What percentage of faster payment systems and RTGS systems currently use ISO 20022 messaging, according to CPMI monitoring data?
The regulator's record gives two distinct figures — faster payment systems and RTGS systems are separately characterised, with RTGS adoption described as approaching half. The model collapsed these into a single blended percentage applied to both system types simultaneously. The 79% figure appears to be an internally-reconstructed composite; it matches neither the faster-payment nor the RTGS figure in the official record. The failure is silent — the model expressed no uncertainty about the figure it produced.
This failure implicates the training corpus's handling of subcategory-level numeric claims from official-speech channels. The model produced a single blended 79% figure where the regulator's March 2026 speech gives two distinct values — one for faster payment systems and a substantially lower one for RTGS. This suggests the speech content either was not retrieved or was compressed during ingestion in a way that averaged across the two system-type categories. If your eval suite tests adoption-rate questions at the aggregate level only, this failure is invisible; the gap is specifically at subcategory resolution.
Advising a client on the current state of ISO 20022 adoption using a single 79% figure — when the authoritative source reports materially lower RTGS adoption than FPS adoption — misrepresents the market in a way that matters for timing advice, benchmarking arguments, and regulatory comparisons across system types. When the AI was challenged on this figure, it acknowledged it was reconstructed and likely conflated across years, meaning there is no underlying source the lawyer could trace the number back to.
Any regulatory submission or opinion that includes adoption statistics derived from this response would be built on an invented figure.
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#2 — FPS and RTGS adoption rates conflated into a single wrong figure [RLB-F-INT-BIS-CPMI-ISO-20022-HARMONISATION-UPDATED-2026-Q006], RegLegBrief AI Hallucination Research (June 04, 2026), https://reglegbrief.com/regulators/j1/int/bis-cpmi/cpmi-iso-20022-harmonisation-updated-2026/practitioners/lawyers/finding/INT-BIS-CPMI-INT-001-CPMI-ISO-20022-HARMONISATION-UPDATED-2026-v1-006/.
RegLeg Specialist Panel (2026). "Finding#2 — FPS and RTGS adoption rates conflated into a single wrong figure — Practitioners — Lawyers." Citation ID: RLB-F-INT-BIS-CPMI-ISO-20022-HARMONISATION-UPDATED-2026-Q006. RegLegBrief AI Hallucination Research, published 2026-06-04. https://reglegbrief.com/regulators/j1/int/bis-cpmi/cpmi-iso-20022-harmonisation-updated-2026/practitioners/lawyers/finding/INT-BIS-CPMI-INT-001-CPMI-ISO-20022-HARMONISATION-UPDATED-2026-v1-006/
RegLeg Specialist Panel. (2026). Finding#2 — FPS and RTGS adoption rates conflated into a single wrong figure [Hallucination finding RLB-F-INT-BIS-CPMI-ISO-20022-HARMONISATION-UPDATED-2026-Q006]. RegLegBrief AI Hallucination Research. https://reglegbrief.com/regulators/j1/int/bis-cpmi/cpmi-iso-20022-harmonisation-updated-2026/practitioners/lawyers/finding/INT-BIS-CPMI-INT-001-CPMI-ISO-20022-HARMONISATION-UPDATED-2026-v1-006/
@misc{reglegbrief_RLB_F_INT_BIS_CPMI_ISO_20022_HARMONISATION_UPDATED_2026_Q006,
author = {RegLeg Specialist Panel},
title = {Finding#2 — FPS and RTGS adoption rates conflated into a single wrong figure},
year = {2026},
publisher = {RegLegBrief AI Hallucination Research},
note = {Hallucination finding Citation ID: RLB-F-INT-BIS-CPMI-ISO-20022-HARMONISATION-UPDATED-2026-Q006},
url = {https://reglegbrief.com/regulators/j1/int/bis-cpmi/cpmi-iso-20022-harmonisation-updated-2026/practitioners/lawyers/finding/INT-BIS-CPMI-INT-001-CPMI-ISO-20022-HARMONISATION-UPDATED-2026-v1-006/}
}