Briefings Blog

Legal teams at Payment Institutions firms working on the CPMI-IOSCO Principles for Financial Market Infrastructures (PFMI, 2012) are increasingly relying on AI to draft legal opinions on FMI third-party oversight obligations, structure CSP-mandate contracts and contractual flow-down clauses, prepare counterparty representations on PFMI Annex F compliance, and validate supervisor-engagement scope language against the regulator's Annex F text.

The PFMI framework is the global standard for systemically important payment systems, central counterparties, and securities settlement infrastructures, and the document's structure makes it particularly amenable to AI summarisation: numbered Principles, numbered Key Considerations, and lettered annexes that the model can address by number.

That surface structure is also what makes the failure mode the RegLeg Brief Specialist Panel records here invisible at runtime: the document is regularly cited by Key Consideration number in board papers, disclosure-framework returns, and counterparty representations, which means a misattributed citation does not register as a substantive error in the draft, it registers as a competent regulatory paragraph that the reader will not check against the regulator's primary text unless something else prompts the verification.

Two frontier AI models tested by the RegLeg Brief Specialist Panel produced confidently wrong reconstructions of the PFMI's governance and oversight architecture under Principle 2 (governance) and Annex F (oversight expectations for critical service providers). The Panel records one finding in the class the team labels "Supervisor-Scope Inversion", in which the models stated a substantively plausible governance position and pinned it to a named Key Consideration that the published PFMI text does not support. The finding identifiers are RLB-H-INT-BIS-CPMI-IOSCO-PFMI-2012-Q011-Sonnet46.

For Legal teams at Payment Institutions firms, the failure shape matters because the work product is legal opinions on FMI third-party oversight, CSP-mandate contract structures, counterparty representations on PFMI Annex F compliance, and supervisor-engagement scope memoranda, all of which travel under the firm's name to a board, supervisor, counterparty, or public reviewer who can locate the cited Key Consideration and check it against the regulator's primary text.

Legal teams at payment institutions signing off on opinions or contract structures for CSP mandates are the population most exposed when AI output documents the supervisory relationship as purely contractual and FMI-internal, because the opinion carries the firm's name to a counterparty or supervisor who can locate Annex F and see the parallel regulator-to-CSP oversight channel the text contemplates.

The Panel documents the finding identifiers RLB-H-INT-BIS-CPMI-IOSCO-PFMI-2012-Q011-Sonnet46. The AI subjects under test were Claude Sonnet 4.6, each running with web search enabled, mirroring the workflow most practitioners run when they ask an assistant a Principle 2 or Annex F question. The verbatim regulator text is held as primary substrate (R2-REGULATION-d101a_PFMI_main_text.pdf). Each finding card sets out the exact strings the model produced, the verbatim regulator excerpt the model's output contradicts, and the failure-class label the RegLeg Brief Specialist Panel assigns.

The records are open-access; AI labs named in any finding have an unconditional right of reply, and the Specialist Panel will document any factual correction or contextual response alongside the original finding.

Governance & Company Secretarial teams at Payment Institutions firms working on the CPMI-IOSCO Principles for Financial Market Infrastructures (PFMI, 2012) are increasingly relying on AI to draft board charters and committee terms of reference under PFMI Principle 2, prepare board papers describing risk-management frameworks, complete PFMI disclosure-framework templates for the board's governance section, and validate committee-mandate language against the regulator-issued Key Considerations.

The PFMI framework is the global standard for systemically important payment systems, central counterparties, and securities settlement infrastructures, and the document's structure makes it particularly amenable to AI summarisation: numbered Principles, numbered Key Considerations, and lettered annexes that the model can address by number.

That surface structure is also what makes the failure mode the RegLeg Brief Specialist Panel records here invisible at runtime: the document is regularly cited by Key Consideration number in board papers, disclosure-framework returns, and counterparty representations, which means a misattributed citation does not register as a substantive error in the draft, it registers as a competent regulatory paragraph that the reader will not check against the regulator's primary text unless something else prompts the verification.

Two frontier AI models tested by the RegLeg Brief Specialist Panel produced confidently wrong reconstructions of the PFMI's governance and oversight architecture under Principle 2 (governance) and Annex F (oversight expectations for critical service providers). The Panel records one finding in the class the team labels "Source-Credit Misattribution", in which the models stated a substantively plausible governance position and pinned it to a named Key Consideration that the published PFMI text does not support. The finding identifiers are RLB-H-INT-BIS-CPMI-IOSCO-PFMI-2012-Q022-Sonnet46.

For Governance & Company Secretarial teams at Payment Institutions firms, the failure shape matters because the work product is board charters, risk-committee terms of reference, governance policy manuals, PFMI disclosure-framework responses, and committee-mandate submissions to the board, all of which travel under the firm's name to a board, supervisor, counterparty, or public reviewer who can locate the cited Key Consideration and check it against the regulator's primary text.

Governance and Company Secretarial teams at payment institutions responsible for committee architecture and the governance section of the disclosure-framework return are the population most exposed when AI output ties a committee recommendation to the wrong Key Consideration, because the return is reviewed against the PFMI primary text and the misattribution is visible to any reviewer who locates the cited Key Consideration.

The Panel documents the finding identifiers RLB-H-INT-BIS-CPMI-IOSCO-PFMI-2012-Q022-Sonnet46. The AI subjects under test were Claude Sonnet 4.6, each running with web search enabled, mirroring the workflow most practitioners run when they ask an assistant a Principle 2 or Annex F question. The verbatim regulator text is held as primary substrate (R2-REGULATION-d101a_PFMI_main_text.pdf). Each finding card sets out the exact strings the model produced, the verbatim regulator excerpt the model's output contradicts, and the failure-class label the RegLeg Brief Specialist Panel assigns.

The records are open-access; AI labs named in any finding have an unconditional right of reply, and the Specialist Panel will document any factual correction or contextual response alongside the original finding.

Compliance teams at Payment Institutions firms working on the CPMI-IOSCO Principles for Financial Market Infrastructures (PFMI, 2012) are increasingly relying on AI to scope third-party oversight programmes for CSP relationships under an FMI mandate, complete PFMI disclosure-framework responses and self-assessments, draft committee-mandate language for board submissions, and verify governance-arrangement claims against the regulator-issued Key Consideration text. The PFMI framework is the global standard for systemically important payment systems, central counterparties, and securities settlement infrastructures, and the document's structure makes it particularly amenable to AI summarisation: numbered Principles, numbered Key Considerations, and lettered annexes that the model can address by number.

That surface structure is also what makes the failure mode the RegLeg Brief Specialist Panel records here invisible at runtime: the document is regularly cited by Key Consideration number in board papers, disclosure-framework returns, and counterparty representations, which means a misattributed citation does not register as a substantive error in the draft, it registers as a competent regulatory paragraph that the reader will not check against the regulator's primary text unless something else prompts the verification.

Two frontier AI models tested by the RegLeg Brief Specialist Panel produced confidently wrong reconstructions of the PFMI's governance and oversight architecture under Principle 2 (governance) and Annex F (oversight expectations for critical service providers). The Panel records two findings in the class the team labels "Source-Credit Fabrication and Supervisor-Scope Inversion", in which the models stated a substantively plausible governance position and pinned it to a named Key Consideration that the published PFMI text does not support. The finding identifiers are RLB-H-INT-BIS-CPMI-IOSCO-PFMI-2012-Q011-Sonnet46, RLB-H-INT-BIS-CPMI-IOSCO-PFMI-2012-Q022-Opus47.

For Compliance teams at Payment Institutions firms, the failure shape matters because the work product is disclosure-framework returns, self-assessment responses, CSP-oversight policies, third-party risk-management procedures, and committee-mandate submissions to the board, all of which travel under the firm's name to a board, supervisor, counterparty, or public reviewer who can locate the cited Key Consideration and check it against the regulator's primary text.

Compliance teams at payment institutions filing a disclosure-framework return or completing a self-assessment response are the population most exposed when AI output embeds a fabricated Key Consideration or inverts a supervisor's scope, because the document goes to a national authority and will be cross-checked against the regulator's primary text in any Level 2 or Level 3 monitoring review.

The Panel documents the finding identifiers RLB-H-INT-BIS-CPMI-IOSCO-PFMI-2012-Q011-Sonnet46; RLB-H-INT-BIS-CPMI-IOSCO-PFMI-2012-Q022-Opus47. The AI subjects under test were Claude Opus 4.7 and Claude Sonnet 4.6, each running with web search enabled, mirroring the workflow most practitioners run when they ask an assistant a Principle 2 or Annex F question. The verbatim regulator text is held as primary substrate (R2-REGULATION-d101a_PFMI_main_text.pdf). Each finding card sets out the exact strings the model produced, the verbatim regulator excerpt the model's output contradicts, and the failure-class label the RegLeg Brief Specialist Panel assigns.

The records are open-access; AI labs named in any finding have an unconditional right of reply, and the Specialist Panel will document any factual correction or contextual response alongside the original finding.

Governance & Company Secretarial teams at Investment Banking firms working on the CPMI-IOSCO Principles for Financial Market Infrastructures (PFMI, 2012) are increasingly relying on AI to draft board charters and committee terms of reference under PFMI Principle 2, prepare board papers describing risk-management frameworks, generate committee-mandate templates for board subcommittees, and validate governance-policy language against the regulator-issued Key Considerations.

The PFMI framework is the global standard for systemically important payment systems, central counterparties, and securities settlement infrastructures, and the document's structure makes it particularly amenable to AI summarisation: numbered Principles, numbered Key Considerations, and lettered annexes that the model can address by number.

That surface structure is also what makes the failure mode the RegLeg Brief Specialist Panel records here invisible at runtime: the document is regularly cited by Key Consideration number in board papers, disclosure-framework returns, and counterparty representations, which means a misattributed citation does not register as a substantive error in the draft, it registers as a competent regulatory paragraph that the reader will not check against the regulator's primary text unless something else prompts the verification.

Two frontier AI models tested by the RegLeg Brief Specialist Panel produced confidently wrong reconstructions of the PFMI's governance and oversight architecture under Principle 2 (governance) and Annex F (oversight expectations for critical service providers). The Panel records one finding in the class the team labels "Source-Credit Fabrication", in which the models stated a substantively plausible governance position and pinned it to a named Key Consideration that the published PFMI text does not support. The finding identifiers are RLB-H-INT-BIS-CPMI-IOSCO-PFMI-2012-Q022-Opus47.

For Governance & Company Secretarial teams at Investment Banking firms, the failure shape matters because the work product is board charters, risk-committee terms of reference, governance policy manuals, and committee-mandate submissions to the FMI's board, all of which travel under the firm's name to a board, supervisor, counterparty, or public reviewer who can locate the cited Key Consideration and check it against the regulator's primary text.

Governance and Company Secretarial teams responsible for committee architecture at investment banks are the population most exposed when AI output imports a fabricated 'non-executive chair' mandate into a board charter, because the charter circulates to the board, the supervisor, and counterparty due-diligence reviewers, all of whom can locate the cited Key Consideration and check it.

The Panel documents the finding identifiers RLB-H-INT-BIS-CPMI-IOSCO-PFMI-2012-Q022-Opus47. The AI subjects under test were Claude Opus 4.7, each running with web search enabled, mirroring the workflow most practitioners run when they ask an assistant a Principle 2 or Annex F question. The verbatim regulator text is held as primary substrate (R2-REGULATION-d101a_PFMI_main_text.pdf). Each finding card sets out the exact strings the model produced, the verbatim regulator excerpt the model's output contradicts, and the failure-class label the RegLeg Brief Specialist Panel assigns.

The records are open-access; AI labs named in any finding have an unconditional right of reply, and the Specialist Panel will document any factual correction or contextual response alongside the original finding.

Compliance teams at Investment Banking firms working on the CPMI-IOSCO Principles for Financial Market Infrastructures (PFMI, 2012) are increasingly relying on AI to scope third-party oversight programmes for CSP relationships under an FMI mandate, complete PFMI disclosure-framework responses and self-assessments, draft committee-mandate language for board submissions, and verify governance-arrangement claims against the regulator-issued Key Consideration text. The PFMI framework is the global standard for systemically important payment systems, central counterparties, and securities settlement infrastructures, and the document's structure makes it particularly amenable to AI summarisation: numbered Principles, numbered Key Considerations, and lettered annexes that the model can address by number.

That surface structure is also what makes the failure mode the RegLeg Brief Specialist Panel records here invisible at runtime: the document is regularly cited by Key Consideration number in board papers, disclosure-framework returns, and counterparty representations, which means a misattributed citation does not register as a substantive error in the draft, it registers as a competent regulatory paragraph that the reader will not check against the regulator's primary text unless something else prompts the verification.

Two frontier AI models tested by the RegLeg Brief Specialist Panel produced confidently wrong reconstructions of the PFMI's governance and oversight architecture under Principle 2 (governance) and Annex F (oversight expectations for critical service providers). The Panel records two findings in the class the team labels "Source-Credit Fabrication and Supervisor-Scope Inversion", in which the models stated a substantively plausible governance position and pinned it to a named Key Consideration that the published PFMI text does not support. The finding identifiers are RLB-H-INT-BIS-CPMI-IOSCO-PFMI-2012-Q011-Sonnet46, RLB-H-INT-BIS-CPMI-IOSCO-PFMI-2012-Q022-Opus47.

For Compliance teams at Investment Banking firms, the failure shape matters because the work product is disclosure-framework returns, self-assessment responses, CSP-oversight policies, third-party risk-management procedures, and committee-mandate submissions to the board, all of which travel under the firm's name to a board, supervisor, counterparty, or public reviewer who can locate the cited Key Consideration and check it against the regulator's primary text.

Compliance teams at investment banks signing off on a disclosure-framework return or a self-assessment response are the population most exposed when AI output embeds a fabricated Key Consideration or inverts a supervisor's scope, because the document goes to a national authority or the FSB-coordinated monitoring review and will be cross-checked against the regulator's primary text.

The Panel documents the finding identifiers RLB-H-INT-BIS-CPMI-IOSCO-PFMI-2012-Q011-Sonnet46; RLB-H-INT-BIS-CPMI-IOSCO-PFMI-2012-Q022-Opus47. The AI subjects under test were Claude Opus 4.7 and Claude Sonnet 4.6, each running with web search enabled, mirroring the workflow most practitioners run when they ask an assistant a Principle 2 or Annex F question. The verbatim regulator text is held as primary substrate (R2-REGULATION-d101a_PFMI_main_text.pdf). Each finding card sets out the exact strings the model produced, the verbatim regulator excerpt the model's output contradicts, and the failure-class label the RegLeg Brief Specialist Panel assigns.

The records are open-access; AI labs named in any finding have an unconditional right of reply, and the Specialist Panel will document any factual correction or contextual response alongside the original finding.