AI writing assistant — Accounting & finance

Turn financial models and tables into audit-ready commentary

Create earnings summaries, variance memos, forecast narratives, regulatory disclosures, and slide outlines — with prompts, reconciliation checklists, and an assumptions log designed for economists, FP&A, treasury, and audit teams.

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Targeted for accounting & finance

Why economists and finance teams use this assistant

Designed for professionals who must convert source data into clear, defensible commentary. The assistant emphasizes numeric fidelity, audit trails, and regulatory framing so narratives align with source schedules and reviewer expectations.

Reduce manual drafting time by using structured prompts that map directly to tables and model outputs.
Maintain consistency across memos, press releases, and board decks with tone and technical-depth controls.
Document assumptions and methodology alongside narrative text to simplify review and sign-off.

Prompt clusters tailored to economist workflows

Key capabilities and templates

Practical templates and prompt clusters guide the assistant to produce targeted deliverables while surfacing the source data and assumptions reviewers need.

Earnings release summary

3–5 bullet highlights + 1-paragraph summary that explains top-line drivers, cost trends, and cash-flow implications; includes a reconciliation sentence linking non-GAAP items to GAAP results and flags items needing footnote language.

Input: quarter P&L, balance sheet movements, management notes
Output: headline bullets + reconciled paragraph ready for press or internal memo

Variance analysis memo

Organize by revenue, gross margin, and operating expenses; quantify deltas, list probable causes, note one-offs, and propose actions management can take.

Input: current vs prior period tables and budget
Output: structured memo with suggested management actions

Forecast narrative & assumptions appendix

Write the forward-looking narrative, list key assumptions with sensitivity ranges, and add a methodology paragraph describing drivers and data sources.

Input: model worksheet or assumptions table
Output: narrative + assumptions log for reviewer sign-off

Regulatory disclosure draft (IFRS/GAAP)

Draft disclosure language suitable for IFRS or GAAP filings, call out measurement basis and judgment areas, and suggest cross-references to supporting schedules.

Input: transaction details and accounting policy
Output: disclosure draft formatted for statutory review

Executive briefing slides

Generate a 5-slide outline: executive summary, financial highlights, risks/assumptions, and recommended actions.

Input: topline metrics and 3 strategic points
Output: slide bullet outlines ready for deck integration

Audit-ready commentary with assumptions log

Annotate narratives with inline references to source tables and produce an assumptions log that records author, date, data sources, and rationale for judgments.

Input: draft narrative + source table references
Output: annotated commentary and a separate assumptions appendix

Where your inputs usually come from

Source ecosystems supported

The assistant is designed to work with common finance data sources so outputs can be cross-checked and traced back to originals.

Financial statements and supporting schedules (income statement, balance sheet, cash flows)
Spreadsheet models (Excel, Google Sheets) and CSV exports
ERP exports and general ledger extracts
Market and macro datasets (public time series and vendor data feeds)
Regulatory filings and accounting literature (SEC/IFRS/GAAP references)
Internal BI dashboards and treasury systems

Generation + validation steps

Practical QA workflow to reduce risk

A recommended lightweight review flow combines generated output with reconciliation checks and human sign-off to reduce hallucination and inconsistent statements.

1) Provide source tables and note the primary reconciliations you expect the draft to reference.
2) Generate narrative using an economist template that inserts inline table references.
3) Run a data-reconciliation checklist to confirm headline figures match source tables.
4) Attach an assumptions log (author, date, sources, judgment) to the draft.
5) Final reviewer signs off after confirming all cross-references and required disclosure language.

Drafts ready for use

Export formats & delivery

Outputs are structured for direct use in common deliverables so teams can move quickly from draft to distribution.

Memo and regulatory draft formats with clear section headings and inline references
Slide outlines and bullet exports optimized for copy/paste into presentations
Assumptions appendix and reconciliation checklist as separate exportable artifacts

Prompt clusters you can use right away

Example prompt patterns (copyable)

Concrete prompt examples tuned for economist tasks — adapt with your table names and figure references.

Earnings summary: "Draft a concise earnings summary (3–5 bullets + 1-paragraph highlight) using the provided P&L and balance sheet. Explain top-line drivers, cost trends, and cash-flow implications. Reconcile non-GAAP items to GAAP and flag footnote language needed."
Variance memo: "Produce a variance explanation organized by revenue, gross margin, operating expenses. For each variance, state the quantitative delta, probable cause, any one-off, and propose three actions management can consider."
Forecast appendix: "Write forward-looking narrative, list key assumptions with sensitivity ranges, and include a methodology paragraph describing model drivers and data sources."

Primary audience

Who this is for

Built for professionals who need to convert numeric analysis into clear, defensible narrative.

Corporate economists and macro teams
FP&A and financial analysts
Accounting managers and regulatory reporting specialists
Treasury analysts, valuation/M&A professionals, and audit teams preparing management letters

FAQ

How does the assistant ensure narratives match source numbers and avoid contradictions?

Use the assistant's data-to-narrative prompts that require you to attach or reference source tables. The recommended workflow includes inline source references, a reconciliation checklist that compares headline figures to inputs, and an assumptions log. These steps guide reviewers to confirm numeric fidelity before sign-off.

Can I generate IFRS-style disclosures or adapt language for GAAP reporting?

Yes. Choose the regulatory framing option in the prompt (IFRS or GAAP). The assistant provides disclosure-language drafts that call out measurement basis, required judgments, and suggested cross-references to supporting schedules — but final legal review by accounting and compliance teams is required.

What steps should I take to validate and sign off AI-generated financial commentary?

Apply a simple sign-off workflow: 1) confirm inputs and source table versions, 2) run the reconciliation checklist, 3) review the assumptions log, 4) have a subject-matter expert verify judgment areas and disclosure language, and 5) archive the reviewed draft with reviewer initials and date.

How do I document forecasting assumptions and maintain a review trail for audits?

Include an assumptions appendix generated by the assistant that lists each assumption, sensitivity range, data source, author, and date. Store the appendix alongside the model version and reviewer notes so auditors can trace decisions to sources and sign-offs.

Which data sources should I provide for the assistant to produce reliable economic analysis?

Provide the primary financial tables (P&L, balance sheet, cash flow), model worksheets or CSV exports, and any macro or market series you referenced. Add short notes on data currency and known adjustments so the assistant can surface them in the narrative.

How can I control tone and technical depth for different stakeholders (CFO vs investor relations)?

Use tone and audience modifiers in prompts — for example, request 'CFO-level: technical with line-item detail' or 'Investor-relations: plain language, one-paragraph summary.' The assistant adapts phrasing and verbosity accordingly; always validate technical points for board or regulatory distribution.

What human review practices reduce the risk of unsupported conclusions in AI drafts?

Require explicit source references in the draft, keep an assumptions log, mandate reconciliation checks, and involve a subject-matter expert to vet judgment calls and disclosure language. Treat the AI output as a draft that speeds drafting but does not replace domain review.

How do I convert assistant outputs into filing-ready formats or board slide decks?

Select the export format suited to your use case: memo/disclosure text for filings, or slide-outline output for decks. Copy the outline into your slide tool, attach the assumptions appendix, and run the final compliance and editorial review before distribution.

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