AI on Amendments to Regulation 1.25 — Permissible Investments of Customer Funds by Futures Commission Merchants and Derivatives Clearing Organizations for Treasury teams at Investment Banking firms in the United States

Executive Summary

Treasury teams at U.S. investment banks sit at the intersection of two obligations under the 2024 Regulation 1.25 amendments: their affiliated FCMs must comply directly, and the Treasury function typically owns or heavily influences the internal investment policy, eligible-asset schedules, and concentration-monitoring controls that operationalise that compliance. When those teams turned to AI assistants to answer questions about the updated permissible-investment framework, the AI got both questions substantively wrong — across concentration limits for government money market funds and Treasury ETFs, and across the dollar-weighted average maturity calculation and its exclusions.

The failures are not edge-case misreads: one AI confidently asserted a flat, uniform limit structure with no size-based tiers and later retracted on challenge, admitting it had synthesised secondary commentary rather than the regulatory text; a second omitted the exclusion clause from the WAM calculation entirely. Both errors carry direct regulatory enforcement risk — a Treasury function that builds or signs off on investment guidelines using these AI responses would be encoding non-compliant concentration ceilings or a miscalibrated maturity metric into live controls.

How AI gets this regulation wrong

The failures on this regulation split into two distinct patterns: one AI invented a simplified rule structure — asserting uniform limits with no size-based tiers — then retracted when pressed, revealing it had drawn on third-party commentary rather than the amended text; a second AI correctly identified a numeric ceiling but silently dropped the exclusion clause that materially changes which instruments count toward it.

Together they illustrate a consistent vulnerability: the regulation's structural complexity (tiered thresholds conditioned on fund and manager asset size, and a portfolio-level metric with carve-outs) is exactly where AI tools flatten or elide the detail that matters most to compliance.

What that means for your team

Both failures in this cell land in the same risk category: regulatory enforcement. That alignment is not coincidental — the 2024 amendments are operating rules with direct compliance obligations, and errors in how the Treasury function reads them feed immediately into the investment policy, eligible-asset registers, and daily limit-monitoring that the CFTC would scrutinise in an examination or enforcement action. The table below maps each finding to the specific enforcement exposure the firm carries if the AI's version of the rule is what ends up in the guideline.

When this affects your department

Treasury teams at U.S. investment banks reach for AI tools on Regulation 1.25 in several high-stakes moments: updating the firm's FCM-affiliated eligible-asset policy after a regulatory amendment cycle, building the concentration-limit schedules that feed daily limit-monitoring systems, reviewing or challenging a business line's proposal to add a new fund type to the approved-investment list, and responding to internal audit's periodic review of segregated-asset compliance.

They also use AI to produce training materials for junior staff responsible for overnight segregated-fund allocation decisions, and to quickly orient new team members on the mechanics of the permissible-investment framework without pulling external counsel every time. Each of these use cases places AI output directly upstream of a compliance artefact — a policy document, a limit parameter, a system configuration — that will be tested against the actual rule text in the next CFTC examination.

The concentration-limit error (Finding #1) is particularly dangerous in the policy-drafting context. A Treasury team encoding a flat 10% per-fund limit without the 50% ceiling for large-fund / large-manager combinations is writing a policy that is simultaneously more restrictive in some positions (unnecessarily limiting eligible diversification into qualifying large funds) and potentially misleading in how it characterises the rule to business lines. More critically, if the team has modelled its approved-fund list or limit-monitoring thresholds on the AI's flat-structure version, the actual regulatory requirement — the tiered ceiling — will not be reflected in the control environment the CFTC sees.

The WAM exclusion omission (Finding #2) surfaces most sharply when Treasury is calibrating the portfolio-level maturity metric or explaining to the CIO or risk committee how much duration headroom the segregated portfolio carries. Dropping the exclusion of government money market funds, Treasury ETFs, and foreign sovereign debt from the WAM denominator is not a rounding error — it changes the effective constraint on the rest of the portfolio materially.

A Treasury team that builds its duration-management framework around the AI's version of the metric will have a miscalibrated ceiling, and when internal audit or the CFTC tests the calculation against the actual rule text, the gap becomes a control deficiency with enforcement implications.

The findings at a glance

The two findings below represent the questions on which AI assistants produced materially incorrect answers when tested against the 2024 Regulation 1.25 amendments — both confirmed hallucinations, both in the regulatory enforcement risk tier.

Aggregate impact

Both findings cluster on the same structural feature of the 2024 amendments: the rules are not simple uniform limits, and AI tools consistently failed to represent that complexity accurately. The concentration-limit framework is tiered — the 50% ceiling for large government money market funds and large managers sits above and is distinct from the per-issuer limits — and that tier is exactly what AI assistants stripped out, reverting to a flat-structure description that reads like a summary from secondary commentary rather than the operative rule.

The WAM calculation has a defined exclusion set that is integral to how the metric actually constrains the portfolio, and AI tools omitted it, leaving a materially incomplete characterisation of the ceiling.

For Treasury functions at U.S. investment banks, the systemic risk is that these are precisely the provisions most likely to require a policy call or a numeric parameter to be set somewhere in the control environment. A flat concentration limit gets encoded in a limit register. A WAM calculation without the correct exclusion set gets built into a monitoring spreadsheet or risk system feed.

Once those artefacts are live, the error propagates silently: daily monitoring runs against the wrong threshold, compliance sign-offs affirm adherence to a rule that is not the rule in force, and internal audit's periodic testing — which typically validates against the policy document rather than the primary regulatory text — may not catch the delta.

The shared risk category (regulatory enforcement) across both findings is the right frame. The CFTC's examination focus on segregated-fund compliance is intensive, and the 2024 amendments were specifically intended to update and tighten the permissible-investment framework. An FCM-affiliated firm whose investment policy reflects the pre-amendment or AI-flattened version of either provision is exposed to a finding of non-compliance with the amended rule — not a technical gap, but a substantive control failure in one of the CFTC's highest-priority examination areas.

What your team should do

The default position for Treasury teams on the 2024 Regulation 1.25 amendments should be primary-source verification for any provision that touches a numeric threshold, a tiered structure, or an exclusion clause. That is a deliberately narrow scope — AI tools can handle orientation tasks reliably (summarising the broad purpose of the amendment cycle, listing the asset categories addressed, explaining the regulatory history). Where they fail is in the structural detail: the conditions under which a higher ceiling applies, the instruments excluded from a portfolio-level calculation.

Any AI output touching those features should be verified directly against the CFTC's published final rule and the amended regulatory text before it feeds a policy document, a limit parameter, or a training material.

For the concentration-limit framework specifically, the practical safeguard is to require that any internal policy or limit schedule referencing government money market fund or Treasury ETF limits explicitly states the conditions under which each ceiling applies — fund asset size, management company AUM — rather than a single percentage. If an AI-generated draft reads as a flat limit with no qualifying conditions, that is a reliable signal the tiered structure has been missed.

The same applies to any vendor or counsel summary used as a policy input: the 50% ceiling for qualifying large-fund combinations was noted to be absent from multiple secondary-source paraphrases, so external summaries carry the same risk as AI output on this point.

For the WAM calculation, the safeguard is straightforward: any model or spreadsheet implementing the dollar-weighted average maturity ceiling must explicitly exclude government money market funds, Treasury ETFs, and foreign sovereign debt from the denominator, and that exclusion should be documented in the methodology note for the calculation. AI tools are safe for explaining the general mechanics of WAM and its role in the segregated-fund framework; they are not reliable for specifying which instruments are in or out of the calculation under the 2024 amendments.

Internal audit testing of the WAM control should validate the exclusion set against the amended regulatory text, not against the firm's policy document alone.

How RLB Can Help

RegLeg's published Hallucination Research is available as a public reference — before your team routes a regulatory question through any AI tool, the findings corpus gives you a ground-truth check on where those tools have already been caught fabricating or inverting positions on the rules your function lives by. For Treasury at a US investment bank, that means a pre-flight read on the regulations that govern your liquidity coverage ratio calculations, FRTB SA/IMA boundary decisions, swap dealer obligations under CFTC Part 23, and intraday liquidity monitoring under SR 14-1 and related Fed guidance.

It is not a substitute for independent legal review, but it is a faster and more specific signal than a generic AI disclaimer.

Beyond the published catalogue, we work with Treasury teams on bespoke regulator deep-dives — mapping which AI-supported workflows in your specific function carry the highest hallucination exposure given the document types, question structures, and regulatory vintage involved. The failure modes that surface most often in Treasury contexts — inverted thresholds, misattributed calculation methodologies, stale phase-in dates from superseded Basel text — are not random. They cluster around the same structural features of how AI tools process dense, amendment-layered regulatory material.

We can scope that mapping to your actual workflow, from daily LCR reporting queries through to NSFR modelling assumptions and SACCR exposure calculations, and give you a prioritised view of where AI-assisted outputs need the tightest human review layer.

For teams with a formal AI-use policy already in place, we offer a confidential review against RegLeg's failure-mode catalogue — not a compliance audit, but a working session that identifies where your current policy's assumptions about AI reliability are contradicted by documented failure patterns on the regulations you reference most. That output feeds directly into remediation prioritisation and, where your team needs to demonstrate due diligence internally or to regulators, into CPD-aligned training material your Treasury staff can use to build structured scepticism into their AI-assisted workflows.