Executive Summary
The CFTC's 2024 amendments to Regulation 1.25 tighten the permissible investment universe for customer segregated funds held by FCMs and DCOs — changes with direct operational and compliance impact on any corporate banking firm that clears or provides clearing-related services. Legal teams tracking the rulemaking process, preparing regulatory-history memos, or advising on the rule's procedural posture need an accurate account of how and when the rule was formally adopted.
Across the questions we put to AI tools on this regulation, one question produced a clear hallucination: AI tools misrepresented the CFTC's adoption mechanism, asserting a noticed public Commission meeting occurred when the actual vehicle was the seriatim voting process. The failure is not cosmetic — it misidentifies the procedural record, the availability of dissent, and the evidentiary basis for any challenge to the rule's adoption. When pressed, the AI retracted the claim entirely, confirming it had no reliable basis for the assertion.
How AI gets this regulation wrong
The dominant failure pattern on this regulation is confident fabrication that collapses under follow-up — AI tools supplied specific procedural details (meeting format, presiding official) that turned out to be invented, then walked them back when challenged. The fabrication follows a recognisable logic: AI pattern-matched the December 3 date to the more familiar open-meeting adoption vehicle, generating plausible-sounding procedural narration without any verified basis in the actual CFTC record.
| AI's Failure Mode | Count | Affected findings |
|---|---|---|
| Exposed Fabrication | 1 | Finding#1 |
What that means for your team
For Legal teams at corporate banking firms, the risk here is a wrong work product — a memo, regulatory history summary, or litigation-support briefing that embeds a factually incorrect procedural account of how the rule was adopted. The seriatim-versus-open-meeting distinction matters precisely because it determines what documentary record exists (transcripts, live dissents, commissioner statements) and therefore what your team can cite or rely on if the rule's validity is ever at issue.
| Risk Impact | Count | Affected findings |
|---|---|---|
| Wrong deliverable | 1 | Finding#1 |
When this affects your department
Legal teams at corporate banking firms engage with Regulation 1.25 in several distinct workflows: mapping the rule's investment restrictions against the firm's existing FCM or DCO-client product suite, advising treasury and operations on the permissible-investment categories, and preparing or reviewing internal policies that implement the 2024 amendments. For any of those outputs, a threshold question is: what exactly did the CFTC adopt, when, and through what process? Teams routinely use AI tools to produce fast regulatory-history summaries — the kind of background section that anchors a legal opinion, a business-line briefing, or a compliance committee memo.
If the AI fabricates the procedural vehicle, that error travels into every downstream document the team touches.
The procedural question is particularly live for any firm that fields internal or client-side questions about the rule's APA defensibility — whether the CFTC followed its own procedures, whether commissioners had the opportunity to register dissent, and what the official rulemaking record shows. A seriatim adoption does not create the same kind of public transcript as a noticed open meeting; the commissioner vote record is not individually published in the sources the AI would typically cite.
That asymmetry means a junior attorney using AI to draft the procedural-history section of a challenge-risk memo could insert an affirmatively wrong characterisation — one that implies richer documentation exists than actually does.
Where this becomes operationally costly is in any escalation where the regulatory history is load-bearing: a formal legal opinion to the board or a risk committee, a written response to an examiner, or support materials for litigation. Correcting the record after a document has been circulated internally or shared with a regulator forces a retraction workflow that is disproportionate to what the AI error appears to be — a single sentence about meeting format — but which touches the firm's credibility on regulatory matters.
The findings at a glance
The table below summarises the finding from our AI testing on this regulation — the question asked, the nature of the AI's failure, and the risk category it creates for your team.
| # | Finding title | Type | Citation ID |
|---|---|---|---|
| 1 | CFTC seriatim adoption vs. fabricated open meeting | Hallucination | RLB-F-US-CFTC-FCM-DCO-CUSTOMER-FUNDS-INVESTMENTS-REG-1-25-2024-Q005 |
Aggregate impact
The single finding on this regulation clusters on procedural provenance — a category of question that Legal teams routinely treat as settled background rather than substantive analysis. That is precisely the vulnerability: attorneys reviewing AI output for substantive accuracy (correct investment categories, correct effective dates, correct exemption scope) may not scrutinise the procedural framing with equal care. The AI's fabrication here is structurally designed to pass that review — it is specific, confident, and attaches named officials and dates, all of which signal sourced knowledge.
The systemic exposure is to the firm's regulatory-history documentation layer. For a corporate banking firm with FCM or DCO clearing relationships, Regulation 1.25 touches the firm's operational and legal infrastructure at multiple points — and any internal library of regulatory summaries, policy rationale memos, or compliance-committee papers that was populated with AI-assisted drafting may now contain this error without anyone knowing. The firm cannot easily audit which documents carry the fabricated procedural account without reviewing them individually.
What compounds the risk is the AI's own retraction behaviour. When challenged, the AI admitted it had no reliable basis for the assertion — which means any attorney who accepted the initial answer at face value and did not run the follow-up received a confidently delivered, factually wrong answer. The population of AI users who do not challenge initial responses is larger than the population who do; the probability of propagation is therefore high.
What your team should do
The default position for your team on Regulation 1.25 procedural-history questions should be primary-source verification before any document leaves Legal. The CFTC Federal Register publication is the authoritative record of the adoption mechanism; the agency's website lists its seriatim voting procedures separately from open meeting agendas. For any memo, opinion, or briefing where the adoption process is cited — particularly where it is load-bearing for a challenge-risk or APA-defensibility analysis — an attorney should confirm the vehicle directly against the Federal Register preamble, not an AI summary.
AI tools are safer on the substantive investment-restriction content of the 2024 amendments — the eligible asset categories, the concentration limits, the liquidity requirements — because those provisions are reproduced verbatim in the final rule text and in widely indexed secondary sources. Where AI tools are unreliable is on procedural and institutional process questions: how a rule was adopted, what the commissioner vote record shows, whether dissents were filed, and what the rulemaking docket contains. These questions require institutional knowledge of CFTC practice that AI tools frequently substitute with plausible inference from more familiar regulatory patterns.
For the practical control design: if your team has a template for regulatory-history sections in legal opinions or committee memos, adding a two-step check — (1) confirm adoption mechanism from Federal Register preamble, (2) confirm whether any commissioner statements appear in the public record — costs minutes and eliminates the category of error we observed. Treat AI output on rulemaking procedure as a starting hypothesis, not a citable source, and make the verification step explicit in your team's drafting protocol for any Regulation 1.25 work product.
How RLB Can Help
RegLeg's published Hallucination Research gives your team a concrete pre-flight check before placing weight on AI-generated output on regulatory questions. If your attorneys are using AI tools to answer questions about capital requirements, swap dealer obligations, BSA/AML thresholds, or enforcement posture, the research shows exactly where those tools produce confident, specific, and wrong answers — wrong entity, wrong threshold, wrong effective date. That catalogue is public and searchable by regulation, which means your team can cross-reference a specific reg before it enters a memo, a board presentation, or a client-facing opinion.
Beyond the public research, we work with Corporate Banking Legal teams on bespoke regulator deep-dives — mapping which AI-supported workflows in your function carry the highest hallucination exposure. In a Corporate Banking Legal context that typically means: regulatory change monitoring and horizon scanning, first-cut responses to exam requests and supervisory letters, internal policy drafting against multi-regulator frameworks (OCC, Fed, FDIC, FinCEN, CFTC), and cross-border compliance analysis where US rules interact with foreign equivalents. The output is a prioritised exposure map scoped to your team's actual workflow stack, not a generic AI-risk checklist.
We also conduct confidential reviews of existing AI-use policies against the failure-mode catalogue — identifying gaps between what the policy assumes AI tools can reliably do and what the research demonstrates they routinely get wrong, with remediation ranked by regulatory materiality rather than technical severity.
For teams building internal capability, we produce training material and CPD-aligned content your attorneys can use directly — grounded in real failure patterns against the specific regulations your practice covers, not vendor-supplied AI literacy modules. The framing is practical: what to verify, what to never accept at face value, and how to document the human review step for supervisory or litigation purposes.