AI Hallucination ResearchFindings by audienceSectorsInternational / MultilateralInvestment BankingRisk › Streamlining Variation Margin in Centrally Cleared Markets — Examples of Effective Practices
Investment Banking × Risk — International / Multilateral · Last updated 14 Jun 2026 · methodology v2.3 · Hallucination Register
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AI Hallucination on CPMI-IOSCO Variation Margin Effective Practices for Risk teams at Investment Banking firms in International

Investment Banking Risk teams: documentation and reporting gaps possible from AI reading of CPMI-IOSCO VM Effective Practices 2025

Risk teams inside investment-banking divisions acting as clearing members at central counterparties are increasingly using AI to draft variation margin policy updates referencing CPMI-IOSCO d226, prepare board risk committee briefings on intraday VM call obligations, classify each d226 effective practice in the firm's regulatory-change inventory, and validate proposed amendments to the firm's liquidity risk policy against d226 language. Leading AI assistants tested by the RLB Specialist Panel produced confident, citable answers on the binding force of d226 that the document itself directly contradicts.

The RLB Specialist Panel tested whether two frontier AI models could correctly characterise the legal status of d226, asking them to classify each of the eight effective practices set out in the document as either a mandatory requirement with enforcement consequences, a supervisory expectation that regulators will test against, or voluntary guidance with no binding legal force. The exercise targeted what the Panel calls inverted modality: AI commitments that flip the binding force of a source text from voluntary illustration to supervisory or mandatory rule.

The frontier model under test produced a complete compliance obligations memo that classified every one of the eight effective practices as either a supervisory expectation in its own right or as overlapping with mandatory national rules, with a threshold classification asserting that d226 carries "a strong gravitational pull into (B) SUPERVISORY EXPECTATION." The document's own stated purpose paragraph, by contrast, records that d226 sets out "examples of how standards set out in the CPMI-IOSCO Principles for financial market infrastructures, as supplemented by the relevant guidance, can be met."

For Clearing-member Risk, the operational consequence is direct. Policy updates and board risk committee briefings that characterise d226 practices as supervisory expectations or mandatory obligations push the firm toward implementation timelines and liquidity buffers calibrated against a non-binding publication, distorting the risk function's capacity to focus on the underlying PFMI Principles and national rulebook obligations that drive actual supervisory examination. The pattern is also reproducible: it surfaces wherever a deliverable asks the model to commit to a legal characterisation of an international standard-setter publication, and it is not addressed by general-purpose prompting.

The RLB Specialist Panel records the finding under the misstated-rule failure category and binds it to verbatim regulator text drawn from the d226 final report held as primary substrate.

The full finding is recorded under Citation ID RLB-H-INT-BIS-CPMI-CPMI-IOSCO-VARIATION-MARGIN-CCPs-2025-Q004-Opus47. The regulation hub is at /regulators/j1/INT/BIS-CPMI-INT-001/CPMI-IOSCO-VARIATION-MARGIN-CCPs-2025/. Questions are prepared by the RLB Specialist Panel based on real practical AI usage in the workflows the respective audience uses AI for. The Panel binds each AI finding to verbatim regulator-issued source text held as primary substrate.

AI mis-frames CPMI-IOSCO d226 effective practices as supervisory or mandatory obligations when the report itself describes them as non-binding examples of how PFMI standards can be met.

Executive Summary

Risk teams inside investment-banking divisions acting as clearing members at central counterparties are increasingly using AI to draft variation margin policy updates referencing CPMI-IOSCO d226, prepare board risk committee briefings on intraday VM call obligations, classify each d226 effective practice in the firm's regulatory-change inventory, and validate proposed amendments to the firm's liquidity risk policy against d226 language. Leading AI assistants tested by the RLB Specialist Panel produced confident, citable answers on the binding force of d226 that the document itself directly contradicts.

This cell collects one hallucination finding on the January 2025 CPMI-IOSCO publication "Streamlining variation margin in centrally cleared markets, examples of effective practices" (d226), organised for Clearing-member Risk working on CPMI-IOSCO VM matters in international and cross-border contexts. Across the finding, the AI subject in this audit produced a confident, citable response on the legal status of the eight effective practices in d226 that the document itself directly contradicts.

The Specialist Panel found that the AI classified each practice as a supervisory expectation or as carrying mandatory overlap with national rules, when the document's own stated purpose paragraph records it as "examples of how standards set out in the CPMI-IOSCO Principles for financial market infrastructures, as supplemented by the relevant guidance, can be met." Every finding in this cell is bound to verbatim regulator-issued source text held as primary substrate by the RLB Specialist Panel.

When this affects Clearing-member Risk work

This pattern surfaces whenever Clearing-member Risk use AI to characterise the binding force of d226 in variation margin policy updates, board risk committee briefings, and regulatory-change inventory entries. The risk is structural: d226 is a CPMI-IOSCO publication that frames itself as voluntary effective-practice illustration, yet the AI's commitment reads as a supervisory or mandatory classification that a practitioner would paste into a deliverable before verification against the source. The deliverable then carries forward an inverted modality on binding force into downstream work products.

Findings overview

#Finding titleTypeCitation ID
1Inverted modality on binding forceMisstated ruleRLB-H-INT-BIS-CPMI-CPMI-IOSCO-VARIATION-MARGIN-CCPs-2025-Q004-Opus47

What the AI got wrong

Finding 1: Inverted modality on binding force of d226 effective practices

Citation ID: RLB-H-INT-BIS-CPMI-CPMI-IOSCO-VARIATION-MARGIN-CCPs-2025-Q004-Opus47

In response to a Specialist Panel application-style question asking for a compliance obligations memo classifying each of the eight effective practices in d226 as either (A) MANDATORY REQUIREMENT, (B) SUPERVISORY EXPECTATION, or (C) VOLUNTARY GUIDANCE, the AI subject produced a complete memo that treated every one of the eight effective practices as either a supervisory expectation in its own right or as overlapping with mandatory national rules.

The memo opened with a threshold classification that placed d226 in category (C) but immediately added "a strong gravitational pull into (B) SUPERVISORY EXPECTATION because the underlying PFMIs are the de facto binding standard against which CCPs are supervised," then classified each practice individually under (B), (B) trending (C), (B) with (A) elements, or similar mixed labels.

The document's own stated purpose paragraph, by contrast, records that d226 sets out "examples of how standards set out in the CPMI-IOSCO Principles for financial market infrastructures, as supplemented by the relevant guidance, can be met." The text is illustrative of one way the underlying PFMI standards can be met, and is not a new layer of supervisory expectation or mandatory rule. The AI subject's framing inverts that modality: it converts a voluntary illustration into a baseline supervisory expectation, with each practice acquiring its own binding-force overlay.

The Specialist Panel records this as a misstated-rule finding because the AI's commitment on the legal status of d226 contradicts the document's own characterisation of itself. The full finding analysis, including the verbatim regulator text, the AI subject's complete answer, and the AI's failure mode classification, is at See full finding analysis.

Policy updates and board risk committee briefings that characterise d226 practices as supervisory expectations or mandatory obligations push the firm toward implementation timelines and liquidity buffers calibrated against a non-binding publication, distorting the risk function's capacity to focus on the underlying PFMI Principles and national rulebook obligations that drive actual supervisory examination.

AI's failure mode

#Finding titleTypeCitation ID
1Inverted modality on binding forceMisstated ruleRLB-H-INT-BIS-CPMI-CPMI-IOSCO-VARIATION-MARGIN-CCPs-2025-Q004-Opus47

Risk impact

#Finding titleTypeCitation ID
1Inverted modality on binding forceMisstated ruleRLB-H-INT-BIS-CPMI-CPMI-IOSCO-VARIATION-MARGIN-CCPs-2025-Q004-Opus47

What Clearing-member Risk teams should do

  • Classify d226 in the firm's regulatory-change inventory as a CPMI-IOSCO voluntary publication of effective practices, with a separate line for the underlying PFMI Principles and any national rule that incorporates a specific practice.
  • Before drafting a VM policy update that cites d226, verify the document's own framing as "examples of how standards … can be met" so the policy does not overstate the binding force of the source.
  • In board risk committee briefings, present d226 as a benchmark against which the firm's existing VM policy can be assessed, not as a new rule that the board must adopt.
  • Where AI-generated risk inventory entries label d226 as supervisory or mandatory, return them to the regulatory-change desk for re-tagging.
  • Coordinate the firm's d226 position with the legal function so policy, briefing, and disclosure language are aligned on binding force.

Right of Reply

These findings and associated work have been put up in public with a view of the greater good for the development of a safer AI ecosystem. Any party reading this or any finding on reglegbrief.com may contact us and have an unconditional right of reply; the Specialist Panel will publish any factual correction or contextual response alongside the original finding, with no editorial gatekeeping. Researchers, regulators, and compliance teams with questions on methodology or specific findings can reach the Specialist Panel via the same channel.

Source & Methodology Standards

RegLeg Brief is operated by Verdus Technologies Pte. Ltd. (UEN 201616982R), incorporated in Singapore. The RLB Specialist Panel, with an aggregate of over 60 years of public-policy and industry experience, documents only confirmed hallucination findings, under a methodology that requires a verbatim regulator excerpt for every documented claim. All findings, citation IDs, model outputs, regulator excerpts, and methodology notes are open-access.

Primary source verified: d226 — Streamlining variation margin in centrally cleared markets — examples of effective practices (January 2025). R-folder reference: R6-FINAL_REPORT-00001. BIS portal: bis.org/cpmi.

Citation IDs referenced:

  • RLB-H-INT-BIS-CPMI-CPMI-IOSCO-VARIATION-MARGIN-CCPs-2025-Q004-Opus47

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.