Safe way to analyse budget and transactions with AI/Chat GPT, "cleanse" or obfuscate data before uploading for anaylsis
I want to see if ChatGPT can help me analyse some of my spending. Questions such as "can you help explain the sudden drop in spending the last 3 months compared to earlier in the year", or "what are ways I can cut back based on my spending and averages for…
I want to see if ChatGPT can help me analyse some of my spending. Questions such as "can you help explain the sudden drop in spending the last 3 months compared to earlier in the year", or "what are ways I can cut back based on my spending and averages for families like mine in my city". Of course I have spent hours manually pouring over charts and graphs and have a reasonable idea but I think there could be some value in a more holistic and nuanced view. I don't want to link my bank accounts to Chat GPT and nor would I upload my bank statements as is. I was thinking of downloading everything as a csv, and then cleansing the data to remove any personal information and possibly obfuscate the data (primarily the merchant names) but keeping the categories. The categories are really the most important part of the information it seems to me. Any one got thoughts on this or done something similar? I was thinking of actually vibe coding an app to do this process (locally) for me.
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Higher yield in taking the time/energy/effort to "manually pouring over charts and graphs and have a reasonable idea" over placing any potentially life altering decision making based on hallucinations. Learning to discern what "AI" can and (more importantly) can not do is an incredibly important life skill.
The categorisation part is already handled by the finapp I am using. I know the app I am using and others either have or will introduce some AI analysis but for now I want to do this myself. You are correct though that categorisation is IMHO the most critical part of any finapp and without that being done really accurately any inferences or analysis made on that data is unreliable and kind of useless. I am just looking at obfuscating my data before sending to AI for analysis to reduce the amount of personal info out there.
I have done plenty of my own analysis based on the data and tools provided by my finapp tool of choice. That said, AI can be excellent at taking a large amount of interconnected data and finding patterns and making inferences that can be hard for a human, or more precisely my poor little brain. I can easily see that my grocery spending has reduced the last few months as I try and rein in spending, but it can be harder to really drill down to the nitty gritty and see how changes in one are affect another. For example, my electricity bill has increased because I now charge an ev and thus my gas station spending has massively reduced, but has it reduced more or less than the changes in the electric bill and by how much. In that calculation one also has to account for the time of year and HVAC use etc. AI isn't a magician but it can provide insights. It goes without saying though that all output from AI has to be very carefully evaluated and cross referenced. I find it useful to help give me some confidence in my own analysis. I do all the calculations myself and then if the AI is also providing similar results I can feel a bit more confidence in my own work (and yet always being wary!).
I love the new Reddit. Down vote away because you don't like LLMs, but I literally answered the questions asked - which included, "Has anyone done something similar?" Yes. Yes, I have. It works. Quite well. The people that use it love it, because they have more time to do things they enjoy rather than scrutinizing J. Random Salesrep's scrawled notes and compare them to the credit card statements. Or worse. I'm still sorry to disagree with the other poster, but because LLMs love patterns, they love this kind of stuff.
FWIW I had chatgpt make a very quick and dirty csv cleanup/obfuscation script to remove identifying text and anonymise merchants/descriptions/bank account names, just leaving the values, dates and categories as they were. I then uploaded the cleaned csv to chat gpt and was able to ask it a bunch of questions and it was able to correctly identify when for example I forgot to pay a bill in month and so paid 2 months bills the next time (the kind of thing that makes graphs kind of crappy to look at and parse). It was able to then more clearly and cleanly identify how much I was actually spending on things like food and groceries, electricity etc. Essentially it was better at seeing through the noise than I was. It was undoubtedly handy and also helped me make a more precise budget than I had been able to before, one that was realistic given my income and also the area I live in and also reflecting my spending habits. All in all, quite successful.
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I have a LLM-based product in production right now that categorizes expense reports for a client. It makes less mistakes than the co-workers that have done the task for years. It also does it in a fraction of the time, even accounting for the required cross-check. As much as I hate to disagree with the inestimable u/AvocadoBeforeToast , this is actually a great use for LLMs. Even a smallish local model can get the majority of the categorizations correct, and leave the ambiguous ones for you to sort. That would allay many of your privacy concerns.