AI Governance for Regulated Systems
A practitioner perspective informed by participation in Singapore's public consultation on AI governance for financial institutions.
Disclaimer: This content reflects /tmp Labs' practitioner experience and interpretation only. It does not represent regulatory guidance, supervisory positions, or endorsement by any regulatory authority.
Why AI Governance Matters in Financial Services
Singapore's Monetary Authority of Singapore (MAS) has initiated a public consultation to strengthen expectations around the governance, risk management, and accountability of artificial intelligence systems used within financial institutions.
As practitioners building AI systems in regulated financial and insurance environments, /tmp Labs participated as an interested party — contributing perspectives grounded in real enterprise deployments where governance, accountability, and auditability are operational requirements, not policy aspirations.
Banking & Finance
Governance perspectives focused on model risk management, decision chains, decision, audit readiness, accountability, controlled adoption and integration with existing MRM frameworks.
Read Banking PerspectiveInsurance
Governance perspectives focused on actual system behaviour, fairness metrics, decision traceability, bias detection in pricing/underwriting, behavioral drift monitoring, and defensible claims adjudication.
Read Insurance Perspective