Briefings Blog

In-house legal teams at corporate banks operating under the Consumer Duty are increasingly using AI to draft scope opinions on the boundary between in-scope SME retail customers and out-of-scope large-risk commercial contracts, validate group-insurance distribution scope, and prepare director briefings on PRIN 2A. The work product feeds into legal-opinion files for the SME segment and director attestations on Consumer Duty applicability.

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: Misstated Statutory Architecture, 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 corporate-banking in-house legal teams sign off on.

For corporate-banking legal, the operational consequence is direct. Scope opinions on the SME-versus-large-risk boundary, group-insurance scope memos, and director attestations on Consumer Duty applicability all rest on accurate PRIN 2A scope framing. A defect imported from AI work product surfaces on legal-file review or board challenge, and the in-house function carries the professional 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-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 corporate-banking in-house legal 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.

Compliance officers at corporate banks operating under the Consumer Duty are increasingly using AI to validate the scope boundary between in-scope SME retail customers and out-of-scope large-risk commercial customers, update Dear CEO letter expectation registers in light of FS25/2, and reconcile FCA Feedback Statements against existing supervisory correspondence on relationship-managed accounts. The work product feeds into the bank's compliance monitoring plan and the SME-segment supervisory dialogue.

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: Hedge in Place of Verified FS25/2 Figure, Refusal to Confirm a Documented FS25/2 Count, Invented Dual-Event Timeline for a Single FS25/2 Withdrawal. 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 corporate-banking compliance teams sign off on.

For corporate-banking compliance, the operational consequence is direct. The compliance monitoring plan, the SME-segment supervisory dialogue, and the Dear CEO letter expectation register all rest on accurate framing of scope boundaries and of recent FCA Feedback Statements such as FS25/2. A defect imported from AI work product surfaces on supervisory follow-up, and the function carries the regulatory exposure.

Citation IDs for the findings in this brief: 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-Q020-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 corporate-banking compliance 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.

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.

In-house legal teams at payment institutions and e-money firms operating under the Consumer Duty are increasingly using AI to validate Principle 12 scope opinions for retail-customer-facing services, draft scope memos on PRIN 2A exclusions, and prepare director briefings on FCA Feedback Statements such as FS25/2. The work product sits at the centre of new-product legal opinions, scope-of-application memos, and supervisor-correspondence drafting.

Two frontier AI models tested by the RLB Specialist Panel produced 4 substantive failures on this regulation under audit conditions. The failure classes recorded are: Misstated Statutory Architecture, 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 payment-institutions in-house legal teams sign off on.

For payment-institutions legal, the operational consequence is direct. Scope-of-application memos, new-product legal opinions, and director attestations on Consumer Duty applicability all rest on accurate PRIN 2A scope and FSMA-statute framing. A defect imported from AI work product surfaces on legal-file review or board challenge, and the in-house function carries the professional 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-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 payment-institutions in-house legal 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.

Risk teams at retail banks operating under the Consumer Duty are increasingly using AI to update foreseeable-harm matrices, draft fair-value risk assessments for product committees, and validate the bank's customer-outcome KPIs against the FCA's stated expectations. The work product feeds directly into the bank's Consumer Duty risk register and the executive-risk-committee dashboard the supervisor sees.

Two frontier AI models tested by the RLB Specialist Panel produced 4 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. 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 retail-banking risk teams sign off on.

For retail-banking risk, the operational consequence is direct. The Consumer Duty risk register, the foreseeable-harm matrix, and the executive-risk-committee dashboard all rest on accurate PRIN 2A framing. A defect imported from AI work product surfaces in supervisor dashboard review or internal-audit, 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-Q007-Sonnet46, 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 retail-banking 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.