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, not as a reasoned extrapolation from category names.
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 this document appears to lack per-recommendation 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.
When a Risk team member uses an AI-generated stakeholder matrix to map which of the 10 CPMI API harmonisation recommendations create obligations for the firm versus for payment system operators or standards bodies, a fabricated attribution table routes the firm's compliance scope analysis in the wrong direction from the outset — misidentifying whether a gap is the firm's to close or a systemic issue to monitor.
For a Payment Institution operating across multiple international jurisdictions, that scoping error is not contained to one market: it propagates into regulatory obligation registers, product-launch risk assessments, and board risk reports across all in-scope jurisdictions simultaneously. The AI's willingness to retract when challenged provides no protection if the review workflow does not include a challenge step; and given that the full recommendation-level detail is in the inaccessible PDF, a reviewer without direct access to primary sources has no independent basis to flag the error.
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 (2026). "Finding#1 — AI fabricates recommendation-level stakeholder attribution — Payment Institutions × Risk — International / Multilateral." Citation ID: RLB-F-INT-BIS-CPMI-API-HARMONISATION-CROSS-BORDER-2024-Q008. RegLegBrief AI Hallucination Research, published 2026-06-04. https://reglegbrief.com/regulators/j1/int/bis-cpmi/cpmi-api-harmonisation-cross-border-2024/sectors/payment_institutions/risk/finding/INT-BIS-CPMI-INT-001-CPMI-API-HARMONISATION-CROSS-BORDER-2024-v1-008/
RegLeg Specialist Panel. (2026). Finding#1 — AI fabricates recommendation-level stakeholder attribution [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/sectors/payment_institutions/risk/finding/INT-BIS-CPMI-INT-001-CPMI-API-HARMONISATION-CROSS-BORDER-2024-v1-008/
RegLeg Specialist Panel, Finding#1 — AI fabricates recommendation-level stakeholder attribution [RLB-F-INT-BIS-CPMI-API-HARMONISATION-CROSS-BORDER-2024-Q008], RegLegBrief AI Hallucination Research (June 04, 2026), https://reglegbrief.com/regulators/j1/int/bis-cpmi/cpmi-api-harmonisation-cross-border-2024/sectors/payment_institutions/risk/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#1 — AI fabricates recommendation-level stakeholder attribution},
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/sectors/payment_institutions/risk/finding/INT-BIS-CPMI-INT-001-CPMI-API-HARMONISATION-CROSS-BORDER-2024-v1-008/}
}