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.
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.
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]
In the BERT era of language models, it was normalized that to get the best performance for a task, you probably needed targeted post-training
As models got bigger and instruction following got better, everyone jumped on the general capabilities of the model + prompting
We're approaching wall that needs to be overcome with a completely new and unheard of breakthrough, otherwise we're going to have to go back to specialized post-training (which lends itself to vertical solutions)
I think people are seeing that now with stuff like Devstral being posttrained specifically for OpenHands and massively over-performing for its size at agentic coding
Anthropic doesn’t have the universal name recognition of ChatGPT, so they’re going for an underdog strategy of building a portfolio of strong niches. Seems smart, sounds higher-margin.
The 30/50/100gb of random numbers that is a trained LLM is basically worthless - if it has any value at all on day 1, that value depreciates at multiple percentage points per day.
Anthropic more than OpenAi are going for the integrations, verticals and MCP - I think that is the right play. "OpenAi Inside" can replace the "Intel Inside" sticker but their marketcap needs to go 1/100x
Investment firms aren't known to advertise or resell their secret sauce. AI has been used in trading in some form or the other for close to 40 years now.
If you work in a Project, Claude populates an "artifact" in the righthand pane.
The hamburger menu lets you select different artifacts, if there are several, and the "Copy" button has a dropdown that lets you either add it to your Project or download the file locally.
The more and more AI projects I see both at work and online, the more convinced I'm that I should treat AI as an application interface, that's all.
It's a slightly different modality for the application. Nothing AI does wasn't possible before. You could always "create a price performance chart showing a stock's movement with key events annotated since May". You could also always buy dozens of software that will not just give you all the charts you could possible think of, but any one that you could even dream of. Check tradingview.com or koyfin.com for a taste of what a "free" offering can give you. Then imagine what the 100k software gives you.
The difference is the interface. You'll 100% need someone onboarding on their 100k custom trading platform. It might take you months to master it if you never saw one of these things before. Once you have learned it though, your productivity and velocity is expected to significantly increase.
Now with the AI interface, you don't need someone onboarding you or months to learn. You can ask the AI to "build a benchmarking analysis against Velocity's athletic footwear comps" instead of learning how to learning how to use the software to create such a thing. Maybe you never saw financial analysis software before, but you spent the last 20 years analysing financials by hand (in 2025 for some reason) and now you wanna onboard to a financial software. You don't need to "learn" anything. Just describe your thoughts to the AI and it figures the interface for you.
How transformative was that for you? I don't know. Maybe your financial analysis tool is as big of a piece of shit as Reactjs is and it's mind-numbingly tedious to generate such report. "It's just a 75 clicks that you have to do" and the AI interface saves you from doing that like it saves me from using React's shitty interface (text editor) to write garbage react components that are all just a copy of each other.
I've been thinking that for some time. Its a "looser way" to describe what you want as a different modality; a dynamic interface if you will. Even with code editors I've found its good to generate a lot of volume, but the detail still needs iteration or going back to direct instruction (i.e. code/clicking/etc). That applies to any artifact where iteration and validation is required to get it right. Instead of deterministic clicking and having to instruct every detail you can describe in "vague english" and the 80%/20% rule applies. Definitely an acceleration/leverage and a smaller learning curve.
Nothing any technology does wasn't NOT possible before that tech went mainstream. The point being tech saves time/cost and boosts productivity. For e.g. if you would have been able to find a webpage in an hour before, search made it easier to find that webpage. Similarly, AI synthesizes webpages and information for you.
That is the point of technology. If you could reach from point A to point B, using a bicycle, car, train or an aeroplane, each has its own use case at its own value and price point. Each such tech saves time/cost. To say that is is only a different modality, fails to capture the value add.
In the end in few years, it will be whosoever has better AI wins in all fields. Monopoly sort of thing. I finance world maybe they win most of the trades.
jasonthorsness|7 months ago
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.
georgeecollins|7 months ago
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.
wrs|7 months ago
nibble1|7 months ago
Claude 4 orders Melaniacoin ETF.
intended|7 months ago
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.
Havoc|7 months ago
>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...
injidup|7 months ago
snthpy|7 months ago
Benjammer|7 months ago
whazor|7 months ago
MaxPock|7 months ago
mildlyhostileux|7 months ago
-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.
blitzar|7 months ago
varispeed|7 months ago
MuffinFlavored|7 months ago
Let's put a terminal pane in Excel!
hbcondo714|7 months ago
https://openai.com/solutions/financial-services/
MuffinFlavored|7 months ago
gyosko|7 months ago
Imustaskforhelp|7 months ago
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
lbreakjai|7 months ago
dang|7 months ago
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)
mildlyhostileux|7 months ago
raptorraver|7 months ago
osn9363739|7 months ago
ido|7 months ago
yodon|7 months ago
mschuster91|7 months ago
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.
pogue|7 months ago
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...
AdieuToLogic|7 months ago
0 - https://en.wikipedia.org/wiki/GameStop_short_squeeze
asdev|7 months ago
BoorishBears|7 months ago
As models got bigger and instruction following got better, everyone jumped on the general capabilities of the model + prompting
We're approaching wall that needs to be overcome with a completely new and unheard of breakthrough, otherwise we're going to have to go back to specialized post-training (which lends itself to vertical solutions)
I think people are seeing that now with stuff like Devstral being posttrained specifically for OpenHands and massively over-performing for its size at agentic coding
dcre|7 months ago
apwell23|7 months ago
there isn't money or moat in this due to commodification.
blitzar|7 months ago
Anthropic more than OpenAi are going for the integrations, verticals and MCP - I think that is the right play. "OpenAi Inside" can replace the "Intel Inside" sticker but their marketcap needs to go 1/100x
v5v3|7 months ago
tom_m|7 months ago
the_arun|7 months ago
khurs|7 months ago
So what is the existing competition? what is JP Morgan doing already in house/Bloomberg offering?
Deepseek was made by a HedgeFund founder, so he is also well placed.
paxys|7 months ago
andrewstuart|7 months ago
It’s copy and paste hell and they’re just not solving it.
“Download all files” from a chat or git pull from a chat or sftp from a chat or something but please fix it.
driggs|7 months ago
The hamburger menu lets you select different artifacts, if there are several, and the "Copy" button has a dropdown that lets you either add it to your Project or download the file locally.
eddythompson80|7 months ago
It's a slightly different modality for the application. Nothing AI does wasn't possible before. You could always "create a price performance chart showing a stock's movement with key events annotated since May". You could also always buy dozens of software that will not just give you all the charts you could possible think of, but any one that you could even dream of. Check tradingview.com or koyfin.com for a taste of what a "free" offering can give you. Then imagine what the 100k software gives you.
The difference is the interface. You'll 100% need someone onboarding on their 100k custom trading platform. It might take you months to master it if you never saw one of these things before. Once you have learned it though, your productivity and velocity is expected to significantly increase.
Now with the AI interface, you don't need someone onboarding you or months to learn. You can ask the AI to "build a benchmarking analysis against Velocity's athletic footwear comps" instead of learning how to learning how to use the software to create such a thing. Maybe you never saw financial analysis software before, but you spent the last 20 years analysing financials by hand (in 2025 for some reason) and now you wanna onboard to a financial software. You don't need to "learn" anything. Just describe your thoughts to the AI and it figures the interface for you.
How transformative was that for you? I don't know. Maybe your financial analysis tool is as big of a piece of shit as Reactjs is and it's mind-numbingly tedious to generate such report. "It's just a 75 clicks that you have to do" and the AI interface saves you from doing that like it saves me from using React's shitty interface (text editor) to write garbage react components that are all just a copy of each other.
throw234234234|7 months ago
srivmo|7 months ago
Nothing any technology does wasn't NOT possible before that tech went mainstream. The point being tech saves time/cost and boosts productivity. For e.g. if you would have been able to find a webpage in an hour before, search made it easier to find that webpage. Similarly, AI synthesizes webpages and information for you.
That is the point of technology. If you could reach from point A to point B, using a bicycle, car, train or an aeroplane, each has its own use case at its own value and price point. Each such tech saves time/cost. To say that is is only a different modality, fails to capture the value add.
noobly|7 months ago
bugglebeetle|7 months ago
xoralkindi|7 months ago
blitzar|7 months ago
kaycebasques|7 months ago
mhh__|7 months ago
Maybe they use it to help search but the search in my terminal is fairly bad
daft_pink|7 months ago
mrbonner|7 months ago
overgard|7 months ago
6Az4Mj4D|7 months ago