AI Hallucination ResearchRegulatorsMajor advanced economiesGBFCACONSUMER-DUTY-PS22-9White paperDetail › Finding
AI Labs · published 2026-05-26 · methodology v2.1

Claude Opus 4.7 with web search

RLB-H-GB-FCA-CONSUMER-DUTY-PS22-9-Q008-Opus47
What the RLB Specialist Panel found
  • Question (paraphrased to protect IP): Does the Consumer Duty require firms to quantify non-monetary benefits as part of a fair value assessment? What methodology does the FCA expect?
  • AI's response: > "Quantification is encouraged where feasible but qualitative assessment is acceptable where quantification is impractical, provided the reasoning is robust."
  • Regulator's text: The FCA does not expect firms to quantify non-monetary costs and benefits as part of its fair value assessment process, but firms should undertake some form of qualitative assessment.
  • Why the AI went wrong: The FCA's position is a clear negative: it does not expect quantification. The model inverted this into an affirmative expectation ("encouraged where feasible"), creating a compliance standard that is stricter than the regulator's published position. The model also introduced a robustness condition on qualitative reasoning that the FCA does not specify. A compliance team reading the model's response might allocate resource to quantification exercises the FCA has explicitly said it does not require.
  • Cited source(s):
  • https://handbook.fca.org.uk/handbook/PRIN/2A/4.html — Pretextual
  • https://www.fca.org.uk/publication/finalised-guidance/fg22-5.pdf — Pretextual
  • Regulator portal (if any cited link is dud): https://www.fca.org.uk
Impact for this audience

This is a negation-reversal error on a clear FCA policy position: the regulator explicitly does not expect quantification, and the model inverted this into an affirmative expectation. This pattern — where a clean regulatory negative is reconstructed as a positive standard — is particularly dangerous for compliance users, as it creates phantom obligations. The retrieval step appears to have been ineffective: the cited FCA Handbook and FG22/5 URLs, if successfully retrieved, would have contained the correct negative statement.

References — raw findings (per AI model)
This finding also affects
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Cite this finding

Each finding has a stable Citation ID (RLB-F-… for aggregated case-study findings, RLB-H-… for raw per-model hallucinations) — like a DOI, the ID always resolves to the canonical finding even if URLs change.

Plain text
RegLeg Specialist Panel (2026). "Claude Opus 4.7 with web search — AI Labs." Citation ID: RLB-H-GB-FCA-CONSUMER-DUTY-PS22-9-Q008-Opus47. RegLegBrief AI Hallucination Research, published 2026-05-26. https://reglegbrief.com/regulators/j3/gb/fca/consumer-duty-ps22-9/whitepaper/finding/GB-FCA-GB-001-CONSUMER-DUTY-PS22-9-v1-008--opus-47-websearch/
APA 7th edition
RegLeg Specialist Panel. (2026). Claude Opus 4.7 with web search [Hallucination finding RLB-H-GB-FCA-CONSUMER-DUTY-PS22-9-Q008-Opus47]. RegLegBrief AI Hallucination Research. https://reglegbrief.com/regulators/j3/gb/fca/consumer-duty-ps22-9/whitepaper/finding/GB-FCA-GB-001-CONSUMER-DUTY-PS22-9-v1-008--opus-47-websearch/
Bluebook / OSCOLA (US + UK legal)
RegLeg Specialist Panel, Claude Opus 4.7 with web search [RLB-H-GB-FCA-CONSUMER-DUTY-PS22-9-Q008-Opus47], RegLegBrief AI Hallucination Research (May 26, 2026), https://reglegbrief.com/regulators/j3/gb/fca/consumer-duty-ps22-9/whitepaper/finding/GB-FCA-GB-001-CONSUMER-DUTY-PS22-9-v1-008--opus-47-websearch/.
BibTeX
@misc{reglegbrief_RLB_H_GB_FCA_CONSUMER_DUTY_PS22_9_Q008_Opus47,
  author    = {RegLeg Specialist Panel},
  title     = {Claude Opus 4.7 with web search},
  year      = {2026},
  publisher = {RegLegBrief AI Hallucination Research},
  note      = {Hallucination finding Citation ID: RLB-H-GB-FCA-CONSUMER-DUTY-PS22-9-Q008-Opus47},
  url       = {https://reglegbrief.com/regulators/j3/gb/fca/consumer-duty-ps22-9/whitepaper/finding/GB-FCA-GB-001-CONSUMER-DUTY-PS22-9-v1-008--opus-47-websearch/}
}
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