Compliance · June 2026 · 9 min read

The MAS FEAT AI Credit Decision Checklist: 32 Controls Your Fintech Needs

If your fintech uses any form of AI or machine learning to score, approve, decline, or price credit — even a single logistic regression feeding into an underwriter's dashboard — MAS expects you to be able to demonstrate Fairness, Ethics, Accountability, and Transparency (FEAT) for that model on demand. Below are 32 controls, organized by FEAT pillar, that map directly to what an inspector will ask for. Use this as a self-assessment before they ask.

Pillar 1: Fairness

Fairness controls focus on whether your model treats similar customers similarly, and whether it produces disparate outcomes across customer segments — intentionally or not.

• Document the model's intended use case and decision scopeFEAT: Fairness · TRM §7.3
• Maintain an inventory of all AI/ML models used in credit decisionsFEAT: Fairness, Accountability
• Test for disparate impact across protected and sensitive attributesFEAT: Fairness
• Document proxy variables and their correlation to protected attributesFEAT: Fairness
• Establish fairness metrics and acceptable thresholds before deploymentFEAT: Fairness
• Re-test fairness metrics on a defined cadence (e.g. quarterly)FEAT: Fairness · TRM §7.3
• Validate training data for representativeness across customer segmentsFEAT: Fairness
• Maintain a model risk rating (high/medium/low) with documented rationaleFEAT: Fairness, Accountability

Pillar 2: Ethics

Ethics controls cover whether the model's objective itself is appropriate, and whether there are safeguards for customers who may be disproportionately affected by automated decisions.

• Define and document the ethical objective the model servesFEAT: Ethics
• Establish a human review process for borderline or declined decisionsFEAT: Ethics, Accountability
• Define escalation paths for ethically ambiguous casesFEAT: Ethics
• Assess vulnerable customer segments for differential treatmentFEAT: Ethics, Fairness
• Document data sourcing and consent basis for training dataFEAT: Ethics · PDPA
• Review third-party and vendor model ethics policies, including LLM providersFEAT: Ethics · TRM §7

Pillar 3: Accountability

Accountability is where most fintechs have the biggest gap — not because the controls are hard, but because no one owns them. This is also the pillar most directly tied to the audit trail retention requirements covered in our OpenAI log retention post.

• Assign a named owner accountable for each AI model in productionFEAT: Accountability
• Establish board or senior management oversight of AI riskFEAT: Accountability · TRM §7
• Maintain a 5-year audit trail of model inputs, outputs, and versionsFEAT: Accountability · TRM §7.3
• Hash-chain or otherwise tamper-proof the audit logFEAT: Accountability · TRM §7.3
• Log the model version used for every individual decisionFEAT: Accountability, Transparency
• Maintain an incident response plan for model failures or bias findingsFEAT: Accountability · TRM §7
• Conduct independent validation before model deploymentFEAT: Accountability
• Re-validate models after material retraining or data changesFEAT: Accountability, Fairness
• Maintain change management records for all model updatesFEAT: Accountability · TRM §7
• Define and test rollback procedures for faulty model versionsFEAT: Accountability · TRM §7

Pillar 4: Transparency

Transparency is what your customers and MAS both care about: can a person who was declined a loan understand why, and can your firm reproduce and explain that decision a year later?

• Provide customers with a meaningful explanation of adverse decisionsFEAT: Transparency
• Disclose to customers when AI is used in the decision processFEAT: Transparency
• Generate human-readable reason codes for declinesFEAT: Transparency
• Maintain internal documentation explainable to non-technical reviewersFEAT: Transparency, Accountability
• Establish a customer dispute or appeal process for AI-driven decisionsFEAT: Transparency, Ethics
• Map each control to its MAS FEAT principle and TRM clauseFEAT: Transparency, Accountability
• Run a pre-inspection self-assessment against this checklist annuallyFEAT: All pillars
• Centralize evidence — logs, test results, sign-offs — for inspection readinessFEAT: All pillars · TRM §7.3
• Verify your audit trail can reconstruct any single decision end-to-end on requestFEAT: Transparency, Accountability

Why This Checklist, Not a Generic AI Governance Framework

Most "AI governance checklists" available online are written for US or EU audiences and reference NIST AI RMF or the EU AI Act. Those are useful background reading, but they don't map cleanly to what a MAS inspector is trained to look for. The 32 controls above are organized specifically around the four FEAT pillars and cross-referenced to MAS Technology Risk Management Guidelines §7, which is the section inspectors cite most often when they ask for "your AI audit trail."

For the broader regulatory context — what FEAT is, who it applies to, and how it intersects with TRM — see our MAS AI Risk Management Guidelines 2025 overview.

Where Veritrail Fits

Veritrail doesn't replace the policy and process work behind most of these controls — items like #1, #5, #9, and #15 are organizational decisions your team needs to make. What Veritrail does is make controls #17, #18, #19, and #32 automatic: every call to your AI model — whether it's OpenAI, Gemini, Claude, or an in-house model — is captured, hash-chained, encrypted, and retained for 5 years, with the model version and decision context attached. When an inspector asks you to reconstruct a specific decision from 2024, you query it instead of searching for it.