SEC AI washing sources
Use this page when AI wording appears in investor-facing materials, advisor claims, filings, or financial product marketing. The cases below show what model-use, disclosure, and performance evidence questions SEC materials raise.
This is a source-organized view of reviewed cases and claim patterns. It is not a current determination about a vendor, legal advice, a compliance certification, a company ranking, or a vendor approval list.
Source scope
What this source covers
Investor-facing AI usage, investment performance, model capability, and regulated-advisor positioning claims.
Context and limits
SEC sources are most useful when AI language appears in investor-facing materials, adviser marketing, public-company disclosures, offering materials, or product statements that could shape investor expectations. The SEC's AI-related enforcement focus has covered recurring wording categories including: AI-driven investment or performance claims that lack model description and attribution evidence; first, only, or regulated AI advisor positioning that lacks a defined comparison scope and time boundary; AI trading platform claims where model existence or live deployment is unclear; and public-company statements about automation that misstate human intervention rates or third-party technology roles.
The table below also includes additional claim patterns that carry the same disclosure burden.
For each pattern, the central question is whether the AI system's role was accurately described and disclosed at the time investors could rely on the statement.
How buyers should use it
SEC materials are most applicable to registered advisers, public-company reporting obligations, and offer-related statements—they do not address internal technology choices, private-company product claims, or marketing outside investor-facing contexts.
Do not use this page as investment advice, a company rating, or a pass/fail view of any entity.
Source timeline
Use these dated source points to understand which AI claim patterns this page can help you compare before you open a case detail.
-
March 18, 2024
Delphia source addresses claims that AI and machine learning were used to predict consumer spending and inform investment decisions.
AI-driven investment claims need a model description, data-source explanation, performance attribution method, and disclosure of limits.
-
March 18, 2024
Global Predictions source addresses a first regulated AI financial advisor claim and related AI portfolio-management wording.
First, only, or regulated AI positioning needs a defined comparison scope, a time boundary, and evidence that the AI capability matches the public wording.
-
October 10, 2024
Rimar source addresses statements about an AI-driven platform for trading securities and automated trading for client accounts.
AI trading-platform claims need evidence of model ownership, live use, trading workflow role, oversight controls, and investor disclosure support.
-
January 14, 2025
Presto Automation source addresses statements about an AI voice product, third-party technology, and the degree of human intervention.
Automation claims in public-company materials need technology ownership details, human intervention rates, completion metric definitions, and deployment context.
Claim patterns and evidence gaps
Match the public AI wording you saw to a pattern below, then use the buyer question to decide what evidence to request or whether to check the exact wording.
| Claim pattern | Evidence gap | Buyer question |
|---|---|---|
| AI drives investment decisions or performance | Model description, data sources, decision role, performance attribution, independent validation, and disclosure of model limits. | Which investment decision does AI influence, and how is the AI contribution separated from market exposure, human judgment, or ordinary strategy? |
| First, only, or regulated AI advisor positioning | Comparison scope, time period, market category, regulatory-status basis, AI capability description, and evidence the claim remains current. | What exact category does first or only apply to, and when was that comparison last checked against other market offerings? |
| AI-driven trading platform claims | Model existence, model ownership, trading workflow role, live-deployment proof, risk controls, backtest limits, and client disclosure records. | What evidence shows the AI system was live and used in the trading workflow when the claim was made? |
| AI voice or automation product claims in public-company statements | Third-party technology role, deployed-unit coverage, human intervention rate, completion metric definition, exception categories, and quality controls. | What percentage of tasks are completed without human intervention, and do buyer, investor, and operator statements use the same metric? |
| AI capability claims without disclosure support | Disclosure document references, named AI component, capability boundaries, known limitations, supervision process, and version or date of supporting evidence. | Where do the public disclosures describe what the AI does, what it does not do, and who supervises decisions affected by the system? |
| AI-enhanced portfolio construction or asset allocation claims | Model description, factor selection method, backtesting scope and limitations, live-deployment period, benchmark comparison basis, and disclosure of model constraints in offering documents. | What evidence shows the AI contribution to portfolio construction, and where are its limits disclosed to investors before they rely on the claim? |
| AI risk management, compliance screening, or abuse-detection claims in financial products | Detection scope, false positive and false negative rates, rule-based versus model-based component breakdown, audit trail, third-party validation, and regulatory disclosure status. | What performance record exists for the AI's risk-screening or abuse-detection role, and where are the detection limits disclosed to clients or regulators? |
| AI-enhanced research or due diligence claims in investment marketing | AI system description, human analyst involvement, data source currency, model update cadence, conflict-of-interest disclosure, and comparison to standard research methodology. | How much of the research output involves AI versus qualified human judgment, and where is that breakdown disclosed to investors in writing? |
Reviewed cases
Open a case to see the original source, claim type, evidence gap, buyer questions, wording boundaries, and update status.
-
SEC Delphia AI investment claims
- Source date
- March 18, 2024
- Checked date
- May 22, 2026
Risk pattern: AI-driven performance claim without disclosed model description or independent validation
Evidence gap: Description of the AI or ML model used, training data and sources, how the AI contribution to investment returns was measured and isolated from other factors, independent validation of stated capabilities, and disclosed limitations of the model.
See evidence gap and buyer questions → -
SEC Global Predictions 'first regulated AI advisor'
- Source date
- March 18, 2024
- Checked date
- May 22, 2026
Risk pattern: Uniqueness claim without defined scope; AI capability claim without disclosed method
Evidence gap: The scope definition used to support the 'first' claim, how the comparison was made and against what alternatives, what the AI component does versus conventional advisory tools, independent validation of AI capabilities, and when the claim was last reviewed for continued accuracy.
See evidence gap and buyer questions → -
SEC Rimar AI trading platform
- Source date
- October 10, 2024
- Checked date
- May 22, 2026
Risk pattern: AI-driven investment platform claim without evidence of model ownership, live use, trading workflow, or investor disclosure support
Evidence gap: Model description, ownership and vendor involvement, live-deployment evidence, trading workflow role, client-account coverage, risk controls, human oversight, performance attribution, backtest limits, disclosure documents, and records showing the AI capability existed when the claim was made.
See evidence gap and buyer questions → -
SEC Presto AI voice automation
- Source date
- January 14, 2025
- Checked date
- May 22, 2026
Risk pattern: AI automation claim without disclosure of third-party technology, human intervention rate, or completion metric definition
Evidence gap: Technology ownership and vendor role, deployed-unit coverage, human intervention rate, order-completion metric definition, exception categories, customer deployment data, quality control process, and disclosure showing how AI-assisted work differs from human-assisted work.
See evidence gap and buyer questions →
How to use this source page
- Match the source to the claim context. Investor-facing AI usage, investment performance, model capability, and regulated-advisor positioning claims.
- Read the closest case. Compare the risk pattern and evidence gap with the claim you are reviewing.
- Check the exact wording. Paste the public URL or claim text into the checker to get evidence requests, buyer questions, and wording boundaries for the specific claim.
Source updates and corrections
If a source status, date, company response, or case context is outdated, send a correction. Submissions are private by default and reviewed before any update appears on this page.
Send a correction →