Responsible Adoption of Artificial Intelligence

For compliance, risk and technology leaders, the message is clear: AI use must be governed with the same rigour as any other material risk discipline — with board-level accountability, documented model validation, robust data governance and demonstrable fairness, transparency and explainability. Firms deploying generative AI in customer-facing or credit-decisioning processes should expect heightened supervisory scrutiny, particularly where outputs influence outcomes for retail customers. Actionable next steps include mapping current and pipeline AI use cases against the HKMA's expectations, embedding AI risk into the existing operational risk and model risk frameworks, strengthening third-party and vendor oversight for outsourced AI solutions, and ensuring staff have appropriate AI literacy and escalation channels.

Compliance teams should also refresh policies on data privacy, bias testing, human-in-the-loop controls and audit trails, and be prepared to evidence these controls to supervisors. Institutions that move early can turn compliance into a competitive advantage, using trusted AI governance to accelerate innovation while limiting conduct, reputational and operational risk. Those that delay risk remediation costs and potential supervisory findings.