The U.S. Securities and Exchange Commission's first artificial-intelligence-washing settlements, announced March 18, 2024, fined two registered investment advisers a combined $400,000 for claiming to use AI and machine learning that neither firm had actually built — a violation the agency's own order treats as false marketing, not a failed model.
What did the SEC's first AI-washing orders actually allege?
Delphia (USA) Inc., a Toronto-based adviser, told clients between 2019 and 2023 that it used artificial intelligence and machine learning to analyze their personal spending and social-media data, feeding the results into stock-picking algorithms. According to the SEC's order against the firm, Delphia collected some client data intermittently but never used that data with artificial intelligence or machine learning, or as an input into its investing algorithms, at any point.
Global Predictions Inc., a San Francisco-based adviser, separately marketed itself as the "first regulated AI financial advisor" and claimed to deliver "expert AI-driven forecasts" — claims the SEC found were not substantiated. Both firms settled without admitting or denying the findings. Delphia paid a $225,000 penalty and accepted a censure; Global Predictions paid $175,000. Both were ordered to cease and desist from further violations of Sections 206(2) and 206(4) of the Investment Advisers Act, the provisions covering fraudulent and misleading statements to clients.
Then-SEC Chair Gary Gensler framed the enforcement rationale in plain terms: "Investment advisers should not mislead the public by saying they are using an AI model when they are not. Such AI washing hurts investors." Enforcement Director Gurbir S. Grewal added the underlying standard the orders apply: "if you claim to use AI in your investment processes, you need to ensure that your representations are not false or misleading."
| Firm | Claim examined | SEC finding | Penalty |
|---|---|---|---|
| Delphia (USA) Inc. | AI/machine learning trained on client spending and social-media data, 2019–2023 | Data was collected intermittently but never used with AI, machine learning, or as an algorithm input | $225,000 plus censure |
| Global Predictions Inc. | "First regulated AI financial advisor"; "expert AI-driven forecasts" | Claims not substantiated; marketing-rule violations | $175,000 |
How is "no model to validate" different from a model that fails validation?
The SEC's orders describe an absence, not an error: no algorithm existed for AI or machine-learning claims to be tested against. That is a different failure than a model that runs, produces forecasts, and is later found miscalibrated — the scenario federal model-risk guidance for banks was written to address.
Model risk management is the discipline of managing "the potential for adverse consequences from decisions based on incorrect or misused model outputs and reports," as defined in joint 2011 guidance from the Federal Reserve and the Office of the Comptroller of the Currency. That guidance assumes a model is already running: it sets out three validation elements — conceptual soundness, ongoing monitoring, and outcomes analysis, the last comparing actual results to model predictions through back-testing over a stated evaluation period. None of the three applies to a claim about a model that was never built. Conceptual soundness requires design documentation to review; outcomes analysis requires outputs to compare against realized results. The Delphia and Global Predictions matters never reach that stage, because the system supposedly generating those outputs, per the SEC's findings, did not exist as described.
What would model risk management guidance have required before a model reached investors?
Interagency guidance requires disciplined development, independent "effective challenge" from staff not involved in building the model, board-level governance, and review at least annually — a structure built for models that exist and produce output, not for marketing copy describing capabilities that were never deployed.
Applied here, the guidance's checklist explains the size of the gap regulators found. Conceptual soundness review would have asked for the model's design and assumptions; there was none to review. Ongoing monitoring would have confirmed the model was implemented and used as intended, including benchmarking against alternative approaches; there was no live implementation to monitor. Outcomes analysis would have compared predicted results to what actually happened over a defined window; there was no track record to benchmark, because the AI system described to clients and regulators in filings, press releases, and marketing material was not operating.
| Validation element (per the guidance) | What it requires | Status in the Delphia/Global Predictions matters |
|---|---|---|
| Conceptual soundness | Design documentation and supporting research to review | No documented model design existed to review |
| Ongoing monitoring | Confirmation the model is implemented and used as intended, benchmarked against alternatives | No live implementation was running to monitor |
| Outcomes analysis | Comparison of predicted results to realized results over a stated period | No output record existed to compare against outcomes |
The interagency guidance also calls for "effective challenge": objective review by staff with the incentive, competence, and authority to critically assess a model, independent of the people who built it. In both matters, the SEC's findings suggest that step never had a model to challenge in the first place — the compliance failure sat further upstream, in what the firms told clients and regulators, not in how a model performed once deployed.
What do the SEC's orders not establish?
The orders establish that specific marketing statements were false and settled under securities law; they do not evaluate whether any AI or machine-learning model used elsewhere in the industry actually forecasts well, and they carry no finding on model accuracy, since accuracy was never at issue in either case.
The SEC's Office of Investor Education separately warned, in a January 25, 2024 investor alert, that AI-generated information "might rely on data that is inaccurate, incomplete, or misleading," and urged investors to verify a firm's registration status independently rather than take AI claims at face value. That alert addresses a different risk than the Delphia and Global Predictions orders: platforms actively using AI output to mislead, versus firms claiming AI use that did not occur. Both sit outside the scope of conventional model validation, because in each case regulators examined a marketing or disclosure statement, not a model's calibration.
For a reader evaluating a forecasting claim from any adviser or vendor, the record separates two questions worth keeping apart: whether a model exists and was validated against a stated baseline, and whether its description to clients matches what was actually built and run. The SEC's 2024 orders resolved only the second question, for two firms, under securities marketing law — not the first.
For a related forecasting perspective, read What is 'probability of backtest overfitting,' and how is it actually measured?.
For more context, read Tech Giant: Data and Google.