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I think a lot of people are misunderstanding the typical workload of people in Financial Services. They aren't using Claude to transfer money, they're just building a LOT of slideshows and fancy excel docs on made-up numbers to try to sell mergers and new financing options/types of loans. Most programmers would just consider this "sales".

The only reason they are doing it is because there are regulation for people but not for machines.

I would recommend you to not use these, if you are not willing to absorb the risk.

Luckily there is still a significant market for the services.

> properly handling

Why, they can sell user data to other brokers. Experts indeed! But not in insurance or finance, of course.

Claude's actually pretty great at this! I actually used to use Claude A LOT to answer interesting questions (which I'll be writing up on!) More generally, Claude is palpably different from most other agents. I'd recommend these models – especially Opus – without qualifications.

But there's a process risk here based on their current practises. I'm hoping those practises change so that I can recommend Claude to everyone I know, but as of now, there's existential risk exposure here that's greater than Google's.

Anthropic's automated systems can and will ban you for pretty arbitrary things; and you won't get human support or Claude – even if you are an enterprise paying out of your nose. And there's 0 redressal unless you go viral on social media. Or know someone who knows someone. See: https://x.com/Whizz_ai/status/2051180043355967802 https://x.com/theo/status/2045618854932734260

And I say that as someone who likes how Anthropic has been training Claude and Opus. I just don't think they're prepared to be the trillion dollar company they've become. They are – in a very real way – suffering from success. Which is extremely inconvenient to be on the receiving end of when you're on a deadline.

the details are key here. there is plenty of automatable financial work, sure, but also when it comes to reporting finances/costs (formally or informally) and having a real human being be accountable for them, you REALLY need to trust that nothing is hallucinated.

Any idea how they ensure this doesnt happen? As in, how can a user verify that the model did not touch any of the numbers and that it only built pipelines for them.

what I've been telling my CFO who wants to get AI involved in things is that for a lot of accounting and finance work "Trust but verify" doesnt work because verify is often the same process as doing the work.

I'll be honest, I thought the first few items on your list of time consuming work was sarcasm.

Reads different to me. Some examples to go run with and build your own. Covers cases from the investment side and then the obvious ones in an accounting perspective. It would be highly surprising that any of these would be use in production without modification. I am sure it will happen but the intent to me is to take this and run with your own process.

I find all of these .md files released by the labs to be ai generated slop. The only exception being maybe the /simplify command

Slightly related, I used Opus 4.6 to help me make marketing copy and ideas for my app. It understood the vibe I was going for on my baby-naming app (elation at discovery, curiosity, shared experiences), while 4.7 instantly wanted to pit the couples against each other (really highlighting the he said/she said) and the marketing copy went from "find a name easier" to "Our new feature is great. You're welcome." I can't get it to drop the snarky sass no matter how much I change CLAUDE.md, brand voice, etc.

All I did was upgrade claude code and use the new model. It most definitely exhibits misaligned behavior (compared to 4.6)

Nobody is using LLMs to make lending decisions. They are using LLMs to read, extract and audit the supporting documents that go into normal well-tested, compliant and rules-based underwriting systems. And firms A/B test against humans doing the same work. The outcomes your are looking for are metrics like delivering faster results back to customers, with fewer mistakes and less fraud, more compliant, than a comparable human-only process.

Building a startup on an LLM is like building a house on a foundation of quicksand. As the LLM gets better it naturally erodes your moat. It's a completely different dynamic compared to the internet. It's why I'm watching this from the sidelines.

> Will the big labs leave anything for external competition?

No, why would they if they have the choice?

> what happened to platformication of everything?

Business happened. The web works differently from how it used to. The users are different. LLM inference and AI tools is a different core product from search and ads. That, and we have the benefit of hindsight now. Maybe a Google newsroom would've actually been a good idea in 2006 in hindsight, who knows.

Also realistically you could say the same thing about Google Maps and Street View. That probably also killed some startups. Google isn't running a charity for startups.

I work in a space where one could imagine a Claude replacing our product.

I think someone stated it clearly - they can't take on these kinds of businesses until they build out the risk side and the personnel, all of which is a human problem not a tech one. A lot of processes still require physical steps and backstops because it's not possible to source all the data needed to act on it in the first place. Then you have audits and reconciliations, a bunch of strict workflow rules and atomicity to reach levels of software that bigger financial institutions would accept.

My gut reaction to stuff like this is a mix of "oh shit, they could take over my company" and "they're the next script kiddy that thinks software is anywhere near a majority of the work in some software spaces".

> Will the big labs leave anything for external competition?

Is this a serious question?

Without the big labs with deep pockets investing to change the consumer mindset do you think a small company with no funding has any chance of even existing?

I remember when paying $1.99 for a mobile game on iOS was considered too expensive and now it seem most consumers are primed to spend more on in-app purchases every week. That mind-shift did not happen overnight.

It was not that long ago $200 for ChatGPT subscription was considered extravagant but now even wrappers can charge this price without hesitation - some of them do.

What Anthropic is doing is priming the market of which they will be potentially one of the main beneficiaries as long as they can continue existing. But I don't think anyone will go to Anthropic directly to source their financial services agent. They will go to financial service companies that use Anthropic to build the capabilities.

> in the early internet you wouldn't see google creating their own news site

Google News was definitely a thing (and actually still exists).

I'm not sure if this was tongue-in-cheek or not, but Yahoo created its own news site in 1996: https://en.wikipedia.org/wiki/Yahoo_News and FB had Zynga's Farmville as well.

But Google did move into a lot of spaces: maps, mail, docs, etc.

It's not wise to build a startup that is just a feature of the product that you're building on.

What's even sadder is it can work for way too long.

This is premature caution/fear.

Why control part of the world when you can control it all?

Less cynically, you might say that "use AI to do <obvious thing>" is not really a viable startup pitch anymore. That's not necessarily bad.

History suggests otherwise. railroads, telecoms, search all consolidated. The natural equilibrium for transformative infrastructure is winner take all. AGI/ASI won’t be different but will be nearly every vertical and governments will legislate too little too late.

I don't think Claude Design killed menu competitors and I don't think this will too

I am not sure if people are using claude design, security review stuff and other tools they have built so far.

Building is the easy part. There are lot of service level stuff that I am sure anthropic will not be able to provide, therefore they are trying to partner with other orgs in that realm.

I am very skeptical about their stuff now.

If you are builder, I believe you should avoid anthropic, it can be default to monopolistic behavior, I am not saying they are doing it, but they could, where in they see what you are building, if you have traction, position a product in that realm. Just saying.

> Will the big labs leave anything for external competition?

Unfortunately no.

The TAM for Anthropic and OpenAI is anything that runs software or a screen.

Any software or technology business that has high margins that Anthropic and OpenAI are not doing will be a target.

After both their IPO's mandates Wall Street them to push for more growth by competing in other technology business areas or they will get punished in the markets.

It is ROI or bust.

You’re advocating for less competition? AI startup valuations are out of control. People are raising $20m seed rounds.

If you can’t prove PMF and differentiation with $10m, I’m sorry but you’re not a serious enterprise.

And if what you’re building is “pitch deck AI”, I mean, come on.

> tfw you've been huffing your own copium so much that you forgot you're selling shovels

lol these agents are missing the point re. What people actually do in these jobs.

This is an attempt to inflate token generation to fool people into increasing anthropic’s valuation.

Would love to see this

On the spend management side of things, I've found pretty remarkable success in letting LLMs check "does this receipt match this reimbursement request and based on all the information about the user, the request, and our policy, is it appropriately allocated to appropriate GL, Location, Department, and Project codes?" If the verification step fails, it kicks it back and the user can either override it (which gets it flagged for AP review), or fix it. It does substantially better than the naive Bayes classifier I was using before.

Yes. On the accounting side agents can handle a lot of the low value work like recons and other ledger activity pretty well. On the investment side I think like you pointed out it’s going to be a lot of research, industry, company, macro etc. Value in letting run on top of the data you have and put together ideas at a quicker pace than a human can. There is still a human in the loop but it can do a nice job of lining up thought you might have otherwise missed.

Yes, in very specific cases where I fully understand the methodology(ies) that is (are) applicable, and am able to verify correct implementation. Also, as an enhanced ‘Google search’ to supplement what I have found. I am the skeptical type… yet, so far have been impressed. But, I wouldn’t trust using AI to blindly give me solutions to a problem I couldn’t solve myself, albeit much more slowly.

Pretty good as a dev with finance stakeholders. We have skills in place acting over our automated month closing and it was able to provide manual checks and flag issues, for example.

Nowhere near self sufficient tools though, just great to answer questions over the data that would usually take a few hours of custom scripting/excel. I wouldn't trust our stakeholders using AI directly either, being frank.

Seen it used in some of the fraud models (I work in insurance). So that's both from the perspective of people trying to claim fraudulently and from suppliers over charging. I can't say how much of a lift we actually get vs existing ML models

Nope If anything firms are pulling back (I know someone closely who works at blackrock).

> For those in the finance space, are you actually seeing any real AI tools being used? Like for actual operational tasks?

> I've really only seen it used for research / exploration thus far

Summaries and translation for sure.

Speaking with devs in the field I know that AI tools are used to summarize and extract data from... PDFs. Now, thankfully, LLMs got better at answering "How many 'r' in 'strawberry" and it looks like they're good enough for summarizing PDFs and extracting key numbers but I'd still be cautious.

And I've got a friend who's a translator specifically for financial documents: she's a contractor and getting about 1/10th of the work (and 1/10th of the pay) she used to have for now she's only tasked to verify that the translations are correct. Of course she already had lots of tools, way before he LLM era, automating some of her work but she was still billing he use of those tools. Now LLMs are doing nearly all the work and not "for her": it's happening upstream and she only gets the output of the LLMs and has to verify them. And there aren't that many errors.

We’re integrating AI tooling into the Bloomberg Terminal for everyone to use.

https://www.bloomberg.com/professional/insights/press-announ...

haha, insider! :-D

Just yesterday I told a colleague that he should by some of their vests for his company :-D

Just a natural rebalancing of the Rise of the Laptop Class. I think we'll get more productive as the white collar jobs become more efficient, and less days with 8hrs of meetings and responding to emails from people too lazy to look information up themselves.

What will happen is what has happened for the past few years: mostly nothing. People employed, things keep trucking forward.

LLMs do not change the equation all that much: human's ability to imagine is the most scarce resource on the planet and LLMs will not help all that much with it.

Well at that point you can use AI as legal help, right?

There was a paper lately, claiming that bank & insurances are going to layoff around 200k in the next years globally. (which would be according to them a reduction of 3-4% of finance people)

Because the l on your keyboard is broken?

Follow the money, until you can't (compute credits)

At that point what even is the point of doing anything at all? Like, it’s less than useless.

What does a better insurance process look like? Outside of health insurance, which is complicated for a variety of reasons, most insurance is pretty easy to procure. I got an umbrella policy recently and it took about 30 minutes of talking with an agent and answering pretty reasonable questions.