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.
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.
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:
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.
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.
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.
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.
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.
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[ 2.9 ms ] story [ 50.0 ms ] thread-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.
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)
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.
>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...
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.
https://openai.com/solutions/financial-services/
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.
0 - https://en.wikipedia.org/wiki/GameStop_short_squeeze
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.