Use-case library
    Record-to-Report

    Flux Analysis & Variance Narratives

    Draft traceable explanations for every material P&L and balance-sheet swing at close.

    CopilotRole agentSpeedQualityMedium effortAugmentedCFO
    What changed

    Generative AI can now read the trial balance, prior periods, budget and driver data, then write the variance commentary that analysts used to assemble by hand — with the supporting numbers linked back to source.

    Value realized on KokoAI
    Variance commentary per close
    40 → 12 hrs
    lower is better
    Recurring impact
    $1.2M/yr
    run-rate benefit
    Payback
    4 months
    ≈ 5,000 hrs/yr reclaimed

    Drafting month-end variance commentary drops 40→12 analyst-hours per close across the entity set; ≈5,000 hours/yr and ≈$1.2M/yr of recovered analyst capacity, every figure traced to source.

    Run this in Claude on KokoAI's data

    Opens your own Claude with the prompt and KokoAI's p&l for variance / flux analysis pre-loaded as text — no upload, no setup. Tailor it live to your own numbers.

    Synthetic KokoAI figures — a demo fixture, not audited or a guarantee

    Try it

    Illustrative demo

    Worked example of "Flux Analysis & Variance Narratives" on synthetic data — edit the inputs to tailor it. Edit the inputs to tailor the output — it's baked synthetic data, so nothing leaves your browser until you run it live.

    Synthetic demo company (a ~$5B high-growth US AI SaaS) — swap in any name to tailor the output.

    What this run should concentrate on.

    Run live in Claude
    Sample data

    P&L for Variance / Flux Analysis (synthetic) — grab the one-pager PDF (the ROI, the data, and the prompt on one sheet) to share or keep, or the raw CSV / Markdown to drag into Claude or Microsoft Copilot. The prompt hand-off above already includes the same data as text.

    Synthetic KokoAI demo data — not audited

    Run it on your own document

    Drop a PDF, Excel, Word, CSV, or text file (e.g. a 10-Q, an AR aging, an invoice register) and we'll run this use case on it. Your file is read in your browser and never uploaded — only the extracted text goes into your own Claude or Copilot.

    Choose a file or drop it herePDF · XLSX · DOCX · CSV · TXT · up to 15 MB

    Read in-browser · not uploaded · 10-Q/10-K trimmed to financials + MD&A

    Workflow

    1. 1.Pull current actuals, prior period, budget/forecast and the GL detail behind each account.
    2. 2.Compute variances and rank by materiality against a set threshold.
    3. 3.For each material swing, retrieve driver data (volume, rate, mix, one-offs) and draft an explanation.
    4. 4.Attach evidence links to the journals/sub-ledger lines behind each number.
    5. 5.Route the draft commentary to the controller for review and edit.

    Prompt / agent recipe

    You are a financial analyst. Given the attached actuals, prior-period, budget and GL detail, identify every account whose variance exceeds [$X / Y%]. For each, write a 1–2 sentence plain-English explanation grounded in the driver data, cite the source rows, and flag anything you cannot explain from the data provided. Do not speculate.
    Try in Ask KokoAI
    Controls required
    • Materiality threshold set and documented before the run
    • Every figure traceable to a GL/sub-ledger source row
    • Controller reviews and approves narrative before it leaves finance
    • Model output marked AI-drafted in the working papers
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