How to Categorize Your Bank Transactions With AI

Most banks and card issuers let you download transactions from their website, usually as a CSV file and often as OFX or QFX files for finance software. A CSV opens in any spreadsheet and typically includes the date, description and amount, and sometimes a running balance or the bank's own category. Before uploading anything, remove what the AI does not need. Keep the date, description and amount. Delete account numbers, names, addresses and balances, and check descriptions for embedded details such as partial card numbers, reference numbers or the names of people you paid. Review your chatbot's data settings too, such as whether chats are used for training and whether it offers a temporary chat mode. Next, choose the categories yourself. A short, fixed list gives consistent results. If you let the model invent categories, you get near-duplicates like Food, Restaurants and Dining. Ask it to add a single Category column, keep every original row, and use a Needs review label when it is unsure. The biggest misconception is that the AI knows what you bought. It sees only the merchant descriptor, which is often cryptic. Prefixes like 'SQ *' or 'TST*' name payment processors such as Square or Toast, not the type of business. A line from Amazon or Walmart could be groceries, gifts or household supplies, and only you can sort those out. Also watch for items that are not spending: transfers between your own accounts, credit card payments (the card purchases are already counted), refunds and income. Check how amounts are signed, since some exports show debits as negative numbers and others use separate debit and credit columns. Finally, verify the result. The categorized file should have the same number of rows as the original, and the category totals should add up to the overall total. A pivot table in Excel or Google Sheets makes this check quick.