Public Consultation Response: Operationalising the Responsible Use of AI in Bermuda's Financial Services Sector

The Bermuda Monetary Authority has proposed guidance on how existing obligations apply to artificial intelligence in financial services. This response argues that the hardest test is not agreeing with the principles, but proving, on any given day, that AI controls are working.

Executive Summary

Laurenceo supports the Authority's central design choice: to explain how existing legal, regulatory and supervisory obligations apply to AI, rather than to create a stand-alone licensing or approval regime. A principles-based, technology-neutral and proportionate approach suits a jurisdiction whose insurance, reinsurance, banking, investment and digital asset sectors use AI in very different ways.

The response concentrates on the Authority's fifth outcome, adequate evidence for internal assurance and supervisory review. This builds on the earlier Laurenceo research: Building Compliant AI Systems in Bermuda, A Reference Model for AI Governance and Operating AI Governance in Production.

The response makes seven recommendations:

1. Recognise system-generated evidence as a preferred form of evidence for material and higher-impact AI.
2. Publish an illustrative minimum evidence set for lower-impact, material and higher-impact use cases.
3. Specify indicative minimum fields for AI inventories, without prescribing a system.
4. Strengthen agentic AI expectations with boundaries enforced outside the model.
5. Treat material vendor model updates as change events that trigger re-verification.
6. Connect AI incident records to PIPA breach assessment.
7. Add worked sector examples for claims, underwriting, AML/ATF, customer-facing generative AI and agentic automation.

The Eight Principles and Five Outcomes

Laurenceo supports all eight principles: technology neutrality, accountability, proportionality, integration with current frameworks, robust lifecycle management, responsible and ethical data use, explainability and transparency, and adaptability. The paper maps each to a production control and a targeted enhancement. The five outcomes map to evidence a system can generate automatically:

The paper also answers the Authority's three consultation questions. In short: the guidance explains existing requirements well but would benefit from clarity on what form of evidence is sufficient; the materiality approach is workable, and stronger if materiality and inherent risk are rated separately with the higher of the two setting control depth; and sector examples would help.

Agentic AI Controls

The consultation paper states that controls over tools and APIs should not rely solely on prompts or model instructions. The response proposes nine enhancements that give that principle testable form.

The Laurenceo Production Control Specification

Ten controls show how each category of information the Authority may request can be produced continuously by the system itself. Control depth follows three materiality tiers: lower impact, material, and higher impact. The specification is illustrative and consistent with the paper's position that entities may adopt alternative controls that achieve the same outcomes.

Each control writes a standard evidence record, so evidence can be indexed against requirements:

{
  "control_id":    "LPC-03",
  "use_case_id":   "UC-CLM-007 (claims triage, material)",
  "timestamp_utc": "2026-10-14T13:42:08Z",
  "event":         "output_verification",
  "result":        "fail: reserve estimate above authority threshold",
  "action":        "routed to human review queue",
  "maps_to":       ["Outcome 3", "Principle 7"],
  "prev_hash":     "9f1c...e2a7",
  "record_hash":   "4b0d...71c3"
}

The Full Paper

The complete 12-page response, including the principle-by-principle table, the full control specification and the submission details.

Laurenceo submits this response in its own capacity. The views expressed do not represent any employer, placement host or academic institution. This page is for general information only and is not legal, regulatory or financial advice.