Credit card statement processing — extract every transaction

Corporate cards generate the highest-volume, most repetitive document in finance: a statement a month per card, dozens of merchants, fees and interest tucked between purchases. Dynamite Docs reads the statement into transaction rows, merchant, date, amount, category, and leaves the categorization judgment to you, remembered for next month.

Try this workflow free or view pricing.

Card statements are dense, and the details matter

A single statement can mix purchases, refunds, cash advances, fees, interest, and a payment due, each a different kind of row.
Merchant names are truncated or formatted oddly, which breaks naive category rules.
Every card is a separate statement with its own layout, and multi-currency spend adds conversion noise.
Retyping card transactions is exactly where categorization mistakes happen, and they surface at audit time.

How Dynamite Docs handles credit card statements

01 — Upload the statement

Digital PDFs parse deterministically; scanned or photographed statements are read by the vision model you choose. Multi-card batches work in one queue.

02 — Transactions are inferred

Transaction date, post date, merchant, amount, category, and card identifier are extracted per layout, no per-card templates.

03 — Verify merchants and categories

Low-confidence rows surface for review. When you correct a merchant or assign a category, that pattern is reused on the next statement.

04 — Export or reconcile

CSV, JSON, Excel, Word, or Sheets, or push via the API and webhooks. Fees, interest, and payment rows are kept separate from purchases so your totals tie out.

Document types this workflow handles

Credit card statement PDFs — digital statements, one card per layout
Corporate & expense card statements — purchases, fees, interest, and payments
Scanned or photographed statements — read by the vision model you choose
Multi-card batches — several cards processed in one queue

What gets extracted from a card statement

Inferred per layout. A typical statement run yields:

Transaction date — 2026-08-01
Post date — 2026-08-03
Merchant — AWS AMAZON WEB SERV
Amount — $412.30
Category — Software & subscriptions
Card — Visa ····8012
Fees / interest — $25.00 late fee
Payment due / balance — $4,912.10
Currency — USD

A realistic example

A corporate card statement extracts to rows like these, ready for expense and reconciliation workflows:

Date	Merchant	Category	Amount
2026-08-01	AWS AMAZON WEB SERV	Software & subscriptions	$412.30
2026-08-02	DELTA AIR LINES 0065542	Travel	$386.00
2026-08-03	SHELL OIL 584112	Fuel	$64.18
2026-08-04	FOREIGN TXN 18.20 EUR	Travel	$21.35

What your card data is actually used for

Card statement data feeds three workflows at once: expense tracking, reconciliation, and policy review. The same merchant line that needs a category for the expense report is the line that has to tie out against the card balance, and the fees and interest rows are the ones an auditor will chase. Keeping row types separate, purchases, refunds, fees, and payments, is what makes all three work from one extraction.

The recurring nature is the opportunity. A card statement arrives every month with largely the same merchants, so every categorization you confirm is a pattern for next month. After two or three cycles, the review shrinks to the new merchants and the odd foreign-exchange line, and the statement effectively extracts itself. That shrinking review is where the hours come back. Month two takes half the time of month one.

That repeatability is also why model choice and cost matter here. The same statements month after month should run through the cheapest reliable path, deterministic parsing for digital PDFs, and your own vision key at your provider’s rate for the scans, with unlimited BYOK processing on Pro and Ultra.

Try the card your team spends on most first. Once that layout and its categories are remembered, roll the next card in. Each one is a small, one-time setup instead of a standing transcription job.

Why bring-your-own-AI matters for card statements

Card statements repeat monthly, so a per-page cost that is fine on paper becomes the deciding factor at volume. Bring your own key, pay your provider’s rate, and on Pro and Ultra that processing is unlimited.

Merchant-category judgment is your team’s intelligence. Dynamite Docs remembers each categorization as a pattern, so next month’s statement needs less review. That knowledge exports with you.

Cards carry purchase data no one wants in a training set. Sensitive Documents mode, local Ollama, and self-hosting give you full control over where the pixels go.

Questions, answered

Does it separate purchases from fees and interest?

Yes. Fees, interest, refunds, and payments are extracted as their own row types so purchase totals tie out against the statement balance.

Can it handle multiple cards?

Batch processing handles many statements in one queue. Each statement keeps its own card identifier so rows stay attributable.

Does it categorize transactions automatically?

The model infers a category where one can be read, and your manual assignments are remembered as patterns. You always have final say over the categories you export.

Scanned card statements?

Scans and photos are read by the vision model you choose, the same pipeline as digital PDFs, just routed through vision instead of deterministic parsing.