A compliance analyst asks which of the 10 CPMI API harmonisation recommendations specifically target commercial banks or correspondent banking institutions, which target payment system operators, which target central banks or regulators, and which target standards bodies, seeking a recommendation-by-recommendation stakeholder breakdown.
With no retrievable per-recommendation content, the model inferred stakeholder assignments from the recommendation category names and its knowledge of how standards-body governance typically works. BIAN, ISO, and SWIFT appear as plausible assignments to a harmonisation-processes category without any retrieved basis. The model presented this inference as a stakeholder breakdown rather than as reasoned extrapolation from category names, and the structured taxonomic format gave the inference the surface register of a retrieved factual breakdown.
Domain inference used as a stakeholder-assignment mechanism — assigning ISO, BIAN, and SWIFT to a harmonisation-processes category by structural reasoning — is not retrieval. The training data for the CPMI October 2024 recommendations PDF appears to lack the per-recommendation stakeholder content, and the model's self-check did not flag that its output was constructed rather than retrieved. The RAG glue layer is not enforcing a 'content was found' gate before allowing domain-inference fill. Worse, the structured presentation format — a roman-numeral taxonomy with named bodies attached — gives the inference output the visual register of a retrieved factual breakdown.
For an AI lab, this is a generation-calibration probe: when the model produces structured taxonomic output on a regulatory document whose primary text was not in retrieval, the structure itself should signal inference and the response should explicitly say so.
A lawyers mapping the 10 CPMI recommendations to stakeholder obligations, for product scoping, regulatory submissions, or correspondent banking impact assessments, asks Claude Opus 4.7 for a recommendation-by-recommendation stakeholder breakdown. The AI returns a structured taxonomy that names ISO, BIAN, SWIFT and other bodies against specific recommendation groupings. The taxonomy is built from category names and domain priors, not from the regulator's recommendation text, which the AI could not retrieve. A lawyers acting on that breakdown carries fabricated stakeholder assignments into internal scope documents, with no signal that the underlying primary-source extraction never happened.
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.
RegLeg Specialist Panel, Finding#3, Per-recommendation stakeholder taxonomy fabricated [RLB-F-INT-BIS-CPMI-API-HARMONISATION-CROSS-BORDER-2024-Q008], RegLegBrief AI Hallucination Research (June 11, 2026), https://reglegbrief.com/regulators/j1/INT/BIS-CPMI/CPMI-API-HARMONISATION-CROSS-BORDER-2024/practitioners/lawyers/finding/INT-BIS-CPMI-INT-001-CPMI-API-HARMONISATION-CROSS-BORDER-2024-v1-008/.
RegLeg Specialist Panel (2026). "Finding#3, Per-recommendation stakeholder taxonomy fabricated — Practitioners — Lawyers." Citation ID: RLB-F-INT-BIS-CPMI-API-HARMONISATION-CROSS-BORDER-2024-Q008. RegLegBrief AI Hallucination Research, published 2026-06-11. https://reglegbrief.com/regulators/j1/INT/BIS-CPMI/CPMI-API-HARMONISATION-CROSS-BORDER-2024/practitioners/lawyers/finding/INT-BIS-CPMI-INT-001-CPMI-API-HARMONISATION-CROSS-BORDER-2024-v1-008/
RegLeg Specialist Panel. (2026). Finding#3, Per-recommendation stakeholder taxonomy fabricated [Hallucination finding RLB-F-INT-BIS-CPMI-API-HARMONISATION-CROSS-BORDER-2024-Q008]. RegLegBrief AI Hallucination Research. https://reglegbrief.com/regulators/j1/INT/BIS-CPMI/CPMI-API-HARMONISATION-CROSS-BORDER-2024/practitioners/lawyers/finding/INT-BIS-CPMI-INT-001-CPMI-API-HARMONISATION-CROSS-BORDER-2024-v1-008/
@misc{reglegbrief_RLB_F_INT_BIS_CPMI_API_HARMONISATION_CROSS_BORDER_2024_Q008,
author = {RegLeg Specialist Panel},
title = {Finding#3, Per-recommendation stakeholder taxonomy fabricated},
year = {2026},
publisher = {RegLegBrief AI Hallucination Research},
note = {Hallucination finding Citation ID: RLB-F-INT-BIS-CPMI-API-HARMONISATION-CROSS-BORDER-2024-Q008},
url = {https://reglegbrief.com/regulators/j1/INT/BIS-CPMI/CPMI-API-HARMONISATION-CROSS-BORDER-2024/practitioners/lawyers/finding/INT-BIS-CPMI-INT-001-CPMI-API-HARMONISATION-CROSS-BORDER-2024-v1-008/}
}
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.