Intelligence (AI) into asset management has shifted from experimental automation to a core necessity for managing complex regulatory obligations. However, legal experts and practitioners emphasize that while AI enhances efficiency, it creates significant new demands for accountability, governance, and transparency.
Those are among key findings from Funds Europe Intelligence has launched its latest industry guide titled A smarter approach: AI & FinCrime in asset management compliance in partnership with FIS,
The shift toward harmonised supervision
A central theme in the evolving legal landscape is the push for cross-border consistency, particularly within the European Union. The establishment of the Anti-Money Laundering Authority (AMLA) represents a major step toward harmonised supervision. Starting in 2028, AMLA will directly oversee 40 of the most complex, high-risk financial institutions using a common risk-assessment methodology. This shift is supported by the transfer of the EuReCA database to AMLA in 2026, which will centralize information on AML/CFT deficiencies and enhance EU-wide coordination.
Governance and the “Human-in-the-Loop”
Legal and compliance professionals argue that responsibility for financial decisions cannot be “outsourced” to algorithms. Experts assert that strong governance must be built into every stage of AI deployment to balance innovation with regulatory conservatism. Key legal safeguards include:
- Human oversight: Maintaining a “human-in-the-loop” is considered essential for high-impact decisions affecting financial or legal outcomes.
- Traceability: Compliance teams must be able to demonstrate exactly how a decision was reached; a lack of traceability is considered a “deal-breaker” for regulators.
- Data integrity: Foundations must include “clean data,” strict access controls, and full GDPR compliance.
Ethical pillars and regulatory standards
As AI reshapes decision-making, ethical considerations such as fairness, privacy, and bias testing have become critical legal benchmarks. Practitioners expect regulators to increasingly codify these standards, moving toward mandatory requirements for explainability and logging. In the US, the Corporate Transparency Act is already increasing the volume of data available for monitoring, while the UK’s Edinburgh Reforms aim to balance oversight with market competitiveness.
Ultimately, the legal consensus is that AI is a tool to support, but not replace, human judgment. Effectiveness is no longer measured just by speed, but by an organisation’s ability to explain the “why” and “how” behind its AI-driven actions.
To read the full report from Funds Europe Intelligence, click here.
