Briefings Blog

Accountants advising on cross-border merger reviews engaged with the 2025 OECD Merger Review Recommendation are increasingly using AI to draft client briefings on transaction-screening obligations, prepare partner-level summaries of the failing firm defence evidentiary standard, and validate operative-section citations against the OECD text before signing financial-suitability opinions or transaction-cost reviews.

The RLB Specialist Panel put a set of practitioner-grade questions on the 2025 OECD Merger Review Recommendation to two frontier AI models with web search active. Each question is prepared by the Panel based on the workflows that accountants actually use AI for under the OECD's 2025 revision of the Recommendation of the Council on Merger Review (OECD/LEGAL/0333). The Panel then binds every AI response to verbatim regulator-issued source text held as primary substrate. On the 2025 OECD Merger Review Recommendation, the AI subjects returned a single hallucinated answer for accountants, in the form of Inter-Alia-to-Closed-Test Conversion.

For accountants advising on cross-border merger transactions that engage the 2025 OECD Merger Review Recommendation, the operative-section structure of the Recommendation, the failing-firm-defence evidentiary standard, and the Council-reporting cadence drive transaction-suitability opinions, due-diligence reports, and inter-agency-engagement memos. A financial-suitability opinion that frames the failing-firm-defence under a closed three-condition cumulative test produces wrong client guidance on whether the defence is worth running and on what evidence to commission. A transaction-cost review that mis-states the operative section count signals to the partner and to the client that the underlying regulatory map is unreliable, which puts the entire engagement at risk.

The published Specialist Panel findings carry the following citation identifiers:

• RLB-H-INT-OECD-OECD-MERGER-REVIEW-RECOMMENDATION-2025-Q005-Opus47 (Failing firm defence: mischaracterised condition 3 and closed-list error)
• RLB-H-INT-OECD-OECD-MERGER-REVIEW-RECOMMENDATION-2025-Q005-Sonnet46 (Failing firm defence: mischaracterised condition 3 and closed-list error)

Lawyers advising on the 2025 OECD Merger Review Recommendation are increasingly using AI to draft 2-page client memos on the Recommendation's operative structure, generate partner-level briefings on remedies hierarchy and failing firm defence standards, and validate Section-level citation language against the published OECD text before issuing legal opinions on cross-border merger strategy.

The RLB Specialist Panel put a set of practitioner-grade questions on the 2025 OECD Merger Review Recommendation to two frontier AI models with web search active. Each question is prepared by the Panel based on the workflows that lawyers actually use AI for under the OECD's 2025 revision of the Recommendation of the Council on Merger Review (OECD/LEGAL/0333). The Panel then binds every AI response to verbatim regulator-issued source text held as primary substrate.

On the 2025 OECD Merger Review Recommendation, the AI subjects returned five hallucinated answers for lawyers, in the form of Structure Inflation, Misattributed Cross-Jurisdictional Doctrine, Open-Interval Collapse, and Inter-Alia-to-Closed-Test Conversion.

For lawyers issuing legal opinions, client memos, transactional documents, and regulatory submissions that engage the 2025 OECD Merger Review Recommendation, citation accuracy on the operative architecture, on Section IV.3 remedies hierarchy, and on Section III.11.b failing firm defence is load-bearing: a counterparty, opposing counsel, or competition authority who can identify a structural omission, a misattributed sub-hierarchy, or a closed-cumulative-test framing on first reading calls the entire piece of advice into question.

An AI-drafted memo that inflates the operative section count and omits Section V, or that imports the EU fix-it-first / buyer-pool / crown-jewel sub-ordering into the OECD's text, or that converts the failing-firm-defence 'inter alia' criteria into a closed three-condition cumulative test, leaves the lawyer exposed to professional liability, the firm exposed to reputational risk, and the client exposed to a defence submission that under-prepares on additional evidentiary lines or to a remedies negotiation built on the wrong normative baseline.

The published Specialist Panel findings carry the following citation identifiers:

• RLB-H-INT-OECD-OECD-MERGER-REVIEW-RECOMMENDATION-2025-Q001-Opus47 (Invented operative architecture; ex-post section omitted)
• RLB-H-INT-OECD-OECD-MERGER-REVIEW-RECOMMENDATION-2025-Q001-Sonnet46 (Invented operative architecture; ex-post section omitted)
• RLB-H-INT-OECD-OECD-MERGER-REVIEW-RECOMMENDATION-2025-Q002-Sonnet46 (EU remedy sub-hierarchy misattributed as OECD text)
• RLB-H-INT-OECD-OECD-MERGER-REVIEW-RECOMMENDATION-2025-Q004-Sonnet46 (Two-tier reporting interval collapsed to single 5-year cycle)
• RLB-H-INT-OECD-OECD-MERGER-REVIEW-RECOMMENDATION-2025-Q005-Opus47 (Failing firm defence: third condition wrong; 'inter alia' dropped)
• RLB-H-INT-OECD-OECD-MERGER-REVIEW-RECOMMENDATION-2025-Q005-Sonnet46 (Failing firm defence: third condition wrong; 'inter alia' dropped)
• RLB-H-INT-OECD-OECD-MERGER-REVIEW-RECOMMENDATION-2025-Q006-Opus47 (Co-operation section invented; ex-post section omitted)

Risk leads at statutory boards and public agencies engaging with the CPMI API harmonisation programme are increasingly using AI to update agency-level CPMI risk dashboards, draft enterprise-risk-assessment annexes on the SARB pre-validation workstream, prepare board-risk-appetite papers on cross-border payments oversight, generate operational-risk metrics using fast payment system operator splits, and verify dated CPMI commitments against primary publications. The RLB Specialist Panel tested how that AI usage performs against the regulator's own primary text on CPMI's October 2024 d224 report and the related CPMI Brief and speech series.

The audit surfaced four substantive failure modes that the AI subjects delivered with regulator-fluent confidence.

Numeric Drift and False-Negative Availability Claim on CPMI API Harmonisation for Cross-Border Payments. Two frontier AI models tested by the RLB Specialist Panel returned confident, citable answers across the panel's CPMI substrate-bound question set on the October 2024 d224 report and the related CPMI Brief and speech series. The panel binds each AI finding to verbatim regulator-issued source text held as primary substrate.

Across the 2 findings in this Risk teams at Statutory Boards & Agencies briefing, the AI subjects returned a global fast payment system count of 57 sourced to the 2025 monitoring survey sample, when the authoritative CPMI figure is 70+; stated that the central-bank versus private operator split of global fast payment systems is not enumerated in public CPMI sources, when the November 2023 CPMI speech gives exact percentages.

A board-risk paper that records a CPMI cutover date the regulator never set is a factual error in a board-approved agency document. A risk dashboard that uses 57 rather than 70+ as the FPS connectivity baseline mis-sizes the agency's oversight scope. An enterprise risk register entry recording 'no SARB pre-validation workstream identified' carries a verifiable error into an official deliverable.

The findings are published with immutable RLB Citation IDs: RLB-H-INT-BIS-CPMI-API-HARMONISATION-CROSS-BORDER-2024-Q010-Opus47, RLB-H-INT-BIS-CPMI-API-HARMONISATION-CROSS-BORDER-2024-Q010-Sonnet46. The full audit is published at the CPMI API Harmonisation for Cross-Border Payments hub on RegLegBrief.com.

Legal teams at statutory boards and public agencies engaging with the CPMI API harmonisation programme are increasingly using AI to draft legal memos on the agency's CPMI engagement position, prepare board-paper legal annexes on the SARB pre-validation workstream, generate scoping documents for inter-agency cooperation on the 10 CPMI recommendations, validate ISO 20022 structured-address commitments against regulator text, and produce horizon-scan summaries for senior officials. The RLB Specialist Panel tested how that AI usage performs against the regulator's own primary text on CPMI's October 2024 d224 report and the related CPMI Brief and speech series.

The audit surfaced four substantive failure modes that the AI subjects delivered with regulator-fluent confidence.

Source-Credit Fabrication and Stakeholder Taxonomy Fabrication on CPMI API Harmonisation for Cross-Border Payments. Two frontier AI models tested by the RLB Specialist Panel returned confident, citable answers across the panel's CPMI substrate-bound question set on the October 2024 d224 report and the related CPMI Brief and speech series. The panel binds each AI finding to verbatim regulator-issued source text held as primary substrate.

Across the 2 findings in this Legal teams at Statutory Boards & Agencies briefing, the AI subjects downgraded a regulator-stated named partnership to a speculative hedge; built a recommendation-by-recommendation stakeholder breakdown from category names rather than the regulator's actual recommendation text.

A legal opinion that hedges the SARB pre-validation partnership as 'plausible but unverified' or that adopts an AI per-recommendation stakeholder taxonomy carries fabricated assignments into the agency's official record. A horizon-scan annex that misses the SARB-CPMI workstream positions the agency one step behind a published regulator-bilateral programme.

The findings are published with immutable RLB Citation IDs: RLB-H-INT-BIS-CPMI-API-HARMONISATION-CROSS-BORDER-2024-Q007-Opus47, RLB-H-INT-BIS-CPMI-API-HARMONISATION-CROSS-BORDER-2024-Q008-Opus47. The full audit is published at the CPMI API Harmonisation for Cross-Border Payments hub on RegLegBrief.com.

Compliance teams at statutory boards and public agencies engaging with the CPMI API harmonisation programme for cross-border payments oversight are increasingly using AI to draft inter-agency briefing notes, prepare board-paper annexes on the SARB pre-validation workstream, generate regulatory horizon-scan summaries on the 10 CPMI recommendations for senior officials, update programme-level CPMI mapping documents, and verify ISO 20022 commitments against regulator-issued source text. The RLB Specialist Panel tested how that AI usage performs against the regulator's own primary text on CPMI's October 2024 d224 report and the related CPMI Brief and speech series.

The audit surfaced four substantive failure modes that the AI subjects delivered with regulator-fluent confidence.

Confident Denial and Stakeholder Taxonomy Fabrication on CPMI API Harmonisation for Cross-Border Payments. Two frontier AI models tested by the RLB Specialist Panel returned confident, citable answers across the panel's CPMI substrate-bound question set on the October 2024 d224 report and the related CPMI Brief and speech series. The panel binds each AI finding to verbatim regulator-issued source text held as primary substrate.

Across the 2 findings in this Compliance teams at Statutory Boards & Agencies briefing, the AI subjects denied that any pilot partner has been named for the CPMI pre-validation API recommendation; built a recommendation-by-recommendation stakeholder breakdown from category names rather than the regulator's actual recommendation text.

An inter-agency briefing note that records 'no jurisdictional partner identified' on the CPMI pre-validation workstream embeds a verifiable factual error into official correspondence. A senior-official horizon scan that quotes a fabricated November 2026 structured-ISO-20022 cutover commits the agency's position to a regulator commitment the regulator never made.

The findings are published with immutable RLB Citation IDs: RLB-H-INT-BIS-CPMI-API-HARMONISATION-CROSS-BORDER-2024-Q007-Sonnet46, RLB-H-INT-BIS-CPMI-API-HARMONISATION-CROSS-BORDER-2024-Q008-Opus47. The full audit is published at the CPMI API Harmonisation for Cross-Border Payments hub on RegLegBrief.com.