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

Executive Summary

Treasury teams at Corporate Banking firms operating in the United States face material compliance exposure when AI tools are used to interpret the CFTC's 2024 amendments to Regulation 1.25, which govern how FCMs and DCOs may invest customer segregated funds — including which instruments qualify, what concentration ceilings apply, and how portfolio maturity must be measured. Across two questions central to day-to-day Treasury compliance work on this rule, AI assistants we tested produced wrong answers both times.

One failure involved AI confidently asserting a flat, uniform concentration limit while omitting the tiered structure that applies to large government money market funds and large asset managers — then retracting only when pressed. The second failure involved AI correctly citing the 24-month dollar-weighted average maturity ceiling while dropping the exclusion clause that carves out government money market funds, Treasury ETFs, and foreign sovereign debt from that calculation — a gap invisible unless the reader already knows the answer.

Both errors carry direct regulatory enforcement risk: a Treasury team that acts on either answer without source verification could miscalibrate investment limits or portfolio maturity controls in ways that breach the rule on the books.

How AI gets this regulation wrong

The failures AI assistants produced on this regulation split across two distinct patterns: confident misstatement of the actual rule structure — with retraction only under challenge — and selective omission of a critical exclusion clause that quietly changes how a key limit is calculated. Together they expose a consistent gap between AI tools' surface-level familiarity with the regulation's headline numbers and their inability to accurately reproduce its operative mechanics.

What that means for your team

Every failure identified here maps to regulatory enforcement risk — the category with the least tolerance for error in Treasury's compliance work. For a Treasury function operating under CFTC oversight, both findings point to scenarios where miscalibrated investment policies or incorrectly constructed maturity calculations could constitute a direct breach of the segregated-funds rules, with consequent examination exposure and potential enforcement action.

When this affects your department

Treasury teams reach for AI tools on Regulation 1.25 in a handful of high-stakes moments: updating the firm's permitted investments policy in the wake of the 2024 amendments, drafting or reviewing the concentration-limit section of an FCM's investment policy statement, preparing internal training materials for fund operations staff, or responding to an NFA or CFTC examination request that requires explaining the firm's interpretation of the new tiered limits. These are not research exercises — they feed directly into enforceable internal documents and examiner-facing representations.

The concentration-limit question is where the risk bites hardest. If a junior analyst or a business-line partner queries an AI tool to confirm whether the firm's current government money market fund exposure is within limit, and that tool returns a flat 10% uniform ceiling while omitting the 50% ceiling available for large funds under a qualifying manager, the firm may be making conservative investment decisions based on a non-existent constraint — or, more dangerously, the policy itself may reflect the wrong limit if it was drafted using an AI-assisted summary.

When that policy goes to internal audit or an examiner, the mismatch between the written policy and the actual rule creates a gap that requires explanation.

The maturity calculation finding is equally consequential in practice. Treasury's portfolio reporting and compliance attestation for segregated assets depends on correctly scoping what goes into the dollar-weighted average maturity calculation. If government money market funds, Treasury ETFs, and foreign sovereign debt are incorrectly included — because the AI omitted the exclusion clause — the reported WAM figure is wrong, the compliance sign-off is wrong, and any internal limit framework built on that calculation is wrong. This type of structural error typically lives undetected in quarterly compliance reports until an examination surfaces it.

The findings at a glance

The table below summarises each finding — the question asked, what the AI got wrong, and the enforcement risk it creates for a Treasury function working on Regulation 1.25.

Aggregate impact

Both findings cluster on the same functional area — the quantitative mechanics of how Regulation 1.25's new limits actually operate in practice — and both errors run in the same direction: AI tools returned simplified, incomplete versions of the rule that strip out the structural complexity that matters most to compliance. The concentration-limit error collapsed a two-tier asset-based structure into a single uniform percentage. The maturity calculation error dropped the exclusion clause that scopes the universe of instruments subject to the 24-month WAM ceiling.

Neither error is a peripheral detail; both affect the core compliance calculations Treasury performs against segregated customer funds.

The pattern is consistent with how AI tools fail on rules that have been amended: they reproduce the pre-amendment framework or secondary-source summaries of the amendment with high confidence, then drop the operative provisions that make the new rule different from the old one. For Regulation 1.25, the 2024 amendments introduced exactly the kind of layered, asset-size-conditional structure that secondary sources tend to paraphrase away. Treasury teams relying on AI-assisted policy work during an amendment cycle face a specific structural risk — the AI's answer may correctly reflect the prior rule while being materially wrong about the current one.

The aggregate enforcement exposure is not theoretical. Segregated-funds compliance is one of the CFTC's highest-priority examination areas; incorrect investment limits or miscalculated maturity figures in the firm's IPS or quarterly compliance reports are the type of findings that generate deficiency letters and remediation timelines. For a Corporate Banking Treasury function, the cost is not just regulatory penalty — it is the operational disruption of a forced policy rewrite, retroactive re-testing of prior period compliance, and the reputational cost of a documented examination gap in one of the most scrutinised areas of FCM oversight.

What your team should do

The default position for Treasury work on this regulation is straightforward: treat AI-generated answers on any quantitative limit or calculation methodology as a starting point that requires direct verification against the CFTC's published final rule text. The 2024 amendments are recent enough, and their structural complexity distinctive enough, that secondary-source paraphrase — which is what AI tools effectively reproduce — reliably omits the provisions that matter most.

That is not a reason to avoid AI entirely, but it is a reason to build source-verification into the workflow before any AI-assisted output touches an internal policy document, an IPS, or a compliance attestation.

For the specific questions these findings cover, the practical safeguard is a two-step check: AI to locate the relevant provision quickly, then a direct read of the regulatory text to confirm the operative details. On concentration limits, that means confirming the tiered structure — the 50% ceiling for qualifying large-fund/large-manager combinations alongside the instrument-level caps — not just the headline percentages. On the maturity calculation, it means explicitly verifying what is excluded from the WAM universe before the calculation methodology is codified in policy or embedded in reporting templates.

Where AI tools are safer in Treasury's Regulation 1.25 workflow: procedural questions about the rule's effective date and phase-in, identifying which instrument categories are eligible at all, and summarising the regulatory background and CFTC rulemaking history. These are areas where the AI's answer is easier to spot-check and where the consequences of minor inaccuracy are lower. The unsafe zone is any quantitative threshold, any tiered or conditional structure, and any exclusion or carve-out from a calculation — all of which require direct source confirmation regardless of how confident the AI's response appears.

How RLB Can Help

RegLeg's published Hallucination Research is available as a free pre-flight reference before your team acts on AI output for any regulatory question covered in the corpus. If your desk is using AI tools to interpret Federal Reserve capital requirements, CFTC swap-dealer obligations, OCC liquidity guidance, or FinCEN reporting rules, the published findings let you check whether those specific instruments and rules fall in a documented failure zone — before a position, a filing, or a board memo goes out the door based on a hallucinated interpretation.

For firms that want to go further, RLB conducts bespoke regulator deep-dives scoped to the Treasury function's actual workflow: intraday liquidity monitoring, LCR/NSFR classification decisions, FX and derivatives hedging compliance, and FRTB internal model use. These engagements map the specific AI-assisted steps your team runs — whether that is policy lookup, regulatory change tracking, or scenario analysis — against the failure-mode catalogue, and rank them by exposure. The output is a prioritised risk register for your AI-use governance, not a generic vendor report.

RLB also conducts confidential reviews of a firm's existing AI-use policies and operating procedures against the failure-mode taxonomy. If your policy governs how analysts use AI tools for regulatory interpretation, we can identify which gaps — ambiguous scope clauses, missing verification checkpoints, untested edge cases — leave the Treasury function exposed to the classes of error the research documents.

Where there is appetite, we build out targeted training material and CPD-aligned content your team can deploy internally, so Treasury staff can interrogate AI output on regulatory questions with the same rigour they would apply to a vendor model or an external counsel opinion.