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Retail Banking × Compliance — International / Multilateral · updated 2026-06-04
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Finding#3 — Invented per-recommendation stakeholder targeting

RLB Citation ID: RLB-F-INT-BIS-CPMI-API-HARMONISATION-CROSS-BORDER-2024-Q008
AI's failure:Exposed Fabrication Risk for Retail Banking × Compliance:Wrong deliverable
What the RLB Specialist Panel found
Question (paraphrased to protect IP)

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.

RLB's analysis

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.

AI Head's analysis — what weakness in the AI model caused this

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.

Impact for Compliance Teams in Retail Banking Sector in international jurisdictions working with the Promoting the Harmonisation of Application Programming Interfaces to Enhance Cross-Border Payments: Recommendations and Toolkit

AI produced category-level stakeholder assignments for the ten CPMI recommendations — asserting, for example, that certain recommendation categories exclusively target standards bodies and public authorities rather than commercial banks — without any accessible primary-source basis. A Compliance team that uses this output to scope which recommendations create obligations for its retail bank as a participant, versus those directed at payment system operators or standards bodies, will either over-scope (building unnecessary controls) or under-scope (missing genuine bank-level obligations). Under-scoping in a regulatory mapping exercise that feeds a product launch or correspondent banking onboarding framework creates direct regulatory enforcement exposure.

References — raw findings (per AI model)
This finding also affects
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Cite this finding

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.

RLB Citation ID: RLB-F-INT-BIS-CPMI-API-HARMONISATION-CROSS-BORDER-2024-Q008
Plain text Download
RegLeg Specialist Panel (2026). "Finding#3 — Invented per-recommendation stakeholder targeting — Retail Banking × Compliance — 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/retail_banking/compliance/finding/INT-BIS-CPMI-INT-001-CPMI-API-HARMONISATION-CROSS-BORDER-2024-v1-008/
APA 7th edition Download
RegLeg Specialist Panel. (2026). Finding#3 — Invented per-recommendation stakeholder targeting [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/retail_banking/compliance/finding/INT-BIS-CPMI-INT-001-CPMI-API-HARMONISATION-CROSS-BORDER-2024-v1-008/
Bluebook / OSCOLA (US + UK legal) Download
RegLeg Specialist Panel, Finding#3 — Invented per-recommendation stakeholder targeting [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/retail_banking/compliance/finding/INT-BIS-CPMI-INT-001-CPMI-API-HARMONISATION-CROSS-BORDER-2024-v1-008/.
BibTeX Download
@misc{reglegbrief_RLB_F_INT_BIS_CPMI_API_HARMONISATION_CROSS_BORDER_2024_Q008,
  author    = {RegLeg Specialist Panel},
  title     = {Finding#3 — Invented per-recommendation stakeholder targeting},
  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/retail_banking/compliance/finding/INT-BIS-CPMI-INT-001-CPMI-API-HARMONISATION-CROSS-BORDER-2024-v1-008/}
}
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