Banks and financial institutions are rushing to layer AI onto compliance operations that were never built to support it, according to identity verification and compliance firm Duna, which argues that a structured readiness assessment is now essential before automation can scale safely across the function.

The adoption gap is already visible. Duna points to the Association of Certified Anti-Money Laundering Specialists (ACAMS) Global Threats Report 2026, which found that more than half of anti-financial-crime (AFC) teams are using AI in their compliance programmes. Yet 52% of those teams say outdated data and legacy IT systems pose a high or very high risk to the very same programmes.

Duna stresses that compliance cannot be treated like customer support, coding or marketing, where generic AI can handle low-risk, unregulated tasks. Mistakes in compliance carry financial and regulatory consequences, and compliance leaders may face personal liability and fines. Regulators are also raising the bar. The EU’s Anti-Money Laundering Authority (AMLA) states in its draft guidelines on ongoing monitoring that firms using AI must be able to explain its role, functioning and outputs so they can be reviewed and challenged.

As a result, Duna argues that institutions need bank-grade AI that is explainable, auditable and repeatable. Teams must be able to show auditors why a case was flagged or accepted, keep a full record of what the AI was asked and what evidence it weighed, and ensure identical inputs under the same policy always produce the same outcome, with hard rules such as knockout criteria applied every time.

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