A market briefing on the global fast payment system landscape needs CPMI data on how many domestic fast payment systems are currently operational globally, how many have already enabled cross-border payment exchanges, how many are planning cross-border linkages, and what proportion are operated by central banks versus private entities.
The model substituted figures from a 2025 monitoring survey — a different publication with a different scope and vintage — for the named speech's specific statistics. The ownership breakdown (40% central banks, 35% private entities) from the 2023 speech is absent from the model's response entirely, displaced by the later monitoring survey count. The retrieval pipeline appears to have ranked the more recent survey over the earlier speech, causing the model to answer with the wrong data source for the question asked.
The substitution of a 2025 monitoring survey count for the correct 2023 speech statistics indicates the retrieval ranker weighted recency over source-match relevance. When the question asks for specific named statistics from a specific named speech, the ranker should surface that speech — not the most recent document that contains related numeric data. The retrieval-routing signal for 'named source in query' vs. 'topic in query' appears not to be differentiated.
A market briefing on the global fast payment system landscape was asked to include the proportion of fast payment systems operated by central banks versus private entities. The response correctly cited the 70+ global systems, 14 already cross-border, and 24 planning links — but falsely stated the ownership breakdown was not available in public CPMI sources, when the November 2023 CPMI speech by Tara Rice explicitly gives 40% central bank-operated and 35% privately operated.
The model retrieved three statistics from the speech correctly but failed to retrieve the ownership breakdown that appears in the same source. The response asserted the ownership data was "not enumerated" in public sources — a false negative. The retrieval pipeline appears to have indexed the summary-level numeric data from the speech but dropped the ownership-breakdown figures, causing the model to report absence of data that is present in the accessible regulator record.
The false-negative on ownership breakdown — asserting the 40%/35% figures were 'not enumerated in public sources' when they appear in the same speech the model successfully retrieved other statistics from — points to a partial-retrieval completeness failure. The retrieval pipeline indexed some sentences from the speech but dropped the ownership-breakdown paragraph. The model's self-check did not compare its 'not found' assertion against the full retrieved text of the already-confirmed source.
When a Risk team at a Statutory Boards & Agencies firm uses AI to source CPMI fast payment system landscape data for a market briefing or regulatory mapping exercise, the AI produces two compounding errors: it presents a monitoring-survey respondent count as the global operational universe figure, and it falsely asserts that the central bank / private operator split is absent from accessible public CPMI sources. A briefing or executive summary carrying these errors misrepresents both the scale and the governance structure of the global FPS ecosystem to internal decision-makers or external counterparts.
In a sector where Risk functions routinely produce analysis shared with or submitted to regulatory bodies — including the very bodies that publish the source data — a demonstrably wrong CPMI figure traceable to AI research undermines the firm's credibility and may require formal correction, with the reputational and relationship costs that follow in a closely supervised environment.
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 — Global FPS count and operator breakdown data — Statutory Boards Agencies × Risk — International / Multilateral." Citation ID: RLB-F-INT-BIS-CPMI-API-HARMONISATION-CROSS-BORDER-2024-Q010. RegLegBrief AI Hallucination Research, published 2026-06-04. https://reglegbrief.com/regulators/j1/int/bis-cpmi/cpmi-api-harmonisation-cross-border-2024/sectors/statutory_boards_agencies/risk/finding/INT-BIS-CPMI-INT-001-CPMI-API-HARMONISATION-CROSS-BORDER-2024-v1-010/
RegLeg Specialist Panel. (2026). Finding#1 — Global FPS count and operator breakdown data [Hallucination finding RLB-F-INT-BIS-CPMI-API-HARMONISATION-CROSS-BORDER-2024-Q010]. RegLegBrief AI Hallucination Research. https://reglegbrief.com/regulators/j1/int/bis-cpmi/cpmi-api-harmonisation-cross-border-2024/sectors/statutory_boards_agencies/risk/finding/INT-BIS-CPMI-INT-001-CPMI-API-HARMONISATION-CROSS-BORDER-2024-v1-010/
RegLeg Specialist Panel, Finding#1 — Global FPS count and operator breakdown data [RLB-F-INT-BIS-CPMI-API-HARMONISATION-CROSS-BORDER-2024-Q010], RegLegBrief AI Hallucination Research (June 04, 2026), https://reglegbrief.com/regulators/j1/int/bis-cpmi/cpmi-api-harmonisation-cross-border-2024/sectors/statutory_boards_agencies/risk/finding/INT-BIS-CPMI-INT-001-CPMI-API-HARMONISATION-CROSS-BORDER-2024-v1-010/.
@misc{reglegbrief_RLB_F_INT_BIS_CPMI_API_HARMONISATION_CROSS_BORDER_2024_Q010,
author = {RegLeg Specialist Panel},
title = {Finding#1 — Global FPS count and operator breakdown data},
year = {2026},
publisher = {RegLegBrief AI Hallucination Research},
note = {Hallucination finding Citation ID: RLB-F-INT-BIS-CPMI-API-HARMONISATION-CROSS-BORDER-2024-Q010},
url = {https://reglegbrief.com/regulators/j1/int/bis-cpmi/cpmi-api-harmonisation-cross-border-2024/sectors/statutory_boards_agencies/risk/finding/INT-BIS-CPMI-INT-001-CPMI-API-HARMONISATION-CROSS-BORDER-2024-v1-010/}
}