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The exact obligations depend on your role, product, firm procedures and jurisdiction. The workflow below is a risk-based control process for U.S. financial professionals; it is not a universal regulator-prescribed checklist.
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What a defensible review must establish
A client-ready analysis should let a reviewer answer six questions:
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- What task was the AI system authorized to perform, and for whom?
- Which statements could influence a client’s decision?
- Can each material fact be verified in an authentic, current source?
- Can the important calculations be reproduced from known inputs?
- Are the assumptions, limitations, conflicts and client-fit considerations explicit?
- Who reviewed, corrected and approved the final communication, and what record proves it?
AI prose can sound precise while being unsupported. FINRA investor guidance states: “Even when based on accurate input, information resulting from AI can be faulty, or even completely made up.” Treat fluent wording as a reason to inspect the underlying work, not as evidence that the work is sound.
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Step 1: Establish scope and stakes before reading the draft
Identify the deliverable and audience
Record whether the output is an internal note, market commentary, research, a portfolio proposal, a performance explanation, a suitability discussion or another form of client communication. Name the intended client or client segment, the relevant jurisdiction, the product or security, the valuation or “as of” date, and whether the text is informational or contains a recommendation.
A paragraph that is harmless educational content can become consequential when it recommends a security, allocation, transaction or strategy. Mark the point at which the document moves from description to advice. That boundary determines how closely you must examine client objectives, risk tolerance, time horizon, liquidity needs, tax considerations, conflicts and required disclosures.
Identify the system and authorized task
Capture the model or vendor, version when available, date of use, prompts or instructions that materially shaped the answer, data supplied to it, and the task it was authorized to perform. If an agent took actions such as retrieving data, writing to a system or selecting sources, preserve the action history required by firm policy.
Do not assume that a tool approved for drafting can also select investments, calculate performance, interpret regulations or handle confidential customer information. Using a system beyond its approved purpose creates a separate control question.
Assign a review level
Increase review depth when the output could cause a material client loss, concerns a complex or illiquid product, relies on forecasts, uses sensitive information, affects many clients, or will be distributed broadly. A low-impact internal summary may need source and arithmetic checks; a recommendation or public communication generally needs subject-matter and supervisory review under the firm’s procedures.
Step 2: Verify every material claim at the source
Build a claim-and-source list
Copy each material factual statement into a review log. Include prices, dates, returns, ratings, regulatory status, tax treatment, product features, economic assertions and statements about risks or historical performance. Distinguish facts from interpretation and from forecasts.
For each claim, open the cited document or database yourself. Do not rely on an AI-generated citation, a search-result snippet or a summary. Confirm all of the following:
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- The source exists and is authentic.
- The quoted or paraphrased material actually supports the claim.
- The source applies to the stated security, product, client type, geography and date.
- The information is current enough for the decision being made.
- Important context, exceptions or risk disclosures have not been omitted.
FINRA investor guidance advises: “Confirm the authenticity of underlying sources and review multiple sources of information before making investment decisions.” For a consequential claim, compare an issuer filing, regulator, exchange, official statistics agency or other primary source with an independent credible source when practical.
Check dates, versions and “as of” language
Separate historical facts, current observations, estimates and forecasts. A statement can be true for an earlier reporting period yet misleading today. Require an explicit “as of” date for market data, portfolio values, yields, credit metrics and macroeconomic indicators. Check whether a product prospectus, fee schedule, index methodology or tax rule has changed since the source was published.
Remove what cannot be substantiated
If a source is missing, inaccessible, outdated or inconsistent with the wording, do not leave the sentence in place merely because it sounds plausible. Replace it with a narrower statement that the source supports, add a clear qualification, or remove it. Never let a fabricated citation survive because the rest of the paragraph is useful.
Step 3: Recompute the quantitative work independently
Recalculate decision-driving figures
Recompute totals, percentages, rates, returns, drawdowns, scenario outputs, valuation multiples and period comparisons from the underlying data. Reperform at least the calculations that could change a recommendation or a client’s understanding of risk.
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- Verify dates and periods: trading days, calendar years, fiscal years, inception dates and the exact start and end points.
- Verify denominators: invested capital, average assets, beginning value, revenue or another stated base.
- Recheck formulas, compounding, cash flows, fees, taxes and currency conversion.
- Check rounding and sign conventions; a rounded intermediate value can materially change a final result.
- Label actuals, estimates, scenarios and forecasts separately.
A calculator or spreadsheet is useful for arithmetic, but it cannot establish that the inputs are authentic or that an assumption is appropriate. Preserve the input values, formula or code, output and reviewer’s result so another person can reproduce the check.
Test sensitivity and scenarios
Where the analysis depends on assumptions, vary the inputs that matter most: growth, inflation, interest rates, spreads, volatility, default, fees, taxes, contribution timing or holding period. Ask whether a small, plausible change reverses the conclusion. Present the range and the conditions under which it applies rather than treating one model output as a prediction.
Stay within the model’s understood limits
Federal Reserve, OCC and FDIC supervisory guidance describes validation as assessing a model’s reliability and limitations and identifying errors. It also says more material models warrant more rigorous oversight and that use should remain within understood limits. That guidance expressly excludes generative and agentic AI, so apply the validation concepts to covered traditional quantitative models and use them only as a governance analogy for generative systems.
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Step 4: Challenge assumptions, limitations and client fit
Make assumptions visible
Require the draft to state material assumptions in plain language. Look for implicit claims about risk tolerance, liquidity, tax status, time horizon, correlations, market conditions, reinvestment, costs and access to information. An output that gives a target return without naming the assumed volatility, holding period or loss scenario is incomplete.
Check intended use
Ask whether the system or model is being used for a purpose it was not designed or approved to support. Federal supervisory guidance warns that “Using a model beyond its intended purpose introduces additional uncertainty and risk.” A tool that summarizes filings may not be suitable for estimating fair value; a generic language model may not be suitable for calculating regulatory capital or determining suitability.
Test client-specific fit
Compare the proposed action with the client information actually available and current. Check objectives, financial situation, experience, risk tolerance, liquidity needs, tax considerations, restrictions and any special circumstances. Identify missing facts instead of silently filling them with typical assumptions. If the recommendation cannot be substantiated for this client, stop the workflow and obtain the needed information or escalate.
Look for one-sided framing and false certainty
Scan for absolute language, selective time periods, omitted downside cases, unsupported rankings, “guaranteed” outcomes, or a conclusion that is stronger than the evidence. Ask what reasonable alternative interpretation the output omitted. Require uncertainty to be tied to a specific driver, not hidden behind vague disclaimers.
Step 5: Review privacy, bias, vendors and communications
Protect customer and confidential information
Confirm that entering client data into the tool was permitted. Review the firm’s rules for personally identifiable information, account numbers, nonpublic financial information, material nonpublic information, retention, access, model training and deletion. Remove unnecessary identifiers from prompts and inspect the response for information about another customer or confidential source.
Assess vendor and third-party controls
Identify the provider, subcontractors, data locations, service terms, incident process and access controls required by your firm. Determine who can change the model, prompts, retrieval sources or guardrails, and how those changes are monitored. A vendor contract does not replace your firm’s responsibility to supervise outsourced activity.
Check for bias and misleading patterns
Review whether the output treats a demographic, geographic, language or product group differently without a legitimate basis. Test examples that could expose systematic omissions or stereotypes. Consider whether training data, retrieval sources or ranking logic could produce discriminatory or misleading recommendations, especially in credit, insurance, lending or other high-impact contexts.
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Apply communications and recordkeeping controls
Determine whether the draft is research, an advertisement, a retail communication, an investment-adviser communication or another regulated record. Apply the firm’s approval, disclosure, retention and review procedures. FINRA’s 2024 Annual Regulatory Oversight Report identifies accuracy, privacy, bias, intellectual property, books and records, public communications, research, supervision, customer information protection, Regulation Best Interest, model risk management and vendor management as areas AI use may implicate. It presents a supervisory perspective, not a single AI-only rulebook.
Step 6: Correct, document and obtain approval
Use a disposition for every issue
Classify each issue as corrected, qualified, removed, unresolved or escalated. Record the original wording, the problem, evidence supporting the correction and the person who made the decision. Material discrepancies should not be resolved by silently editing the final copy.
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Create an audit-ready review record
Retain, in the form required by firm policy:
- The final prompt or instructions that materially affected the output.
- The model or tool identity and date of use.
- Sources checked, publication dates and relevant excerpts or data snapshots.
- Independent calculations, inputs, formulas and scenario tests.
- Client facts and assumptions used for the recommendation.
- Privacy, vendor and communications checks.
- Issues found, corrections made, unresolved uncertainty and escalation.
- Reviewer and supervisory approval, with date and version of the final document.
Escalate before delivery when necessary
Escalate unsupported claims, fabricated or stale sources, material numerical differences, unexplained model behavior, sensitive-data exposure, suspected bias, conflicts, a recommendation outside the client’s profile, or a communication that cannot be substantiated. Delivery should wait until the appropriate supervisor, compliance professional, legal adviser or subject-matter reviewer resolves the issue under firm procedure.
How much review is enough?
There is no universal number of checks. Match effort to potential harm and the likelihood that an error will be missed.
| Output characteristic | Review response |
|---|---|
| Internal, low-impact summary with no recommendation | Verify material sources, dates and arithmetic; confirm no confidential data or unsupported certainty. |
| Client-specific analysis or recommendation | Perform full source traceability, independent calculations, assumption and suitability review, communications checks and documented human approval. |
| Complex, illiquid, leveraged or forecast-heavy recommendation | Add sensitivity or scenario testing, specialist review and explicit limitations; escalate unresolved discrepancies. |
| Broadly distributed or public communication | Apply the firm’s public-communications, research, disclosure, records and supervisory procedures in addition to factual and quantitative review. |
| Workflow using an agent that retrieves data or takes actions | Review permissions, guardrails, action logs, source selection and human approval before any client-facing result. |
This tiering is a practical control framework. The model-risk materiality language cited by federal banking supervisors applies to covered traditional models and institutions; it does not make generative AI subject to that guidance.
Independent reader supportYour contribution helps us test, update, and keep practical guides available for everyone.Regulatory boundaries you should not blur
| Source | Date or scope | What it establishes |
|---|---|---|
| FINRA, 2024 Annual Regulatory Oversight Report | January 2024 | AI use may implicate virtually every aspect of member-firm obligations, including communications, records, privacy, supervision, research, Regulation Best Interest and vendor management. |
| FINRA, 2026 Annual Regulatory Oversight Report | December 2025 | Discusses generative and agentic AI, human-in-the-loop protocols, guardrails and tracking agent actions and decisions. |
| Federal Reserve, OCC and FDIC model-risk guidance | Most relevant to banking organizations above $30 billion in total assets | Uses a risk-based validation and limitation framework; expressly excludes generative and agentic AI. Smaller institutions generally tailor practices to size and risk. |
| U.S. Government Accountability Office, GAO-25-107197 | May 19, 2025 | Describes federal financial-services oversight primarily through existing laws, regulations, guidance and risk-based examinations, while regulators continue assessing emerging risks. |
| SEC Chair Gary Gensler, “Chair Gary Gensler on AI Washing” | March 18, 2024 | Firms should accurately describe what they do with AI and should not claim an AI process or benefit they do not actually use. |
| FINRA/SEC/NASAA, “Artificial Intelligence (AI) and Investment Fraud” | Investor guidance | Warns that AI information can be inaccurate, incomplete, misleading, outdated or fabricated and advises authenticating sources and reviewing multiple sources. |
These materials do not create one checklist that overrides your firm’s policies. Your role, registration status, product, client, jurisdiction and communication channel determine which obligations apply.
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If a client asks how the analysis was prepared, answer accurately. Do not imply that a human performed work the system performed, claim that a model checked a source it never accessed, or suggest that an automated control provides certainty it cannot provide.
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“Investment advisers or broker dealers should not mislead the public by saying they are using an AI model when they’re not, nor say that they’re using an AI model in a particular way, but not do so.”
— Gary Gensler, SEC Chair, March 18, 2024
Truthful description does not mean presenting AI output as independent evidence. Explain the human review, source verification and limits that actually applied, using the firm’s approved language.
Common failure modes and recovery actions
A citation looks authoritative but does not support the sentence
Open the source, compare the exact passage and rewrite or remove the claim. Record the mismatch so similar outputs can be screened for the same pattern.
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The arithmetic is correct but the input is wrong
Trace the input to its date, unit, definition and source. Recalculate from an authoritative dataset and replace every downstream figure affected by the error.
The recommendation assumes facts not in the client file
Pause delivery. Obtain current client information or change the document to general education that does not imply personal suitability. Escalate if the recommendation was already communicated.
The tool exposed confidential information
Follow the firm’s incident and privacy process immediately. Preserve the relevant prompt and output, restrict further access, notify the designated security or compliance contacts and do not attempt to conceal the event by deleting records contrary to policy.
An agent acted without a meaningful human checkpoint
Disable or restrict the workflow until permissions, guardrails and action logging are reviewed. Require a named reviewer to approve consequential actions and verify the data the agent selected.
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- Define the client, deliverable, product, jurisdiction, date and whether the text recommends an action.
- Record the AI tool, version when available, prompt, inputs and authorized task.
- Mark every material claim and verify its authentic, current source and context.
- Cross-check consequential claims against another credible source where appropriate.
- Recompute key figures and test units, dates, denominators, formulas, fees, taxes and rounding.
- Separate actuals, estimates, forecasts and scenarios; test material sensitivities.
- Check intended use, assumptions, limitations, conflicts and client fit.
- Review privacy permission, vendor controls, bias, intellectual property and customer-information handling.
- Apply communications, records and supervisory procedures.
- Document corrections, unresolved issues, reviewer identity and approval; escalate anything that cannot be substantiated.
Bottom line for client-facing teams
The safe default is simple: no material AI-generated claim reaches a client until a human can show where it came from, reproduce the important numbers, explain the assumptions and demonstrate that the communication fits the client and the firm’s controls. AI can accelerate drafting and analysis, but it does not replace evidence, suitability judgment, supervision or accountability.
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