Fun aside, finance and code can both depend critically on small details. Does finance have the same checks (linting, compiling, tests) that can catch problems in AI-generated code? I know Snowflake takes great pains to show whether queries generating reports are "validated" by humans or made up by AI, I think lots of people have these concerns.
I disagree. Claude may fail at running a vending machine business but I have used it to read 10k reports and found it to be really good. There is a wealth of information in public filings that is legally required to be accurate but is often obfuscated in footnotes. I had an accounting professor that used to say the secret was reading (and understanding) the footnotes.
That’s a huge pain in the neck if you want to compare companies, worse if they are in different regulatory regimes. That’s the kind of thing I have found LLMs to be really good for.
Did you go and look at the correctness of the information?
Because I have seen Claude, as recently as a week ago, completely inventing and citing whole non existent paragraphs from the documentation of some software I know well. I only because of that, I was able to notice...
That part about Claude suddenly going all in on being a human wearing a blazer and red tie and then getting paranoid about the employees was actually rather terrifying. I got strong "allegedly self-driving car suddenly steering directly into a barrier" vibes at that point.
Financial modeling does have formatting norms, eg: different coloring for links, calculations, assumptions and inputs.
However one of the major ways people know their model is correct is by comparing the final metrics against publicly available ones, and if they are out of sync, going through the file to figure out why they didnt calculate correctly.
Personally, this is going to be the same boon/disaster as excel has been.
These tools are not getting used for investment advice in the sense of you might go seek out an advisor. It's used for first pass drafts of potential investments. Think deep research where the target is a company and the output is an investment thesis. There are a lot of rubbish companies out there looking for funding so any sort of automation to filter the volume of info down helps
>Does finance have the same checks
Nope. Closest is double entry system and that only prevents the most egregious stuff. It's the equivalent of you must close brackets in code...it's a constraint but the contents can still be hot garbage. For investment ideas that are literally zero guardrails, in fact quite the opposite as this demonstrates:
As my father always told me. Anyone selling you a system to win at the casino/racetrack/stock exchange is a scammer. If the system actually worked then the system would not be for sale.
That's not quite right. For super high Sharpe ratio strategies with low capacity, sure. But for a single digit SR with high capacity your expected profit will be higher by taking a fee on a larger capital base. If you also add in asymmetric fee structures then you see why hedge funds make sense.
This isn't a financial model, they aren't selling the system itself, it's all tooling for data access and financial modeling. It's like they're setting up an OTB, not like they're selling you a system to pick winning horses at the track.
Anthropic just dropped “Claude for Financial Services”
-New models scoring higher on finance specific tasks
-MCP connectors for popular datasets/datastores including FactSet, PitchBook, S&P Global, Snowflake, Databricks, Box, Daloopa, etc
This looks a lot like what Claude Code did for coding: better models, good integrations, etc. But finance isn’t pure text, the day‑to‑day medium is still Excel and PowerPoint.Curious to see how this plays out in the long to medium term.
Devs already live in textual IDEs and CLIs, so an inline LLM feels native. Analysts live in nested spreadsheets, model diagrams, and slide decks. Is a side‑car chat window enough? Will folks really migrate fully into Claude?
Accuracy a big issue everywhere, but finance has always seemed particularly sensitive. While their new model benchmarks well, it still seems to fall short of what an IBank/PE MD might expect?
Curious to hear from anyone thats been in the pilot group or got access to the 1 month demo today. Early pilots at Bridgewater, NBIM, AIG, CBA claim good productivity gains for analysts and underwriters.
LLMs speak programmer well - they don't speak finance that well. To get much useable retraining or super agressive context / prompting (with teaching of finance principles) is needed otherwise the output is very inconsistent.
I find it helpful. Just drop a soup of numbers and ask "Is this business viable" and go from there. I have not used LLM specific for financial services, but ballpark figures and ideas were very useful for planning. Definitely a time saver and helps to iterate quicker.
Two reasons come to mind. 1. AI hype is the hottest it will ever be, better to sell into as many industries as you can now while everyone is excited about it. 2. There are a lot of unknowns as to what these tools will be best at, or which workflows it will improve or supplant. Better to get more people in more industries using the tool now to uncover these use cases.
If all the hedge funds think their workers will have an edge if they are llm powered cybernetics, it will be an amazingly profitable arms race for the AI firms.
A lot of cross pollination between employees. Smart people who like maths and getting paid a lot of money used to go to HFT firms. Now they go to AI labs.
My brother legit invested in a company some 60$ in a company that chatgpt recommended, then he saw that it makes sense.
The day he bought, everything went downhill in that particular company lol. But to be fair, he said that he just had this as chump change and basically wanted to just invest but didn't know what to (I have repeatedly told my brother that invest funds are cool and he has started to agree {I think})
Also don't forget all the people atleast in the crypto alt space showing screenshots saying that grok/chatgpt (since they only know these two most lol) are saying that their X crypto is underrated or it can increase its marketcap to Y% of total market or it has potential to grow Z times and it is the Nth most favourite crypto or whatever.
Trust me, its already happening man but I think its happening in chump change.
The day it starts to happen in like Thousand's of dollars worth of investment is the day when things would be really really wrong
The scope of financial services is pretty broad right. And it's not always about the raw data. So much of it seems to be 'how do we tell the story we want to tell with the numbers we have'. I say this as someone who hangs out with people that work with the big 4 but honestly I have little clue about the day to day. They seem to do analysis, the client will say that doesn't vibe with what they want to tell shareholders, and they will go back and forth to come up with something in the middle.
I thought at first it meant stuff like bookkeeping and taxes and got excited…the most boringly mind numbing work that’s still not quite that easy to automate. I’m guessing that too will come soon enough.
We got that quality of investment advice before, it's called r/wallstreetbets.
Seriously, people on WSB have done some pretty crazy shit. Someone created an "inverse Cramer" tracker, another a "follow Cramer" tracker. And of course there's WSB trackers.
Could this be used for daytrading or something? If you search Gihub for financial ai projects [1] there are a number of interesting ones for finance & ai integration, some claiming to be stock pickers, and many are abandoned. As a financial illiterate person, I don't really know what I'm looking at.
I'd be curious to know if anyone had used any of these successfully.
On a side note, Anthropic published a Claude Financial Data Analyst on Github 9 months ago that runs through next.js [2]
I do think there are some existing mainstream facing consumer AI applications out there. Macrohive touts AI tools, although that's wider than daytrading.
Well, that's what I spend a good amount of time doing, and no, these things aren't going to spontaneously generate alpha and give "stock picks." Well, some of the deeper concepts can probably help do so, but then you're competing against hideously massive budgets in the same arena.
That said I do think that these tools could be a huge help to "daytrading". They could help with the screening and idea generation process. The concept of "factors" or underlying characteristics which drive correlation within certain baskets of instruments, is already well established in the finance industry. And indeed that concept can be widened out beyond the purely academic lens, so you may have a basket of interest rate sensitive names, or names that are one thematic hop away from a meme sector that is taking off. LLM style tools would be great there. Ex: I remember during COVID that for a week mask companies were taking off. One of these names also had a huge run up during the SARS epidemic. Pretty basic LLM style tools would be great at pointing stuff like that out, generating lists of equities which had unusual activity during pandemics within the last 20 years, etc. Much better than hard coding in filters to an old school screener.
Oh, I think machine learning is also being used in Nowcasting. That's where you take the current economic situation, compare it to previous regimes, and then sort of map out of probability distribution for likely forward paths. Good AI workload. I actually think it would be pretty cool to see something like that intraday (if large tech stocks are liquidating which of these smaller momentum tech names on my watch list have been resilient recently?). The thing is there's sort of the retail trading space, where most of the tools are fluff, and then the hardcore space where software engineers are working in OCAML and databases and have absolutely no need for more "presentable" tools. In daytrading, there is a big gap inbetween thet, and it's surprisingly empty.
In Global Macro/portfolio managent adjacent areas (ex: NowcastingIQ.com, was browsing that earlier today thus my thoughts on the matter) you can find humans who don't know how to code who want to use these tools and can afford $25,000 a year, but again in Daytrading - the actual intraday trading stuff that makes real money - there's less of an illusion that it isn't a robotic warzone.
Readit News
https://www.anthropic.com/research/project-vend-1
Fun aside, finance and code can both depend critically on small details. Does finance have the same checks (linting, compiling, tests) that can catch problems in AI-generated code? I know Snowflake takes great pains to show whether queries generating reports are "validated" by humans or made up by AI, I think lots of people have these concerns.
That’s a huge pain in the neck if you want to compare companies, worse if they are in different regulatory regimes. That’s the kind of thing I have found LLMs to be really good for.
It then _didn’t_ include a similar transaction (losing $7bn by exiting Brazil).
This was stuck in footnotes that many people who follow the company didn’t pick up.
https://archive.ph/fNX3b
Because I have seen Claude, as recently as a week ago, completely inventing and citing whole non existent paragraphs from the documentation of some software I know well. I only because of that, I was able to notice...
He must have passed this secret knowledge on, as they all say it now...
was exploring this idea recently maybe I should ship it
Deleted Comment
Claude 4 orders Melaniacoin ETF.
However one of the major ways people know their model is correct is by comparing the final metrics against publicly available ones, and if they are out of sync, going through the file to figure out why they didnt calculate correctly.
Personally, this is going to be the same boon/disaster as excel has been.
>Does finance have the same checks
Nope. Closest is double entry system and that only prevents the most egregious stuff. It's the equivalent of you must close brackets in code...it's a constraint but the contents can still be hot garbage. For investment ideas that are literally zero guardrails, in fact quite the opposite as this demonstrates:
https://www.reddit.com/r/ChatGPT/comments/1k920cg/new_chatgp...
Dead Comment
-New models scoring higher on finance specific tasks
-MCP connectors for popular datasets/datastores including FactSet, PitchBook, S&P Global, Snowflake, Databricks, Box, Daloopa, etc
This looks a lot like what Claude Code did for coding: better models, good integrations, etc. But finance isn’t pure text, the day‑to‑day medium is still Excel and PowerPoint.Curious to see how this plays out in the long to medium term.
Devs already live in textual IDEs and CLIs, so an inline LLM feels native. Analysts live in nested spreadsheets, model diagrams, and slide decks. Is a side‑car chat window enough? Will folks really migrate fully into Claude?
Accuracy a big issue everywhere, but finance has always seemed particularly sensitive. While their new model benchmarks well, it still seems to fall short of what an IBank/PE MD might expect?
Curious to hear from anyone thats been in the pilot group or got access to the 1 month demo today. Early pilots at Bridgewater, NBIM, AIG, CBA claim good productivity gains for analysts and underwriters.
Let's put a terminal pane in Excel!
https://openai.com/solutions/financial-services/
Finance and engineering both have a degree of verifiably. Building evals around finance is easier than, e.g., marketing work.
Much of the work is repetitive or formulaic or error prone. Plus it’s all digital.
https://www.bls.gov/oes/2023/may/oes132051.htm
Salaries are higher in Finance than other industries for the same job, as it is well known.
But also, budgets for everything else is also higher.
These companies will sign 3 year deals for support, have you onsite implementing and training + app and API subscriptions.
how can you ask this question, it literally called "financial". its screams money all over the place
Deleted Comment
The day he bought, everything went downhill in that particular company lol. But to be fair, he said that he just had this as chump change and basically wanted to just invest but didn't know what to (I have repeatedly told my brother that invest funds are cool and he has started to agree {I think})
Also don't forget all the people atleast in the crypto alt space showing screenshots saying that grok/chatgpt (since they only know these two most lol) are saying that their X crypto is underrated or it can increase its marketcap to Y% of total market or it has potential to grow Z times and it is the Nth most favourite crypto or whatever. Trust me, its already happening man but I think its happening in chump change.
The day it starts to happen in like Thousand's of dollars worth of investment is the day when things would be really really wrong
https://news.ycombinator.com/newsguidelines.html
(Submitted title was "AI ate code, now it wants cashflows. Is this finance's Copilot moment?" - we've changed it now)
Seriously, people on WSB have done some pretty crazy shit. Someone created an "inverse Cramer" tracker, another a "follow Cramer" tracker. And of course there's WSB trackers.
I'd be curious to know if anyone had used any of these successfully.
On a side note, Anthropic published a Claude Financial Data Analyst on Github 9 months ago that runs through next.js [2]
[1] https://github.com/search?q=financial%20ai&type=repositories [2] https://github.com/anthropics/anthropic-quickstarts/tree/mai...
Well, that's what I spend a good amount of time doing, and no, these things aren't going to spontaneously generate alpha and give "stock picks." Well, some of the deeper concepts can probably help do so, but then you're competing against hideously massive budgets in the same arena.
That said I do think that these tools could be a huge help to "daytrading". They could help with the screening and idea generation process. The concept of "factors" or underlying characteristics which drive correlation within certain baskets of instruments, is already well established in the finance industry. And indeed that concept can be widened out beyond the purely academic lens, so you may have a basket of interest rate sensitive names, or names that are one thematic hop away from a meme sector that is taking off. LLM style tools would be great there. Ex: I remember during COVID that for a week mask companies were taking off. One of these names also had a huge run up during the SARS epidemic. Pretty basic LLM style tools would be great at pointing stuff like that out, generating lists of equities which had unusual activity during pandemics within the last 20 years, etc. Much better than hard coding in filters to an old school screener.
Oh, I think machine learning is also being used in Nowcasting. That's where you take the current economic situation, compare it to previous regimes, and then sort of map out of probability distribution for likely forward paths. Good AI workload. I actually think it would be pretty cool to see something like that intraday (if large tech stocks are liquidating which of these smaller momentum tech names on my watch list have been resilient recently?). The thing is there's sort of the retail trading space, where most of the tools are fluff, and then the hardcore space where software engineers are working in OCAML and databases and have absolutely no need for more "presentable" tools. In daytrading, there is a big gap inbetween thet, and it's surprisingly empty.
In Global Macro/portfolio managent adjacent areas (ex: NowcastingIQ.com, was browsing that earlier today thus my thoughts on the matter) you can find humans who don't know how to code who want to use these tools and can afford $25,000 a year, but again in Daytrading - the actual intraday trading stuff that makes real money - there's less of an illusion that it isn't a robotic warzone.