Tech & AIBeginnerPreview
AI for Finance & Accounting Tasks
A hands-on course for accountants, bookkeepers, finance teams, and owner-operators who want AI to do the slow narrative parts of finance work: reading a 10-K or month-end pack into a clean summary, explaining a P&L or balance sheet to non-finance people, writing the memo behind every reclassified expense, and drafting investor and board updates from the numbers. You leave with a reusable prompt library, a verification habit that catches AI's math and hallucination errors, and a controls posture that keeps client data and PII safe.
For accountants, bookkeepers, FP&A analysts, controllers, and small-business owners who read and report on numbers and want AI to summarize and draft faster without errors or data leaks.
Course content
Workbook & downloads
Put the course into practice — a printable workbook plus editable templates you can fill in and reuse.
Preview the workbook
This workbook turns the course into tools you run on every close, report, and filing: a finance-safe AI setup, a reusable prompt skeleton and library, a report-summary brief, plain-English statement explainers, expense and journal-entry narrative builders, variance and board-update generators, and the six-step verification routine that makes every output safe to send. Work through one section per module, filling the worksheets and running the prompts in ChatGPT (GPT-4o) or Claude as you go. By the end you will have a complete, reusable finance AI toolkit with verification built in, so AI does the reading and the writing while you keep full control of the numbers.
Setting Up AI for Finance Work
Lock in your data-protection setup, adopt the recompute-everything habit, and build the prompt skeleton every later exercise depends on.
Checklist: Finance-Safe AI Setup Checklist
- Chosen a no-training environment for any client or confidential data (ChatGPT Team/Enterprise, OpenAI API, or Claude Team/Enterprise)
- Turned off Improve the model for everyone in ChatGPT Settings, Data Controls (for any personal account use)
- Decided which data is never pasted: SSNs, bank and card numbers, full PII, unannounced earnings, M&A figures
- Adopted the redaction habit: Customer A, Vendor 3, Employee 12 in place of real identifiers
- Identified where human sign-off lives and that the ledger and workpapers — not the chat — are the system of record
- Confirmed a code/analysis tool is available for real math (ChatGPT Advanced Data Analysis or Claude Analysis tool)
- Started a written note of what data is fed to which tool, for audit and client questions
Worksheet: Your AI Data-Handling Policy
Fill in one line per field to create a one-page policy you and your team follow. Keep it beside your prompt library.
- Approved AI tool and tier for confidential finance data
- Approved tool for non-sensitive / de-identified work
- Data categories that are NEVER pasted into any AI tool
- Redaction convention (how names, accounts, and PII are masked)
- Tool used to actually compute totals, ratios, and percentages
- Who reviews and signs off AI-assisted figures before they leave
- Where the audit note of AI usage is kept
Exercise: Build Your Five-Part Prompt Skeleton
Draft each part once, then save the Role, Source, and Grounding lines as a fixed header you paste atop every finance prompt. Add it as the first entry in your prompt library.
- Write the Role line: who the model is and who it is writing for (e.g. a senior management accountant writing for a non-finance reader).
- Write the Source line that restricts the model to only the figures you paste and forbids inventing or estimating any number.
- Write the Grounding line that tells the model to write UNKNOWN and ask you when a figure is missing, and to end with the questions a CFO would ask.
- Combine the three into a reusable header block and paste it at the top of one real prompt to confirm it changes the output.
Summarizing and Explaining Financial Information
Turn a long document into a verified structured brief, and turn each statement into a plain-English explanation tuned to a real reader.
Exercise: Summarize a Filing or Month-End Pack
Take one real (or sample) 10-K, annual report, or board pack. Run the structured-summary prompt, then complete the verification before you rely on the brief.
- Paste your fixed header, then state the lens: the exact decision or reader this summary serves.
- Demand a fixed structure: headline numbers (revenue, net income, margins, cash, debt/covenants), what changed and why, risks and flags, and open questions.
- Cap the length to a one-page brief and require a Numbers to verify list of every figure pulled.
- Tie each figure in the Numbers to verify list back to the source statement or note, confirm nothing was invented or dropped, and re-derive every percent change yourself.
Worksheet: Plain-English Statement Explainer
For one company, fill the figures, name the audience, then run an explainer prompt per statement. Record the explanation and your recomputation of each ratio.
- Income statement: revenue, COGS, operating expenses, net income
- Balance sheet: total assets, total liabilities, equity
- Cash flow: operating, investing, financing, net change in cash
- Target reader (e.g. department head, founder, board member, loan officer)
- Gross margin — formula shown by AI and your recomputed value
- Operating and net margin — formula shown and your recomputed value
- One why-it-matters sentence per headline number
- Glossary of any terms kept for the reader
Exercise: Interpret Ratios and a Trend
Compute the ratios and time series yourself or in a code/analysis tool, then hand the verified values to AI to interpret. Treat its explanations as hypotheses to investigate.
- Paste your verified ratios (current, quick, gross/operating/net margin, DSO, inventory turnover, debt-to-equity, interest coverage) and ask for an interpretation for your specific business plus the three questions to investigate.
- Paste a clean, labeled monthly time series (e.g. revenue and gross margin) and ask for the trend, inflection points, and hypotheses to test, clearly labeled as hypotheses.
- Ask which additional data would confirm or rule out each hypothesis, then verify any figure the model restates against your records.
Checklist: Summary & Explanation Verification
- Every headline figure spot-checked against the actual statement or note
- No number present that is not in the source (no hallucinated line items)
- Nothing material dropped (covenant, restatement, going-concern, change in estimate)
- Every percent change and margin re-derived independently
- Each ratio formula shown by AI and recomputed by you
- Trend explanations treated as leads to confirm, not findings to publish
Narrating Transactions and Expenses
Generate consistent, policy-aligned narratives for expenses, journal entries, accruals, and reconciliation exceptions — while you own every classification.
Worksheet: Expense Narrative Inputs
Fill one row per transaction (use de-identified vendors if sensitive), then feed the table to your narrative prompt and paste the results back beside each row.
- Vendor (or Vendor A/B/C if sensitive)
- Amount and currency
- Date and frequency (one-off / monthly / annual)
- Business purpose in one line
- Chosen account name and number
- Your one-line rule for why it belongs in that account
- Capital, split, or accrual consideration (Y/N + note)
- Generated narrative (paste back here)
Exercise: Draft a Journal-Entry / Accrual Memo
Pick one non-routine entry (accrual, reclass, prepaid amortization, correction). Decide the treatment yourself first, confirm it against the standard or policy, then have AI write the memo.
- State the business event, the entry (debits and credits), the amount, and exactly how the amount was calculated (rate, days, basis).
- Ask for a structured workpaper memo covering event, accounting rationale and the principle that supports it, amount basis, accounts, and any reversal or schedule.
- Recompute the amount, confirm debits equal credits, and check the rationale cites the correct principle and matches the treatment you actually chose.
Exercise: Write Reconciliation Exception Notes
Complete and verify the reconciliation or investigation in your software first. Then give AI only the verified items to narrate.
- Paste your verified reconciling items (deposits in transit, outstanding checks, fees, timing differences) and ask for a one-line audit-ready note per item.
- For an exception, give the expected figure, actual figure, difference, and the cause you found, and ask for a clear workpaper note plus follow-up questions to check.
- Confirm each note matches reality and reject any note that introduces a cause you did not supply.
Checklist: Narrative Integrity Checklist
- Each narrative matches the account and treatment you actually chose, not one the model picked
- No transaction silently recategorized by the model
- Items needing capitalization, splitting, or accrual were flagged, not smoothed over
- NEEDS REVIEW raised on genuinely ambiguous rows
- Reconciliation math and balancing done by you or software, never by chat
- Client and account identifiers kept out unless in a no-training environment
Reporting to Stakeholders
Turn verified numbers into variance commentary, board and investor updates, and stakeholder emails, then run the standing verification routine before anything ships.
Worksheet: Variance Commentary Inputs
Pull and verify the budget-versus-actual figures, then investigate each material variance before you write a word. Fill this, then run the commentary prompt.
- Headline result: net income actual vs budget
- Driver 1 — line, amount of variance, confirmed cause
- Driver 2 — line, amount of variance, confirmed cause
- Driver 3 — line, amount of variance, confirmed cause
- One-off items separated from ongoing trend
- Forward look: what leadership should watch or decide next period
- Any variance still under investigation (mark as such, do not guess)
Exercise: Draft a Board and an Investor Update
Use your verified metrics and bullet points. Draft both documents from the same numbers but for their different audiences.
- For the investor update, provide real MRR/revenue, growth rate, cash, and runway, plus your wins, challenges, and asks, and request the TLDR / key metrics / highlights / lowlights / asks / thanks structure.
- For the board narrative, ask for candor: the result, the key risks, and the specific decisions the board needs to make this period.
- Set the tone (confident but honest, plain not hype) and tell the model to avoid spin that buries bad news and jargon that hides the story.
Exercise: Draft a Stakeholder Email
Pick one real situation (collections, a lender question, a budget reply, an invoice discrepancy). Give AI the facts, the recipient, and the tone, restricting it to the figures you supply.
- State the situation, the verified numbers (invoice number, amount, days overdue or balance), the recipient and relationship, and the tone you want.
- Cap the length (e.g. under 120 words) and tell the model to use only the facts you provide and to invent no amount, date, or balance.
- Before sending, confirm the invoice number, amount, and dates against your records.
Checklist: Six-Step Verification Routine (run on EVERY output)
- Recompute: redid every total, margin, ratio, and percent change yourself or in a code/analysis tool
- Trace: confirmed each number appears in the source provided — nothing invented, nothing dropped
- Check treatment: any accounting judgment matches the one you actually decided
- Read for spin: tone is honest, nothing material buried or overstated, metrics stated identically throughout
- Protect data: no PII or confidential figure went into a training-enabled tool
- Sign off: a qualified human approved it and the workpaper, not the chat, is the record
Your Action Plan
- Set up your finance-safe AI environment: pick a no-training tier for confidential data, switch off training on any personal account, and confirm a code/analysis tool for real math.
- Write and save your five-part prompt skeleton (Role, Source, Task/audience/format, Constraints, Grounding) as a reusable header in a single prompt library.
- Adopt the redaction habit and write your one-page AI data-handling policy, then decide where human sign-off lives.
- Build and verify a structured-summary prompt by running it on one real filing or month-end pack, ending with a Numbers to verify list.
- Create one plain-English explainer prompt per statement (P&L, balance sheet, cash flow) per core audience, with formulas shown so you can recompute.
- Build an expense-narrative prompt seeded with your chart of accounts and house style, and run it on one month of transactions.
- Build a journal-entry / accrual memo prompt and a reconciliation-exception-note prompt, deciding every treatment yourself first.
- Build a variance-commentary prompt and require that you investigate each driver before the model narrates it.
- Save a board-narrative prompt and an investor-update prompt in the TLDR / metrics / highlights / lowlights / asks / thanks structure.
- Pin the six-step verification routine to the top of your prompt library and run it on every AI output before it leaves your hands.
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