Automate Finance Narrative Reporting with Claude AI

Standardise Your Reporting Inputs for Claude

Before you ask Claude to write commentary, ensure your inputs are consistent. Export P&L, balance sheet and cash flow statements from your ERP in a standardised, table-based layout: the same column order, metric names and sign conventions every period. Where possible, enrich these exports with additional fields (e.g. cost center owner, segment, region) so Claude has context for explanations.

Then, wrap these exports with a short textual header that explains the report type, period, and comparison baseline (e.g. vs. budget, vs. last year). This gives Claude both structured and unstructured cues to work with, improving the accuracy of its variance analysis.

Use a Modular Prompt Template for Variance Commentary

Instead of writing a new prompt every month, define a reusable template for variance analysis and narrative generation. Structure the prompt in modules: role, task, data description, style guidelines, and output format. Here is an example pattern for monthly P&L commentary:

You are a senior finance analyst preparing monthly management commentary.

Task:
- Analyse the provided P&L tables for the current period vs. comparison period.
- Identify the top 5 positive and top 5 negative variances by absolute value and by %.
- For each, explain the main driver based on line item, segment and region.
- Distinguish between structural effects (e.g. headcount changes) and one-offs.

Data:
- You will receive P&L data as tables exported from our ERP.
- Column "Period" indicates current vs comparison.
- Column "Scenario" indicates Actual, Budget, Forecast.

Style:
- Write in concise, neutral management language.
- Avoid speculation; only use drivers that can be inferred from the data.
- Use bullet points for variance lists, then a short narrative summary (max 300 words).

Output format:
1) Short executive summary (max 5 sentences).
2) Bullet list of key variances with numbers.
3) Narrative commentary section suitable for our monthly report.

Here is the data:
[PASTE TABLES HERE]

Store this template in your reporting playbook or internal tool so analysts use a consistent approach each period. Over time, refine it with your own terminology and recurring drivers.

Chain Tasks: From Raw Data to Final Commentary

For reliable results, break the workflow into distinct steps instead of asking Claude to “do everything at once”. A robust chain for automated financial reporting narratives could look like this:

Step 1 – Data check: Ask Claude to verify that periods, currencies, and totals reconcile, and to flag obvious inconsistencies.

First, review the tables and check:
- Do total revenue and total expenses add up correctly?
- Are the periods and currencies consistent?
- Are there any missing or duplicate lines?

Respond with a short diagnostic summary before you start any commentary.

Step 2 – Variance extraction: Have Claude produce a structured list of significant variances above a defined threshold (e.g. >5% and >€50k). Step 3 – Narrative drafting: Feed this structured variance list back into Claude with a second prompt focused purely on writing, not analysis.

Using ONLY the validated variance list below, write management commentary as described earlier. Do not invent new variances or drivers.

This chaining approach reduces hallucinations and makes it easier for analysts to review each step.

Implement Human-in-the-Loop Review with Checklists

Claude should draft, not decide. Build a simple review checklist for analysts so the human-in-the-loop step is systematic, not ad hoc. The checklist might include: verify numbers vs source report, confirm that all material variances are covered, check that one-offs are clearly labelled, and align tone with corporate guidelines.

You can even ask Claude to generate a suggested checklist from your reporting policies:

You are a reporting quality controller. Based on the following internal reporting guidelines, create a 10-point checklist to review monthly variance commentary.

[PASTE YOUR GUIDELINES]

Embed this checklist into your reporting workflow tool or close process so commentary is always signed off against the same criteria.

Fine-Tune Language and Tone with Style Snippets

Many finance teams want commentary that “sounds like us”. Collect a few examples of high-quality narratives from previous reports and turn them into style snippets. Feed these to Claude as examples so it can mimic your preferred tone, structure and phrasing.

You are writing in the style of our existing management reports.
Here are 3 examples of good commentary. Learn the tone, structure and phrasing:

[EXAMPLE 1]
[EXAMPLE 2]
[EXAMPLE 3]

Now, using the variance list below, write new commentary in the same style.

Update these examples periodically to reflect new leadership preferences or changes in reporting focus (e.g. stronger emphasis on cash or ESG).

Log Prompts and Outputs for Auditability and Continuous Improvement

For finance, auditability and traceability are essential. Implement a simple logging mechanism that stores prompts, inputs (tables), and Claude’s outputs together with the final, human-approved version. This can be as lightweight as a dedicated SharePoint/Drive structure or as integrated as a custom internal tool.

Review these logs quarterly to identify common edits analysts make to AI drafts. Feed those patterns back into your prompt templates or data preparation rules. Over time, this continuous improvement loop will increase the share of commentary that is “right first time” and reduce the review burden.

Executed in this way, using Claude for automated narrative commentary in finance can realistically cut drafting time by 50–70%, shorten reporting cycles by 1–3 days, and free up analyst capacity for deeper analysis – while keeping control, compliance and auditability firmly in place.