Risk teams at payment institutions and e-money firms operating under the Consumer Duty are increasingly using AI to update foreseeable-harm risk matrices for retail-customer journeys, validate fair-value risk assessments for new products, and stress-test the firm's customer-outcome KPIs against PRIN 2A. The work product feeds directly into the firm's risk register and the executive-risk-committee dashboard.
Two frontier AI models tested by the RLB Specialist Panel produced 3 substantive failures on this regulation under audit conditions. The failure classes recorded are: Inference Drift on the Foreseeable-Harm Safe Harbour, 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 payment-institutions risk teams sign off on.
For payment-institutions risk, the operational consequence is direct. The risk register, the foreseeable-harm matrix for retail-customer journeys, and the executive-risk-committee dashboard all rest on accurate PRIN 2A framing. A defect imported from AI work product surfaces on internal-audit pull or supervisor review, and the risk function carries the second-line 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-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 payment-institutions risk teams 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.
Payment Institutions risk teams designing customer-onboarding and fraud-warning controls need a clean view of the PRIN 2A.2 safe harbour. The model's multi-condition reconstruction would lead to defensive controls the rule does not require.
Payment Institutions risk teams reviewing fair-value assessment outputs need the FG22/5 qualitative-only standard preserved. The model's reversal would push the risk function to demand quantitative analysis the FCA has expressly not required.
Payment Institutions risk teams reviewing fair-value assessment outputs should not treat 'substantiated comparisons' as the FCA's expectation; FG22/5's standard is qualitative description.
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.