I’m not necessarily interested in the traditional full budgeting and planning type stuff, but more like “AI take all these statements and tell me how to save money” purpose built tools. Anyone used anything they’d suggest?
(And to hopefully head off any unhelpful answers like I got on Reddit, I am not trying to have an AI manage my money, nor am I talking about just a wrapper for ChatGPT. AI in the broad sense of the term that can be intelligently used as part of larger programmatic workflows.)
Edit: For anyone actually trying to understand the possible applications, I found this: https://midday.ai/updates/automatic-reconciliation-engine/. The product is overkill for the sort of personal use I was asking about, but this article does a good job of showing the why and how.
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)
I found this which is overkill for personal use but does a good job of laying out this sort of application: https://midday.ai/updates/automatic-reconciliation-engine/
“Instead of just comparing text strings, we use 768-dimensional vector embeddings to capture the semantic meaning of transactions and receipts.
// Generate embeddings for transaction data const transactionText = prepareTransactionText({ name: transaction.name, counterpartyName: transaction.counterpartyName, merchantName: transaction.merchantName, description: transaction.description }); const embedding = await generateEmbeddings([transactionText]);These embeddings allow our system to understand that “AMZN MKTP” and “Amazon Marketplace Purchase” refer to the same thing, even though the text strings are completely different. The system learns patterns like:
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.You aren’t going to find a useful AI system for personal use in Finance, you simply don’t have the scale needed to benefit from it. You don’t make enough transactions per month that looking over it yourself is going to be any slower than reading the AI summary.
The question as with most process optimization and data analysis is, what’s the actual result you’re hoping for? If you want it to be able to summarize WALMART, WAL-MART and WMART so you can see those numbers added together, you already know you spent a lot at Walmart. Whare are you going to actually do with that information?