Up · Categorised export
Categorise Up Statements by Spending Type
Up Bank statement PDFs go in, a CSV with a Category column for every transaction comes out. Built around how Up formats its statement exports specifically.
Supports PDF files up to 10MB
What comes out
| Description (from statement) | Category |
|---|---|
| STAN ENTERTAINMENT PTY LTD | Subscriptions & Software |
| ROUND UP TRANSFER | Transfers |
| WOOLWORTHS METRO | Groceries |
| GYTHIO GREEK TAVERNA | Dining & Takeaway |
Real examples from Up statements used during testing.
How Up's statement text differs from the big four
Up is an app-only bank, and its statement descriptions tend to be a little cleaner and more consistent than the big four's, which generally helps coverage. Round-ups and Up's automated "Saver" transfers between a user's own linked accounts are common on these statements and are handled as internal transfers, not spending, so they don't get bucketed into a category as if they were a purchase.
The same generic keyword approach used everywhere else applies here too. Streaming and subscription services (Netflix, Spotify, Disney+, Stan, Prime Video and similar) are treated as Subscriptions & Software rather than Entertainment, since they're recurring bills rather than one-off spend, a distinction that came directly out of checking real Up statements against manually corrected categories during testing.
Categories used
Every category comes from a fixed list of keyword and pattern rules, not a model guessing at intent. Anything that doesn't match a rule is left as Uncategorised rather than assigned incorrectly, so you can see exactly what still needs a manual look.
Built without AI
Categorisation runs as static pattern matching in the same request that already parses your PDF. Your Up statement is never sent to an AI model to work out what your transactions mean, and nothing about your spending is stored afterwards to train one. It's a fixed set of rules, applied the same way every time.
What Our Customers Say
“Banks like Westpac only let you download CSV files for the past 18–24 months. If you need older data, you're stuck downloading PDFs and manually extracting transactions from pages of formatting and bank jargon. That job is a real slog. Your product handled it instantly and gave me clean data.”
Mark
Manufacturing, former HR/Finance Systems Consultant
“As someone preparing tax returns, going through PDF bank statements manually is a real hassle. This tool dumps everything into Excel format instantly. Huge time-saver.”
David
Director, Tax & Accounting Firm
“Great tool for dealing with PDF statements from clients. The data comes out clean and ready to import, which saves a lot of repetitive work. I had one client with statements going back several years, and this handled them all without a hitch. Highly recommend for accountants and bookkeepers.”
Tamara
Bookkeeper, Self-Employed
“Converted several years of bank statements in minutes and saved me a lot of effort. I initially wasn’t sure if it would handle my older statement formats, but once I tried it, it worked well. Would recommend if you’re dealing with PDFs.”
James
Owner, Construction Company
Questions
Does this use AI to categorise my Up statement?
No. Categories are assigned with static keyword and pattern matching, not a language model. Nothing in your statement is sent to an AI provider, and nothing is stored to improve a model over time.
How accurate is the categorisation?
On real Up statements tested during development, coverage sits around ~91% of transactions, depending on account type and how many one-off, unpredictable merchant names appear. Anything not matched is labelled Uncategorised rather than guessed at.
Can I edit the categories afterwards?
Yes. The CSV is a normal spreadsheet column, open it in Excel or Google Sheets and adjust anything that isn't quite right before you file it or hand it to your accountant.