[jasim]

Plain Text Accounting has become significantly easier to do for me on a regular basis, thanks to LLMs. Specifically: importing bank statements into hledger and avoiding manual entry.

I use a JSON file to map bank entries to my hledger accounts. For new transactions without mappings, I run a Python script that generates a prompt for Claude. It lists my hledger accounts and asks for mappings for the new entries.

Claude returns hledger journal entries based on these mappings, which I can quickly review.

Then another script prints out hledger journal entries for that month's bank transactions, all cleanly mapped. It takes me just a few minutes to tweak and finalize.

I can also specify these mapping instructions in plain-language which would've otherwise been a fragile hodgepodge of regexps and conditionals.

[frakt0x90]

You don't consider an LLM fragile? Also bold to send your banking information to Anthropic.

[jasim]

Good question. LLMs are surprisingly less fragile than hand-coded parsers for unstructured data like the ones in a bank statement.

And to be clear - I'm not sending the entire statement to Claude; instead, only the account name/narration of those transactions for which I already don't have a mapping. Claude then returns a well-formatted JSON that maps "amzn0026765260@apl" to "expenses:amazon", and "Veena Fuels" to "expenses:vehicle" and so on.

I can also pass in general instructions saying that "Restaurants and food-related accounts are categorized under 'expenses:food'", and it does a good job of mapping most of my dining out expenses to the correct account head.

The actual generation of journal entries are done by a simple Python script. The mapping used to be the hardest part, and what used to need custom classification models is just a simple prompt with LLM.