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.
What share of faster payment systems and RTGS systems are currently using ISO 20022, according to CPMI monitoring data from the December 2024 survey?
The model collapsed two distinct statistics into a single symmetric figure, reporting the same 79% adoption rate for both faster payment systems and RTGS systems. The regulator's survey data shows materially different adoption rates across these two system types, faster payments substantially ahead of RTGS, a disaggregation the model's response erased entirely. The fabricated third-party citation suggests the model's retrieval pulled from a secondary source that had itself misreported the figures, and the model treated that paraphrased summary as authoritative without flagging the discrepancy.
This failure mirrors the Opus 4.7 conflation on the same adoption-rate question — the model produced an identical blended 79% figure applied symmetrically to faster payment systems and RTGS systems, where the regulator's record gives two distinct figures that diverge by roughly thirty percentage points. The Sonnet variant adds a fabricated third-party citation pointing to a centralbanking.com URL that does not contain the figure attributed to it, suggesting the retrieval pipeline surfaced a paraphrased secondary source and the model treated that summary as authoritative without flagging the discrepancy.
The cross-model recurrence is the load-bearing signal: where both Opus and Sonnet produce the same composite shape on a subcategory-disaggregated regulator statistic, the gap is in the retrieval ranker's weighting of official-speech channels, not in a single model's calibration.
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, ISO 20022 adoption rate conflation: RTGS vs faster payments (Opus 4.7) — Payment Institutions × Compliance — International / Multilateral." Citation ID: RLB-F-INT-BIS-CPMI-ISO-20022-HARMONISATION-UPDATED-2026-Q006. RegLegBrief AI Hallucination Research, published 2026-06-11. https://reglegbrief.com/regulators/j1/int/BIS-CPMI/CPMI-ISO-20022-HARMONISATION-UPDATED-2026/sectors/payment_institutions/compliance/finding/INT-BIS-CPMI-INT-001-CPMI-ISO-20022-HARMONISATION-UPDATED-2026-v1-006/
RegLeg Specialist Panel. (2026). Finding#1, ISO 20022 adoption rate conflation: RTGS vs faster payments (Opus 4.7) [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/sectors/payment_institutions/compliance/finding/INT-BIS-CPMI-INT-001-CPMI-ISO-20022-HARMONISATION-UPDATED-2026-v1-006/
RegLeg Specialist Panel, Finding#1, ISO 20022 adoption rate conflation: RTGS vs faster payments (Opus 4.7) [RLB-F-INT-BIS-CPMI-ISO-20022-HARMONISATION-UPDATED-2026-Q006], RegLegBrief AI Hallucination Research (June 11, 2026), https://reglegbrief.com/regulators/j1/int/BIS-CPMI/CPMI-ISO-20022-HARMONISATION-UPDATED-2026/sectors/payment_institutions/compliance/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#1, ISO 20022 adoption rate conflation: RTGS vs faster payments (Opus 4.7)},
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/sectors/payment_institutions/compliance/finding/INT-BIS-CPMI-INT-001-CPMI-ISO-20022-HARMONISATION-UPDATED-2026-v1-006/}
}
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.