Let AI write the words and never the numbers. Export performance, holdings and fee figures from your portfolio system, give the model only the manager's notes and a locked numbers table, merge the figures in, and send each draft to compliance with a claims checklist and a number audit. Nothing about returns, rankings or forecasts should come from the model.
A compliance reviewer does not object to AI as such. They object to statements nobody can trace: a return that matches no system, a "top-performing" label with no source, a sentence about "our AI-driven process" that nobody can evidence. In March 2024 a securities regulator settled charges against two advisers over overstated AI claims, with penalties of $225,000 and $175,000, so even a claim about your own use of AI needs a source.
The production line: who writes what
A quarterly client report and a strategy fact sheet are built from the same parts, and the parts divide cleanly into those a system produces, those a person writes and those AI may draft. Settling that division is most of the work. Ask your compliance officer one more question at the start: your regulator may treat a fact sheet that circulates widely as marketing and a statement sent to one client as reporting, with different approval rules for each.
| Part of the document | Source | AI's role |
|---|---|---|
| Performance table, benchmark, holdings, allocation | Portfolio system export | None. Values are locked and merged in |
| Fees and risk indicator | Fee schedule, risk statement | None. Standard wording, version-controlled |
| Objective and strategy description | Investment policy or prospectus wording | Plain-English rewrite only if compliance approves the new text once |
| Quarter commentary | Manager's notes plus the numbers table | Drafts, the manager edits, compliance reviews |
| Personal covering note | Client situation category | Drafts a library of variant paragraphs, reviewed once |
| Disclaimers and past-performance warning | Legal text approved by compliance | None. Locked |
This is a different arrangement from asking a chatbot to write each client's letter. The model never sees client names or balances, and it never produces a figure. The words are reviewed once per strategy and once per paragraph variant, not 140 times. For the commentary sign-off ladder in a fund-management setting, see AI tools for asset management: factsheet commentary with sign-off. This tutorial concentrates on the quarterly client report, the numbers and the claims.
It helps to see how the three documents differ before drafting anything, because the review path differs with them. The routes below are typical; confirm yours with compliance.
| Document | Reader | Likely review path | What AI can draft |
|---|---|---|---|
| Strategy fact sheet | Anyone who finds it | Marketing approval each time it changes | Commentary, and a plain-English objective once |
| Quarterly client report | One client | Template approved once, then sample checks | Template text and explainer blocks |
| Covering note | One client | Variant library approved once, then sample checks | The variant blocks only |
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Buy nowA quarterly cycle for 140 clients, in hours
Take an illustrative small manager with three model strategies, 140 client reports and three strategy fact sheets a quarter. Before AI, the manager writes a covering note and adjusts the market commentary for each client, about 25 minutes apiece. The three fact sheets take about three hours each. Compliance reviews each fact sheet and its commentary for 30 minutes apiece and samples 20 per cent of the covering notes, 28 notes at ten minutes each.
| Task | Before | With the production line |
|---|---|---|
| Commentary and covering notes | 140 x 25 min = 58.3 h | Commentary 3 x 45 min = 2.25 h; notes 140 x 6 min = 14.0 h |
| Fact sheets | 3 x 3 h = 9.0 h | 3 x 1.25 h = 3.75 h |
| Compliance review | 3.0 h plus 4.7 h sample = 7.7 h | 3.0 h plus 4.7 h sample = 7.7 h |
| Total | 75.0 h | 27.7 h |
The saving is about 47 hours a quarter. Notice what did not shrink: compliance review. It stays at 7.7 hours because it should. The time comes out of drafting and assembly, and a firm that cuts review time to match has stopped reading what it sends. The figures are assumptions, so time your own last quarter before promising anyone a saving.
Step one: lock the numbers before the model sees a draft
Export a numbers table at strategy level, not client level. Everything the commentary may quote is on it, with its source and date. These figures are made up, to show the shape:
| Item | Strategy A | Benchmark | Source |
|---|---|---|---|
| Quarter return, net of fees | 2.4% | 2.9% | Portfolio system, [export date] |
| 12-month return, net of fees | 7.8% | 8.1% | Portfolio system, [export date] |
| Largest holding weight | 4.2% | n/a | Portfolio system, [export date] |
| Ongoing fee | 0.65% | n/a | Fee schedule, current version |
Two habits keep the table honest. Every row states its period and whether it is net or gross of fees, because a number without that label is where misleading comparisons begin. And the model is told to use only what is on the table. If a number is not there, it must write a placeholder, not an estimate.
Step two: commentary from the manager's notes
The manager supplies short factual notes about what was done and why. The model turns them into readable text. It does not supply reasons, forecasts or market colour of its own.
Draft the quarter commentary for Strategy A, about 120
words, plain English, for retail clients.
Use ONLY the manager's notes and the numbers table below.
Quote returns exactly as shown, with the period and "net
of fees". Do not mention any period, ranking, forecast or
reason that is not in the notes. If something you need is
missing, write [NOTES MISSING: what].
End with the standard sentence: "Past performance is not a
reliable guide to future results."
MANAGER'S NOTES: Trimmed energy, added to healthcare. Bond
holdings held back by interest-rate worries. Target
allocation unchanged.
NUMBERS TABLE: [paste the table]A good draft reads: "In the quarter, Strategy A returned 2.4% net of fees, against 2.9% for its benchmark. Over twelve months it returned 7.8% net, against 8.1%. We trimmed energy holdings and added to healthcare. Bond holdings were held back by concern about interest rates. The target allocation is unchanged. Past performance is not a reliable guide to future results." The usual defects are quiet ones. A model may add "reflecting our defensive positioning", an explanation the manager never gave, or "continuing a strong three-year record" when the table has no three-year figure. Delete both. If the manager would rather give the reason, they add it to the notes, so the reason traces to a person.
The same rule covers market commentary. Paste the source, whether your own house-view note or research you are licensed to quote, and ask the model to summarise it and say where each point came from. A model's own memory of recent markets is unsourced and may be out of date.
Step three: variant paragraphs instead of 140 AI letters
Most covering notes differ in a few ways: money added, money withdrawn, a rebalance, a model switch, or nothing happening. So write a library of variants, one per situation, with tokens for the figures, and have the reporting system pick the right one for each client.
Write five variants of a two-sentence covering note for a
quarterly report. Situations: (1) client added money,
(2) client withdrew money, (3) portfolio was rebalanced,
(4) client changed model, (5) no activity. Friendly, plain
English. Use tokens for all figures, such as
{QUARTER_RETURN_NET} and {CLIENT_VALUE}. Do not state any
number, date or performance judgement.Compliance reviews the five variants once, they receive IDs and version numbers, and every client's note is assembled from approved blocks with figures merged in by the system. Nothing client-specific passes through the model, and the reviewer can read the whole library in ten minutes. If your reporting platform cannot merge blocks, a spreadsheet mail-merge does the job.
The fact sheet: three blocks change, the rest stay locked
A strategy fact sheet looks like a lot of writing and is mostly not. Of the blocks in the production-line table, only three change each quarter: the performance table, the top-holdings list and the commentary. The objective, strategy description, fees, risk wording and disclaimers stay as approved until something real changes. So the fact-sheet job is a refresh, not a rewrite: export the new table, draft the new commentary from the notes, merge, and run the audit. That is why the fact-sheet row in the time table drops from three hours to 1.25.
The one block where AI may help, once, is the plain-English objective. Before: "The strategy seeks to achieve long-term capital appreciation by investing primarily in a diversified portfolio of global equity securities, subject to the constraints set out in the investment policy." After: "The strategy aims to grow your money over the long term by investing mainly in a spread of company shares from around the world." The second version is friendlier, and it is also a new statement, because "grow your money" can sound like a promise that "seeks to achieve" did not. Compliance should approve it as new wording, perhaps softened to "aims to grow the value of your investment over the long term, which cannot be guaranteed". Rewrites like this belong in the approved library, reviewed once, not regenerated every quarter.
Step four: the number audit
Even with locked tables, commentary drifts. A model may round 2.4% to "about 3%" or restate a period. So audit every draft mechanically:
- Ask the model: "List every number, percentage, date and period in this draft in a table: item, value, sentence it appears in."
- Paste the list into an audit sheet next to the numbers table. Store both columns as text with the same formatting, so "2.4%" matches "2.4%".
- In a third column, test each value, for example
=IF(COUNTIF(Source!$B:$B,B2)=0,"NOT IN SOURCE","found"). - The reviewer resolves every NOT IN SOURCE line before the draft goes forward.
Take a draft in which the audit flags "3-year record" and "about 3%". The first has no source, so the sentence goes. The second is a rounding of 2.4%, so the exact figure replaces it. The audit takes about five minutes per strategy, and it is the check that makes "no performance claims invented" something you can demonstrate. If you want help writing the formula, the ChatGPT for Excel guide covers it.
Claims the model must not make
Give the reviewer, and the model, a claims register. Each row is a type of statement with a rule:
| Statement type | Example | Rule |
|---|---|---|
| Past performance | "Returned [x]% net over the quarter" | Only from the numbers table, with period, net or gross basis and benchmark, and the standard warning nearby |
| Comparison | "Beat the benchmark" | Only if the table shows it for that period on the same basis |
| Ranking or rating | "Top-quartile" | Needs a named source and date. Otherwise leave it out |
| Forecast | "We expect rates to fall" | Labelled as opinion, and compliance decides whether it stays |
| Guarantee or safety | "Protected", "safe", "no risk" | Never |
| Process claim | "Our AI-driven process finds what others miss" | Only if true and evidenced on file |
You can also ask the model to act as a second pair of eyes: "Flag every sentence containing a comparison, ranking, forecast, guarantee, superlative or claim about our use of technology." It will catch most of them, and it will miss some, so it supplements the reviewer and never replaces one. Here are two before-and-after pairs that show the pattern. "Our disciplined process has consistently outperformed the market" becomes "Over the quarter the strategy returned [x]% net of fees against [y]% for [benchmark]. Past performance is not a reliable guide to future results." The first version made a claim; the second states two numbers with sources. And "We expect rates to fall by year end" either goes, or becomes "In our view..." with compliance's explicit approval.
What the compliance reviewer receives
A reviewer given a PDF and a deadline can only skim. Give them a packet instead:
- The draft, with AI-assisted sections marked.
- The numbers table it was built from, with export dates.
- The audit sheet showing every figure found in source.
- A comparison against last quarter's approved text, so only the changes need close reading.
- The claims register, with the model's flags and the manager's answers.
- The manager's notes the commentary was drafted from.
One item in that packet is easy to produce with AI and hard to do well by eye: the comparison against last quarter. Paste both approved texts and ask:
Below are (A) last quarter's approved commentary and (B)
this quarter's draft. List every difference as a numbered
list: what was removed, what was added, what changed in
wording. Do not judge whether any change is acceptable.The output might read: "1. Sentence 3 now mentions healthcare, where A mentioned technology. 2. The outlook sentence in A has been removed. 3. The benchmark return is now 2.9%, where A had 3.4%." The reviewer still reads the new text in full. The list tells them where to look hardest, and a removed outlook sentence is the sort of change worth a question.
Record approvals with a name, a date and the version approved, and archive the final document with the numbers table and the notes. Retention periods depend on your regulator, so ask compliance for the rule. Agree a calendar too: drafts to compliance five working days before the send date, which stops the review being squeezed on the last afternoon.
Tools and what they cost
You do not need specialist software to start. A general assistant on a business plan covers the drafting. ChatGPT Business and Claude Team both list standard seats at $20 a user a month on annual billing or $25 monthly, with a two-seat minimum, and business plans do not train on your content by default. For three people that is $60 a month annually. If the firm is on a Microsoft 365 business plan, Copilot Chat is included at no extra cost, and Microsoft 365 Copilot Business is listed at $21 a user a month on annual billing (Microsoft's page shows $18 under an offer that runs to 31 December 2026). Check the vendor's terms on retention and sub-processors before the manager's notes go in.
If your reporting platform offers AI commentary, ask it four questions: does the model see only the locked numbers, can the text be edited and approved before release, is there a log of who approved what, and is client data ever used for training? The answers decide whether it fits the production line. Fuller advice on what to pay for is in best AI for investment management firms: what to pay for and skip, and a buying checklist is in AI software for wealth management: a small firm's evaluation checklist.
A four-week pilot
Week one: build the numbers table for one strategy, write the claims register with your compliance officer, and agree what AI-assisted material has to be flagged. Week two: draft that strategy's commentary from last quarter's notes and compare it, line by line, with the text that was approved at the time. Week three: build the five-variant library and have compliance approve it. Week four: run the whole line in parallel with the old process for that strategy and a sample of 20 clients, count discrepancies, and ask the reviewer what they would change in the packet. Switch over only if the audit and the parallel run agree. The cost for the month is at most $75 in subscriptions and perhaps 12 hours of the manager's time.
How to tell the process is working
Four measures tell you. First, corrections per draft: count the edits compliance makes each quarter, and expect the number to fall as notes and prompts improve. Second, audit flags per draft: a handful at first, trending towards none. Third, reconciliation: each quarter, pick one client report at random and trace every number back to the portfolio system by hand. That is the check that catches a broken merge. Fourth, a planted-gap test: give the model a notes sheet with one figure deliberately missing and confirm it writes a placeholder instead of inventing a value. Repeat the planted-gap test whenever the model or your plan changes, since versions move monthly and behaviour moves with them.
Where it goes wrong
These are illustrations of how the process fails, not reports of real events.
- Last quarter's view: the notes are thin, and the model fills the gap by echoing last quarter's outlook paragraph. The "[NOTES MISSING]" instruction and the comparison against the last approved text are the guard.
- The paste: a junior pastes a client statement into a free chatbot to draft a note quickly. Client data has left the building. The fix is a written rule, a business account that is quicker to reach, and the variant library that removes the need.
- The squeezed review: the draft reaches compliance the afternoon before sending. The packet is skimmed and a stale benchmark line goes out. The five-day rule is cheaper than a correction letter.
- The rounding: the model writes "about 3%" for 2.4%. Nobody is lying, and the audit sheet is why nobody has to argue about it.
If you would rather map this production line onto your own reports with someone, that is what my AI implementation consultation covers, and the contact page is the place to start. A related routine for proposals, where the same numbers discipline applies, is in can AI write an investment proposal: draft narrative, check numbers.
Questions managers ask before AI touches a client report
Can AI write the performance section of a fact sheet?
It should not produce the numbers. Performance, benchmark and fee figures come from your portfolio system and sit in the document as locked values or merge fields. The model can draft the sentences around them, using placeholders, and the reviewer checks that each figure, period and net-or-gross label matches the numbers table.
Do I have to tell compliance that AI helped draft a report?
Yes, as a matter of good practice, and check whether your firm's own rules require it. Record which sections were AI-assisted, which tool was used and who reviewed them in the approval record. A reviewer who learns about AI afterwards will reasonably wonder what else was not mentioned.
Is it safe to paste client portfolio data into ChatGPT?
Avoid it. Work at model-portfolio level, keep names and account numbers out, and use a business plan, since ChatGPT Business and Claude Team do not train on business content by default. Free personal accounts are the wrong place for anything client-related. Read the retention terms and ask compliance before any client-level data goes in.
Can AI summarise market conditions for the commentary?
Only from sources you supply, such as your own house-view note or research you are licensed to quote. A model's own memory of recent markets is unsourced and often out of date. Paste the source, ask for a summary that cites it, and treat any forecast as an opinion that compliance decides whether to include.
Further reads
- AI for Wealth Management Firms: A 90-Day Rollout Plan for Small Firms — A staged rollout for a small firm once the report process works.
- Best AI Tools for Investment Management: Committee Packs and Minutes — Applies the same discipline to committee packs and minutes.
- AI in Wealth Management Examples: Five Admin Jobs Worth Trying — Five admin jobs worth trying before anything client-facing.
- AI in KYC for a Small Wealth or Advice Firm: Checks That Stay Human — Which onboarding checks must keep a person making the decision.
- AI Tools for UHNW Wealth Managers: Seven Jobs With Privacy Rules — Privacy rules for high-net-worth client work.
- AI for Wealth Managers: Drafting Proposals With a Compliance Review — Drafting proposals with a compliance review built in.
- Performance Max Reporting: Explain Asset Results to Clients With AI — Turn a Performance Max asset export into a client explanation that says what the ratings mean, what they don't, and what to change next.
- AI Tools and AI Development: The Complete 2026 Guide — the AI hub, including every tutorial in the AI-for-business series.
Sources: ChatGPT Business, Claude and Microsoft 365 Copilot business pricing pages (checked November 2026); a securities regulator's 18 March 2024 press release on two advisers' AI claims; worked figures, numbers tables and sample text are illustrative and are not performance claims.