MAS FEAT

MAS FEAT is guidance for responsible use of AI and data analytics in financial services, centered on Fairness, Ethics, Accountability, and Transparency.

Key facts

Type

Principle-based guidance

Scope

Financial AI and data analytics

Focus

Governance and accountability

Best for

Evidence-based responsible AI

Authoritative resources

• MAS FEAT Principles (Updated 7 Feb 2019) — PDF
• Mirror: MAS FEAT PDF (Singapore AI governance compendium)

What FEAT is good at

FEAT is most useful when you need to demonstrate that:

• fairness is defined, monitored, and improved
• ethical risks are identified and treated
• accountability is assigned and enforced through workflows
• transparency is achieved through documentation and user communication

Go deeper:

• Principles
• Operationalizing in Modulos

How to use this guide

FEAT is principle-based. It becomes actionable when you:

• decide which AI-driven decisions are “material” (higher scrutiny)
• translate principles into controls with owners and approvals
• add testing signals (fairness, drift, robustness) that run continuously
• keep evidence and decisions reviewable for internal audit

How Modulos operationalizes FEAT

Modulos turns principle language into auditable work:

• map FEAT principles to requirements and controls
• attach evidence (methodology, testing results, approvals)
• use testing and reviews to keep governance continuous

Framework mapping

Four layers, one reusable spine.

Frameworks

EU AI ActRegulatory

ISO 42001Standard

Requirements

Art. 9.1Risk management

Art. 10.2Data governance

6.1.1Risk assessment

Components

Risk identification

Impact analysis

Evidence

Risk registerDocument

Test resultsArtifact

Controls

The reusable spine

One control satisfies many requirements across many frameworks, and groups the components and evidence beneath them.

Risk assessment process

Data validation checks

Reusable

Edge from any layer card crosses into the Controls spine — the same control may serve a regulatory article, a standards clause, a downstream component, and the evidence that closes it.

FEAT in practice: principles → execution

Fairnessmetrics, monitoring, drift, remediation

Ethicsharm identification, unacceptable outcomes, governance gates

Accountabilityowners, approvals, recourse, oversight

Transparencydisclosures, explanations, user guidance

Getting started

Principles

The FEAT principles and how to interpret them for AI systems

Operationalizing in Modulos

A practical playbook for controls, evidence, and testing signals

Runtime Inspection

Use tests as governance signals for fairness and robustness

Risk quantification

Prioritize treatment and investment with monetary outputs

Governance operating model

Execute controls, link evidence, and run reviews continuously

See it in Modulos

Modulos translates MAS FEAT principles into controls, testing, and evidence for financial AI.

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Disclaimer

This page is for general informational purposes and does not constitute legal advice.