Finance & FP&A

How to analyze vendor and category spend with AI

Finance & FP&A3 AI tools6 steps6 friction points

Want an agent to handle this on your live data? See the Starch version →

Vendor and category spend analysis means taking your raw transaction history — every bill, subscription, contractor invoice, and one-off purchase — and turning it into a clear picture of where money is going, who your top vendors are, how spending by category is trending month over month, and which line items have grown quietly without anyone noticing. For most operators, this work sits somewhere between accounting and decision-making: too analytical for the bookkeeper, too tedious for the exec team to dig into manually.

AI feels like the right tool here because the underlying task is pattern recognition on structured text. Given a list of transactions, a model can group vendors, label categories, flag outliers, and write a plain-English summary faster than any analyst working through a spreadsheet row by row. The data is finite, the categories are recognizable, and the output — a spend breakdown with commentary — maps naturally to what a language model does well.

ChatGPT, Claude, and Gemini can genuinely help with this workflow. Paste in a CSV of transactions and ask for vendor totals, category rollups, or anomaly flags, and a capable model will produce a solid first draft of the analysis. The output quality is real. The friction is in everything around the analysis itself: getting the data in, keeping it current, and maintaining any structure the model produces from one month to the next.

Skip the manual work → See the Starch version

Finance & FP&A3 AI tools6 steps6 friction points

AI walkthrough

How to do it with AI today

A practical walkthrough using ChatGPT, Claude, and other off-the-shelf LLMs — what they're good at, what you'll have to do by hand.

Tools that work for this

ChatGPTClaudeGemini

Step-by-step

1 Export a transaction CSV from your bank, QuickBooks, or accounting software. Most exports include date, description, amount, and sometimes a category field — clean up the headers so the model can read them without confusion.

2 Open Claude or ChatGPT and paste the CSV directly into the chat window, or upload it as a file if the tool supports attachments. Claude handles large pastes well; ChatGPT with Code Interpreter can run actual aggregations on the file rather than just reading it.

3 Start with a vendor rollup prompt: ask the model to group all transactions by vendor name, sum the totals, and sort descending. This gives you your top-spend list. Expect some cleanup — 'AWS' and 'Amazon Web Services' may appear as two separate vendors.

4 Run a second prompt for category analysis. Ask the model to assign each transaction to a spending category (software, payroll, marketing, cloud infrastructure, etc.) and total by category. You can provide your own category list or let the model propose one, then iterate.

5 Ask the model to flag anomalies: any vendor whose charge this month is more than 20% above their 3-month average, any new vendor that hasn't appeared before, and any recurring charge that's missing compared to last month.

6 Copy the output into a Google Sheet or Notion doc. If you want a consistent format month over month, save your prompt chain somewhere so you can rerun it with next month's export.

Prompts you can copy

Here is a CSV of 3 months of business transactions. Group by vendor name, sum total spend per vendor, and sort highest to lowest. Flag any vendor name that might be a duplicate under a different string.

Using the transactions I've pasted, assign each line to one of these categories: Software/SaaS, Payroll, Cloud Infrastructure, Marketing, Office/Admin, Professional Services, Other. Then give me monthly totals per category for the last 3 months.

Compare this month's vendor totals to last month's. Flag any vendor where spend increased more than 25%, any new vendor that appears this month but not last, and any vendor from last month that's missing this month.

Summarize our top 10 vendors by total spend this quarter, note what each one is for if you can infer it from the name, and flag any that look like they might be duplicates or personal charges mixed into the business account.

Here are two months of transaction exports. Identify which expense categories are trending up, which are trending down, and write 3 sentences summarizing where our spending is growing fastest.

Reality check

Where this gets hard

The walkthrough above works — until your numbers change, the LLM hallucinates, or you have to re-paste everything next month.

No live data connection — every analysis starts with a manual export from your bank or accounting software, which means the numbers are already stale by the time you're reading the output.

Context window limits bite on real transaction volumes. A busy month might have 500–1,000 line items; large CSVs get truncated or force you to chunk the analysis across multiple prompts, which breaks category consistency.

Vendor name normalization is manual and repetitive. The model can't know that 'AMZN Mktplace' and 'Amazon Web Services' and 'AWS' are three separate things until you tell it — and you'll tell it again next month.

Nothing persists. The category taxonomy you built, the anomaly thresholds you tuned, the vendor mapping you cleaned up — none of that carries forward. Next month's analysis starts from scratch.

Comparing across periods requires you to hold both datasets in one prompt or manually reconcile two separate outputs. Period-over-period trending is tedious to maintain without a structured data layer.

The model can flag anomalies but can't send you an alert when one appears. You have to remember to run the analysis, which means most operators do it quarterly instead of continuously.

Tired of the friction?

Starch runs the whole workflow on live data — no copy-paste, no hallucinated numbers, no re-prompting next month.

See the Starch version →

Starch alternative

The same workflow on Starch

Starch is an agentic operating system — it builds and runs persistent apps connected to your live business data. For vendor and spend analysis, that means connecting your accounts once and getting a continuously updated dashboard, not a prompt you re-run every month.

Start with the Transaction Insights app from the Starch App Store. It connects directly to Plaid, syncs every transaction on a schedule, and surfaces vendor totals, category trends, and month-over-month comparisons automatically — no CSV exports, no manual uploads.

Starch syncs your Plaid bank data on a schedule, so the analysis reflects what actually hit your accounts this week, not last month's export. Vendor spend is always current without you doing anything.

Anomaly detection runs continuously. Transaction Insights flags charges that are unusually large compared to that vendor's history and alerts you to any new vendor that's posted a charge in the last 60 days — without you asking.

Describe the custom breakdown you need and an agent builds it. Tell Starch: 'Show me monthly spend by category for my top 20 vendors, with a 6-month trend line and a flag when any category grows more than 15% month over month' — and it builds that as a persistent app, not a one-off answer.

Connect QuickBooks from Starch's integration catalog alongside Plaid to layer in bill-level data — vendor invoices, payment status, and accrued expenses — so your spend view covers both cash out and obligations.

Pair the Runway Analysis app with Transaction Insights to see category spend in the context of your burn rate and cash runway. The same underlying data powers both surfaces, so you're not reconciling two different exports.

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