AI Governance Policy Template
Executive Summary
Key Takeaways
- ✓ An AI governance policy template should move from scope and ownership, through technique-task matching and verification checkpoints, to the risk register and review cadence, in that order, since later sections depend on the scope and ownership established earlier.
- ✓ The scope and ownership section should name specifically which AI applications the policy covers and who owns each, before any checkpoint or risk register content is added, since a checkpoint without a named owner has no practical accountability.
- ✓ The verification checkpoint section should specify, for each covered AI application, where in the workflow the checkpoint sits and what it actually checks, not a generic statement that verification occurs.
- ✓ This template is the policy structure; the underlying technique, governance, and risk guidance should follow the guides referenced throughout this pillar.
Purpose¶
This template sets out a consistent section-by-section structure for a finance function's internal AI governance policy, within AI Financial Modelling & Artificial Intelligence in Finance, so the policy is concrete and operational rather than a general statement of principle disconnected from day-to-day AI use.
Template Structure¶
1. Scope and Ownership. The specific AI applications the policy covers, and a named owner accountable for each. See AI Model Governance.
2. Technique-Task Matching Rationale. For each covered application, which AI technique category is used and why it is matched to the task. See Artificial Intelligence in Finance.
3. Adoption Stage. Where each covered application sits in the staged adoption progression, and the conditions required to advance. See AI Adoption Framework.
4. Verification Checkpoints. For each application, where in the workflow the checkpoint sits and what it specifically checks. See AI-Assisted Financial Analysis and Human-in-the-Loop Review.
5. Audit Trail Requirements. What must be captured, prompt, model version, source material, checkpoint outcome, human decision, for each application. See AI Audit Trail.
6. Quality Assurance Programme. The sampling approach, cadence, and independence structure for ongoing QA review. See AI Quality Assurance.
7. AI Risk Register. Each risk category, owner, control, and review cadence, tied to the specific guidance addressing it. See AI Risk Management.
8. Regulatory and Ethical Considerations. Jurisdiction-specific regulatory considerations confirmed with counsel, and the fairness, transparency, and accountability questions applicable to each application. See AI Regulatory Considerations and AI Ethics in Finance.
9. Review Cadence. A stated, defined schedule for reviewing the policy itself, not only the applications and risks it governs.
Why This Structure Matters¶
Each section exists to prevent a specific failure mode addressed elsewhere in this domain: a policy without named ownership, addressed in AI Model Governance, leaves accountability diffuse; a policy without specific checkpoint detail can permit the rubber-stamp degradation addressed in Human-in-the-Loop Review; and a risk register without owners and controls, addressed in AI Risk Management, documents risk without managing it.
How to Use This Template¶
Populate each section in the order presented, since later sections depend on the scope, ownership, and adoption stage established earlier; a verification checkpoint cannot be meaningfully specified without first knowing which application and owner it applies to. Review and update the populated policy on the defined cadence stated in the final section, incorporating findings from the quality assurance programme and any changes in adoption stage for the applications it covers.
Continue Reading¶
Related Pillars¶
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Related Case Studies¶
- An AI-Drafted Variance Narrative Misattributes the Driver Behind a Margin Decline
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Related Products¶
How OXXON tests thisRun a free structural check with FMAE
Frequently Asked Questions
What is the purpose of this template?
To give a finance function a consistent, operational structure for its internal AI governance policy, moving from scope and ownership through technique-task matching, verification checkpoints, and the risk register to a defined review cadence, ensuring the policy is concrete rather than a general statement of principle.
Why does the template establish scope and ownership before checkpoints and the risk register?
Because a verification checkpoint or a risk register entry without a named owner and a defined scope has no practical accountability mechanism; establishing who owns what, and for which specific AI applications, is the necessary foundation for the sections that follow.
Does this template replace the underlying technique and governance guidance in this domain?
No. This template structures the overall policy; the underlying technique-matching, checkpoint design, and risk category guidance should follow the guides referenced throughout this pillar.
How often should a policy built on this template be reviewed?
On a defined, regular cadence stated explicitly in the policy itself, not on an ad hoc basis, consistent with the review cadence discipline set out in AI Risk Management.
References
Related Articles
AI Financial Modelling & Artificial Intelligence in Finance
AI financial modelling is the application of machine learning and generative AI techniques within the financial modelling process itself, driver identification, construction assistance, scenario generation, and narrative drafting, while artificial intelligence in finance is the broader application of those same technique categories across the finance function generally. This page is the hub for the Knowledge Centre's AI financial modelling content: the foundational distinction between machine learning, natural language processing, and generative AI; how AI accelerates modelling construction without replacing the auditable calculation layer beneath it; a staged framework for adopting AI reliably; enterprise applications across FP&A, forecasting, valuation, and investment analysis; governance and risk practice; and the institutional best practice synthesis this domain builds toward.
AI Model Governance
AI model governance establishes ownership, documented scope and limitations, change control, and periodic re-validation for machine learning and generative AI models used within a finance function. This guide sets out the governance elements specific to AI models, distinct from but complementary to the financial model governance a firm already applies to its spreadsheet and system models, and why an AI model's statistical nature requires governance triggers a static formula-based model does not.
AI Risk Management
AI risk management brings together the distinct risk categories addressed across this domain, hallucination, model drift, explainability limitations, fairness, regulatory exposure, and accountability diffusion, into a single risk register structure a finance function can maintain and review as part of its broader risk management practice. This guide sets out that register structure and how it connects to the governance, validation, and quality assurance practices addressed elsewhere in this domain.
AI Audit Trail
An audit trail for AI-assisted financial work should capture more than the final output: the prompt or task input, the specific model or technique version used, the source material supplied, the verification checkpoint outcome, and the human decision applied to the result. This guide sets out what a complete AI audit trail captures and why each element matters specifically for defending an AI-assisted conclusion after the fact, to an auditor, regulator, or internal governance review.