Briefings Blog

Chartered and public accountants engaged on Consumer Duty fair-value reporting are increasingly using AI to validate fair-value assessment methodology, draft committee-ready summaries of non-monetary benefit analysis, and prepare audit-evidence memos that reconcile the firm's pricing rationale against the FCA's stated expectations. The work feeds directly into audit-file memos, fair-value attestation packs, and board-paper assertions that an external auditor will revisit.

Two frontier AI models tested by the RLB Specialist Panel produced 2 substantive failures on this regulation under audit conditions. The failure classes recorded are: Inference Drift on Fair Value Quantification Expectation, Inference Drift on Required Depth of Non-Monetary Analysis. Questions were prepared by the RLB Specialist Panel based on real practical AI usage in the workflows the respective audience uses AI for, and each finding is bound to verbatim regulator-issued source text held as primary substrate.

The Consumer Duty (PS22/9 introducing Principle 12 and PRIN 2A, in force for open products from 31 July 2023 and for closed products from 31 July 2024) is the central retail-conduct regime the FCA now uses to grade firm behaviour, and the failure modes seen here all land inside the day-to-day work product that accountants sign off on.

For accountants, the operational consequence is direct. A fair-value attestation, an audit memo, or a fair-value methodology review built on the AI's framing imports a defect into audit evidence. The next ICAEW or PCAOB-equivalent file review, a regulatory enquiry, or a client's internal-audit pull will surface the gap, and the accountant carries the professional-quality exposure.

Citation IDs for the findings in this brief: RLB-H-GB-FCA-CONSUMER-DUTY-PS22-9-Q008-Opus47, RLB-H-GB-FCA-CONSUMER-DUTY-PS22-9-Q008-Sonnet46. Each citation links to the per-finding record, the AI subject answer, and the regulator-issued substrate excerpt the answer was tested against. The RLB Specialist Panel maintains an audit-traceable record of which model produced which answer, against which substrate passage, and the binding is what makes the finding referenceable in firm work product and in supervisory correspondence.

The findings below are the ones that accountants working under the Consumer Duty are most likely to encounter in the AI tools they already use, and the briefing sections that follow read each finding against the regulator-issued text.

Financial advisers operating under the Consumer Duty are increasingly using AI to validate suitability narratives, draft client-facing fair value rationales for retained-product reviews, generate compliance file-notes against PRIN 2A.4, and stress-test investor disclosures against the FCA's stated expectations. The work product feeds directly into client-facing letters, advice records, and product-governance documentation that the regulator can pull on a thematic review.

Two frontier AI models tested by the RLB Specialist Panel produced 5 substantive failures on this regulation under audit conditions. The failure classes recorded are: Inference Drift on the Foreseeable-Harm Safe Harbour, Confused Guidance with Rule on Consumer Testing, Inference Drift on Fair Value Quantification Expectation, Inference Drift on Required Depth of Non-Monetary Analysis, Reversed the PRIN 2A Group-Insurance Exclusion. Questions were prepared by the RLB Specialist Panel based on real practical AI usage in the workflows the respective audience uses AI for, and each finding is bound to verbatim regulator-issued source text held as primary substrate.

The Consumer Duty (PS22/9 introducing Principle 12 and PRIN 2A, in force for open products from 31 July 2023 and for closed products from 31 July 2024) is the central retail-conduct regime the FCA now uses to grade firm behaviour, and the failure modes seen here all land inside the day-to-day work product that financial advisers sign off on.

For financial advisers, the operational consequence is direct. A suitability record or client-facing fair-value rationale built on the AI's framing imports a defect into the advice file. A thematic review, a complaint to the Financial Ombudsman Service, or a follow-up supervision visit will surface the gap, and the adviser carries the regulatory exposure.

Citation IDs for the findings in this brief: RLB-H-GB-FCA-CONSUMER-DUTY-PS22-9-Q003-Opus47, RLB-H-GB-FCA-CONSUMER-DUTY-PS22-9-Q007-Sonnet46, RLB-H-GB-FCA-CONSUMER-DUTY-PS22-9-Q008-Opus47, RLB-H-GB-FCA-CONSUMER-DUTY-PS22-9-Q008-Sonnet46, RLB-H-GB-FCA-CONSUMER-DUTY-PS22-9-Q018-Opus47. Each citation links to the per-finding record, the AI subject answer, and the regulator-issued substrate excerpt the answer was tested against. The RLB Specialist Panel maintains an audit-traceable record of which model produced which answer, against which substrate passage, and the binding is what makes the finding referenceable in firm work product and in supervisory correspondence.

The findings below are the ones that financial advisers working under the Consumer Duty are most likely to encounter in the AI tools they already use, and the briefing sections that follow read each finding against the regulator-issued text.

Lawyers advising on the Consumer Duty are increasingly using AI to draft client memos on Principle 12 scope, validate threshold language for fair value assessments, prepare partner-level briefings on PRIN 2A obligations, and reconcile FCA Feedback Statements such as FS25/2 against existing supervisory expectations. The work product is high-leverage: a single retail-customer-outcomes opinion can sit at the centre of a board paper, a Section 138D risk note, or an enforcement-defence brief.

Two frontier AI models tested by the RLB Specialist Panel produced 8 substantive failures on this regulation under audit conditions. The failure classes recorded are: Misstated Statutory Architecture, Inference Drift on the Foreseeable-Harm Safe Harbour, Confused Guidance with Rule on Consumer Testing, Hedge in Place of Verified FS25/2 Figure, Refusal to Confirm a Documented FS25/2 Count, Reversed the PRIN 2A Group-Insurance Exclusion, Invented Dual-Event Timeline for a Single FS25/2 Withdrawal, Refusal to Confirm FS25/2 Withdrawal Count.

Questions were prepared by the RLB Specialist Panel based on real practical AI usage in the workflows the respective audience uses AI for, and each finding is bound to verbatim regulator-issued source text held as primary substrate. The Consumer Duty (PS22/9 introducing Principle 12 and PRIN 2A, in force for open products from 31 July 2023 and for closed products from 31 July 2024) is the central retail-conduct regime the FCA now uses to grade firm behaviour, and the failure modes seen here all land inside the day-to-day work product that lawyers sign off on.

For lawyers, the operational consequence is direct. A Consumer Duty opinion or board memo built on the AI's framing imports a defect into client-facing work product. Cross-examination on the rule text, a Section 138D claim, or a supervisor's follow-up letter will surface the gap immediately, and the lawyer carries the professional liability.

Citation IDs for the findings in this brief: RLB-H-GB-FCA-CONSUMER-DUTY-PS22-9-Q002-Sonnet46, RLB-H-GB-FCA-CONSUMER-DUTY-PS22-9-Q003-Opus47, RLB-H-GB-FCA-CONSUMER-DUTY-PS22-9-Q007-Sonnet46, RLB-H-GB-FCA-CONSUMER-DUTY-PS22-9-Q013-Opus47, RLB-H-GB-FCA-CONSUMER-DUTY-PS22-9-Q013-Sonnet46, RLB-H-GB-FCA-CONSUMER-DUTY-PS22-9-Q018-Opus47, RLB-H-GB-FCA-CONSUMER-DUTY-PS22-9-Q020-Opus47, RLB-H-GB-FCA-CONSUMER-DUTY-PS22-9-Q020-Sonnet46. Each citation links to the per-finding record, the AI subject answer, and the regulator-issued substrate excerpt the answer was tested against. The RLB Specialist Panel maintains an audit-traceable record of which model produced which answer, against which substrate passage, and the binding is what makes the finding referenceable in firm work product and in supervisory correspondence.

The findings below are the ones that lawyers working under the Consumer Duty are most likely to encounter in the AI tools they already use, and the briefing sections that follow read each finding against the regulator-issued text.

Company secretaries supporting boards of regulated firms are increasingly using AI to draft board-pack summaries of Consumer Duty annual board reports, validate Principle 12 mapping for committee minutes, prepare director briefings on PRIN 2A obligations, and reconcile FCA Feedback Statements such as FS25/2 against existing supervisory expectations recorded in board papers. The output sits at the centre of director-attestation packs and audit-committee minutes that auditors and the regulator can request.

Two frontier AI models tested by the RLB Specialist Panel produced 9 substantive failures on this regulation under audit conditions. The failure classes recorded are: Misstated Statutory Architecture, Inference Drift on the Foreseeable-Harm Safe Harbour, Confused Guidance with Rule on Consumer Testing, Inference Drift on Fair Value Quantification Expectation, Hedge in Place of Verified FS25/2 Figure, Refusal to Confirm a Documented FS25/2 Count, Reversed the PRIN 2A Group-Insurance Exclusion, Invented Dual-Event Timeline for a Single FS25/2 Withdrawal, Refusal to Confirm FS25/2 Withdrawal Count.

Questions were prepared by the RLB Specialist Panel based on real practical AI usage in the workflows the respective audience uses AI for, and each finding is bound to verbatim regulator-issued source text held as primary substrate. The Consumer Duty (PS22/9 introducing Principle 12 and PRIN 2A, in force for open products from 31 July 2023 and for closed products from 31 July 2024) is the central retail-conduct regime the FCA now uses to grade firm behaviour, and the failure modes seen here all land inside the day-to-day work product that company secretaries sign off on.

For company secretaries, the operational consequence is direct. A board-pack summary or audit-committee briefing built on the AI's framing imports a defect into director attestations. The next supervisory visit, an internal-audit pull of the board record, or an external review of governance materials will surface the gap, and the secretariat carries the governance-quality exposure.

Citation IDs for the findings in this brief: RLB-H-GB-FCA-CONSUMER-DUTY-PS22-9-Q002-Sonnet46, RLB-H-GB-FCA-CONSUMER-DUTY-PS22-9-Q003-Opus47, RLB-H-GB-FCA-CONSUMER-DUTY-PS22-9-Q007-Sonnet46, RLB-H-GB-FCA-CONSUMER-DUTY-PS22-9-Q008-Opus47, RLB-H-GB-FCA-CONSUMER-DUTY-PS22-9-Q013-Opus47, RLB-H-GB-FCA-CONSUMER-DUTY-PS22-9-Q013-Sonnet46, RLB-H-GB-FCA-CONSUMER-DUTY-PS22-9-Q018-Opus47, RLB-H-GB-FCA-CONSUMER-DUTY-PS22-9-Q020-Opus47, RLB-H-GB-FCA-CONSUMER-DUTY-PS22-9-Q020-Sonnet46. Each citation links to the per-finding record, the AI subject answer, and the regulator-issued substrate excerpt the answer was tested against. The RLB Specialist Panel maintains an audit-traceable record of which model produced which answer, against which substrate passage, and the binding is what makes the finding referenceable in firm work product and in supervisory correspondence.

The findings below are the ones that company secretaries working under the Consumer Duty are most likely to encounter in the AI tools they already use, and the briefing sections that follow read each finding against the regulator-issued text.

ESG & Sustainability teams at Telecommunications firms operating under digital infrastructure environmental impact and data-centre energy reporting are increasingly using AI to populate annual sustainability reports with OECD-cited national data-centre energy benchmarks, draft board briefings on digital-infrastructure footprint across European operations, prepare regulatory data submissions referencing OECD national baselines, and validate disclosed figures against regulator-cited primary sources.

The OECD's 2025 Revision of the Recommendation on Digital Technologies and the Environment carries a named, citable statistic on Ireland's data-centre share of metered electricity, drawn from Ireland's Central Statistics Office, that ESG & Sustainability teams at telecommunications firms will reach for when populating sustainability disclosures, ESG investor responses, and regulatory briefings on digital-infrastructure environmental impact. That statistic is exactly the kind of figure the RLB Specialist Panel tested two frontier AI subjects against.

The RLB Specialist Panel issued a Specialist Panel application-style question on the share of Ireland's 2021 metered electricity that data centres accounted for, per the figure cited in the OECD Digital Economy Outlook 2024 chapter referenced by the 2025 Recommendation, sourced from Ireland's CSO (2023). Two frontier AI models tested by the RLB Specialist Panel returned the figure as 14 per cent and extended the answer with a four-point time series running from 5 per cent in 2015 through 21 per cent in 2023. The regulator's verbatim text records 11 per cent in 2021, with no multi-year trajectory.

The failure class is Fabricated Fact: a confidently delivered, citably attributed statistic that does not match the source document, compounded by a fabricated time series that does not appear anywhere in the OECD or CSO published record.

For ESG & Sustainability teams at telecommunications firms, this is operationally consequential because the wrong figure is not a vague paraphrase. It is delivered with a real source chain, CSO 2023 via OECD Digital Economy Outlook 2024, that survives standard reference-check review. An ESG team at a telecommunications firm consulting AI for the OECD's Ireland data-centre energy figure would receive 14 per cent, not the verbatim 11 per cent, along with a fabricated four-point time series (2015 to 2023) that appears nowhere in the source.

If that figure enters an annual sustainability report, a board briefing, or a regulatory data submission, the firm has published a material misstatement attributed to a real and checkable primary source. The exposure is compounded in international operating environments where multiple national regulators independently cite the same OECD benchmark: a telecommunications group with European operations may find its disclosed figure contradicted not only by the OECD document but by the regulator's own published baseline, creating a discrepancy that requires formal correction and explanation.

The audit's finding on this question is published with an immutable RLB Citation ID. The relevant entry is RLB-H-INT-OECD-OECD-DIGITAL-TECHNOLOGIES-ENVIRONMENT-2025-Q006-Sonnet46. The full audit is published at the OECD Digital Technologies and the Environment Recommendation (2025 Revision) hub on RegLegBrief.com.