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.
This is the consolidated view of findings. Click the Citation IDs or 'see details →' on any item for the full details for each finding.
Financial advisers designing retail-product journeys for vulnerable or general retail customers need a clean view of what the foreseeable-harm safe harbour actually requires. The model's multi-factor reconstruction will, if imported into a customer-disclosure template or product-governance memo, raise the compliance burden on the firm without any regulatory basis. The adviser who acts on the AI's framing builds a customer-warning standard the Duty does not impose, which both elevates cost and dilutes the actual single-test rule the FCA wrote.
Financial advisers responsible for retail communications and disclosure design need to know whether consumer testing is a binding rule or a recommended methodology. The model treats FG22/5's guidance as a binding rule under PRIN 2A.5.10R, which would push the adviser to build a mandatory testing programme where the FCA recommends but does not require one. The compliance-cost mis-pricing is real, and a CASS-equivalent build-out on a guidance-only obligation is hard to roll back once approved.
Financial advisers designing fair-value assessment templates for retail products should not import the AI's reversed methodology. The FCA expressly does not require quantification of non-monetary costs and benefits; the model's affirmation creates phantom analytical work that distracts from the actual regulator-mandated qualitative assessment. Advisers who follow the model's framing will overbuild the fair-value template and may still under-deliver on the qualitative assessment that the FG22/5 guidance actually requires.
Financial advisers reading the Sonnet 4.6 version of the fair-value answer face the same problem with a slightly different surface: 'substantiated comparisons' is a higher bar than the FCA's qualitative-assessment expectation. A product-governance committee that adopts the AI's bar will see fair-value reviews delayed, requesting numerical comparisons that the regulator has not asked for. The adviser who builds the AI's standard into the template carries the cost without any compliance benefit.
Financial advisers in the insurance distribution chain need accurate Consumer Duty scope decisions. The model's reversal of the group insurance carve-out can pull excluded activities into a Duty-compliant programme, which both inflates cost and dilutes attention from the activities that are within scope. An insurance-product adviser working off the AI's answer will misallocate review resources across the product portfolio.
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.