Claude Code · Anthropic
Reconcile a month of transactions and flag the doubtful ones
An engineer in Japan who runs a few small businesses had Claude Code build a re-runnable reconciliation pipeline for his books and measured how it did on one month of 200 transactions.
What they did
The pipeline pulls the month's transactions from his accounting platform over MCP and matches each bank row to a receipt or invoice by amount and date. For every row it proposes a category with a one-line reason, and it flags rows it is unsure about instead of guessing. He then reviewed the flagged rows and spot-checked the rest.
How it went
The poster says 189 of 200 transactions were categorized correctly. Of the 11 errors, 7 were already among the 18 rows it had flagged, and his spot-checks found the other 4.
Worth knowing
Errors came from intent it could not see, missing local tax rules and multi-purpose payments, so a human pass is still needed. The accounting platform is not named, no time saving is reported, and the post ends with a plug for his book.
Try it with Claude Code
Write a script I can rerun each month that pulls transactions for <month> from <your accounting system or export file>, matches each bank line to a document in <your receipts folder> by amount and date, and proposes a category from <your chart of accounts> with a one-line reason. Put every row you are not confident about in a separate review list. Do not post or change anything in the accounting system.
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What this label means: User reports
Someone using it at work posted about it. Any numbers are theirs, so we write "the poster says".
Real cases of AI agents at work, from every major agent, each linked to where it was first reported. Rewritten in our own words, most with a prompt you can try. These are not Wize customers.