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Where I could see an LLM being useful is categorizing entries and maybe proposing sanitization (for example when the payment provider uppercases or abbreviates stuff)
Yep, that’s exactly the sort of thing I’m thinking about here. And it doesn’t even need to be full on chat style LLM, just some decent NLP that can recognize WALMART, WAL-MART, or WMART are all the same thing and label it.
But for some reason this question brings out all the assumption people who want to give financial advice or talk about the AI image the saw last year with 6 fingers.
Honestly, you don't even need NLP for this. Excel supports regex now so you could just do a call like
=REGEXTEST(A1, "(?!)^w.*mart$"). Then just mark by type and graph out to see where your main spending is coming from.You’re missing the point, that would require sitting down and manually doing that for every conceivable payee. Walmart is just an example. The value of any sort of “intelligent” component would be for this to happen automatically and seamlessly for the user. Hell, the AI layer could just be “write regex for al the possible similar payees across these documents”.
That's just called
VLOOKUP(). I think you're over-complicating this process. If you sit down and look at your finances, you'll notice that the number of payees you have isn't some absurd unmanageable amount. As others have mentioned, there's no real use case for involving AI this way. There's no scale, no real benefit to financial tracking, etc. I get this is just to use AI for the sake of using AI, but that's not really a goal when writing financial software.