ING · Categorised export
Categorise ING Statements by Spending Type
ING statement PDFs go in, a CSV with every transaction already categorised comes out. Covers both Orange Everyday and Savings Maximiser account formats.
Supports PDF files up to 10MB
What comes out
| Description (from statement) | Category |
|---|---|
| GOOGLE*DOMAINS | Subscriptions & Software |
| INTEREST PAID | Income |
| TRANSFER TO SAVINGS MAXIMISER | Transfers |
| COLES EXPRESS FUEL | Transport |
Real examples from ING statements used during testing.
Orange Everyday vs Savings Maximiser
ING's two main personal account types show up completely differently in testing. Savings Maximiser statements categorise close to perfectly, largely because interest and internal transfers dominate the transaction list and both are easy, unambiguous patterns to match. Orange Everyday statements, being an everyday spending account, carry the same long tail of one-off merchant names every transaction account has, so coverage there sits lower even though the same ruleset is applied to both.
ING is one of the banks where Google's domain billing shows up in transaction descriptions in an odd way, "Google*domains" rather than "Google domains" with a space. A rule written assuming a space between words won't match the asterisk. It's written to allow either a space or an asterisk in that specific spot now.
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 ING 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 ING 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 ING statements tested during development, coverage sits around 86–100% 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.