The BMA's AI Guidance Note, Explained

The Bermuda Monetary Authority's proposed Guidance Note on the Responsible Use of Artificial Intelligence in Bermuda's Financial Services Sector is the most important AI rulebook in Bermuda. It does not create an AI licence. It tells every BMA-regulated firm how its existing obligations apply when it uses AI, and what evidence the BMA may ask to see. Laurenceo, which submitted a public response to the consultation, explains what the guidance says and what to do about it.

At a Glance

Question	Answer
Who issued it?	The Bermuda Monetary Authority (BMA)
What is it?	A proposed Guidance Note, published with a consultation paper on 14 August 2026
Who does it apply to?	All BMA-regulated entities across all sectors
Is it a new law or licence?	No. No new legislation, licensing regime or pre-approval requirement
Consultation deadline	30 October 2026 (closed)
Final version	Not yet published; the BMA has not announced a date
Core structure	Eight principles and five outcomes

How the BMA Got Here

Date	Step
November 2022	BMA publishes its AI and machine learning survey report on use across the insurance market
July 2025	Discussion paper on the responsible use of AI in financial services
January 2026	The BMA's 2026 Business Plan lists AI framework enhancements among its fintech priorities
February 2026	Stakeholder letter summarising feedback; no new AI-specific requirements at that time
14 August 2026	Consultation paper with the proposed Guidance Note
1 October 2026	Laurenceo publishes its independent consultation response
30 October 2026	Consultation closes

Who It Applies To

Every entity the BMA regulates: commercial insurers and reinsurers, captives, insurance intermediaries, banks, investment businesses and funds, digital asset businesses, and trust and corporate service providers. The unit of assessment is the AI use case, not the firm's size or the label on the technology, as Appleby observes. A small firm running a high-impact underwriting model can face deeper expectations than a large firm using AI to draft emails.

The Eight Principles

Technology neutrality. The same obligations apply whether a decision is made by a person, a rule or a model.
Accountability. The board keeps overall accountability for AI risk; named owners answer for each use case.
Proportionality. Control depth follows the materiality and risk of the use case.
Integration with current frameworks. AI fits into existing governance, risk, conduct, cyber, outsourcing and AML frameworks rather than a parallel regime.
Robust lifecycle management. Selection, validation, deployment, monitoring, change and retirement are all governed.
Responsible and ethical data use. Data quality, lawful use and PIPA compliance.
Explainability and transparency. Outcomes can be explained to the degree the use case demands, including to customers.
Adaptability. Governance keeps pace as models, vendors and uses change.

The Five Outcomes

Outcome	What the BMA wants to see
1. Accountable governance and risk ownership	Board oversight, named owners, an AI inventory
2. Proportionate lifecycle management	Approvals and validation scaled to materiality
3. Reliable, explainable and overseen outcomes	Monitoring, meaningful human oversight, explainability
4. Secure and resilient deployment	Cyber controls, vendor management, fallback and incident response
5. Adequate evidence for internal assurance and supervisory review	Records that show the controls are working

The fifth outcome is the one most firms underestimate. It turns governance from a policy question into an evidence question.

Agentic AI

The proposed guidance includes detailed expectations for agentic AI: systems that can plan and carry out multi-step tasks, call tools, access systems and take actions with limited human intervention (Walkers). The consultation paper says controls over tools and APIs should not rely solely on prompts or model instructions. Laurenceo's response proposes nine enhancements that make that testable, from action tiering and permission manifests to tested suspension controls.

What Firms Should Do Now

Build an AI inventory. Every AI use case, including AI embedded in vendor software, with an owner, purpose, materiality rating and data sensitivity.
Rate materiality. Use the proportionality indicators in the consultation paper's Annex C; Laurenceo recommends rating materiality and inherent risk separately and letting the higher set control depth.
Map controls to the five outcomes. Find the gaps before a supervisor does.
Fix the evidence. Make sure each control leaves a record that a reviewer could rely on.
Check vendors. Treat material vendor model updates as change events.
Brief the board. The board is accountable, so it needs AI reporting it can act on.

Laurenceo's free tools map directly onto this list: the AI Governance Readiness Assessment scores you against the ten Laurenceo production controls, the Evidence Toolkit builds an AI inventory and board view, and the AI Policy Gap Analyzer checks your AI policy against the guidance.

Laurenceo's Position

Laurenceo supports the BMA's design: principles-based, technology-neutral and proportionate. Its consultation response makes seven recommendations, led by recognising system-generated evidence as a preferred form of evidence for material and higher-impact AI, and publishing an illustrative minimum evidence set. The core proposition is evidence by default: in a well-engineered AI system, every control produces its own evidence as a by-product of running.

Questions and Answers

Is the BMA's AI Guidance Note mandatory?

It is guidance, not legislation, but it explains how existing mandatory obligations apply to AI. Firms should expect the BMA to use it as the benchmark when it supervises AI use.

Does the BMA require approval before using AI?

No. The proposal does not introduce a licensing regime or pre-approval requirement for AI. Firms remain responsible for governing their own use.

Does the guidance cover ChatGPT and other generative AI tools?

Yes. It is technology-neutral, so it covers generative AI, machine learning models and agentic systems, including AI embedded in third-party software, assessed by use case.

When does the BMA AI guidance take effect?

The final Guidance Note has not been published. The consultation closed on 30 October 2026, and the BMA has not announced a publication or effective date.

Where can I read Laurenceo's response to the BMA?

Laurenceo's 12-page response, LAU-BMA-AI-2026-01, is published in full on laurenceo.com with a PDF, the principle-by-principle mapping and the Laurenceo Production Control Specification.