Implementing MAS FEAT in Practice: A Controls Library for Banks
FEAT principles translated into engineering controls. What Fairness, Ethics, Accountability, and Transparency look like inside your CI pipeline, model registry, and audit pack.
The Monetary Authority of Singapore (MAS) FEAT principles (Fairness, Ethics, Accountability, Transparency) have moved from high-level guidance to operational expectation. Banks that pass MAS Veritas assessments share a common pattern: they treat each FEAT principle as a set of concrete engineering controls, not as an aspirational policy statement.
Fairness translates into protected-attribute bias tests that run in CI on every model release. Ethics translates into a use-case review board that signs off any new AI initiative before model build begins. Accountability translates into a documented chain of responsibility from model owner to board, captured in a model registry with named roles. Transparency translates into model cards, customer disclosures, and audit-ready documentation that explain what the model does, what data trained it, and how performance is monitored.
The Magnis AI controls library maps each FEAT principle to specific control implementations: a bias-test specification, a use-case intake form, a model registry schema, a model card template, and a quarterly governance review checklist. Banks that adopt this library typically clear MAS Veritas at first attempt and reduce model deployment time by 40 to 60 percent because the same evidence supports both internal audit and regulator review.