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.
This is the consolidated view of findings. Click the Citation IDs or 'see details →' on any item for the full details for each finding.
Accountants supporting fair-value assessment work need to ground methodology choices in the regulator's actual expectation, which is qualitative-only with no quantification mandate. The model's reversal of this position, if adopted in a financial-reporting or audit-support memo, would justify quantification work the FCA has expressly not required, inflating both cost and audit scope.
Accountants reviewing or supporting fair-value templates that include non-monetary cost and benefit analysis need to recognise that the FG22/5 standard is qualitative description, not substantiated comparison. The model's elevated bar would push the accountant to support quantification or comparison work that does not align with the regulator's stated expectation, creating a methodological mismatch between the firm's fair-value documentation and the FCA's published guidance.
Every finding on this page compares an AI subject's account of the rule against the regulator's verbatim text from the regulator's own portal. Both are linked. Each delta, its root causes, and impact analysis are documented and published with immutable Citation IDs.