Latent Space · 3 min · Oct 31, 2026

Financial AI can fit into existing review hierarchies

JKJoseph Kim · Head of Financial Services, OpenAI

Joseph Kim says finance AI can use employee permissions and existing analyst-to-MD review chains.

In this episode

4:10 Bundled market data fixed connector failuresPro pick 3 min
7:00 Most finance AI product work is buildable by startups2 min
22:45 Financial AI evals must predict real job performance3 min
9:16Financial AI can fit into existing review hierarchies3 min
19:01 Moving up the analyst stack makes evaluation harder3 min
15:45 Pairwise human feedback can train models on subjective taste3 min
12:32 Factual errors are more dangerous than weak visual judgment3 min
1:57 Finance training improves the shared model, not just one product2 min

“So if models behave more like people, then you can leverage those systems.”

JKJoseph Kimat 11:46 · Latent Space

Takeaways

The current product assumes a human reviews its work.
Future agents could use coworker-like permissions and checks.
Banking already uses review chains to catch mistakes.

About this segment

Joseph Kim says finance AI can use employee permissions and existing analyst-to-MD review chains. That gives institutions a familiar way to audit increasingly autonomous systems.

JK

Joseph Kim is a leads ChatGPT for Financial Services at OpenAI.

Shawn Wang (swyx), Host · Co-founder, AI Engineer
Wrote “The Rise of the AI Engineer” and founded Smol AI, maker of AI News.

From “Building ChatGPT for Finance — Joseph Kim, OpenAI's Head of Financial Services”, Oct 31, 2026. Watch the full episode

Transcript

Transcribed by machine (Whisper) from the episode’s audio, so a word may be off here and there.

9:19
there are a lot of risks involved in that. So can you talk a bit about the safety that you've baked into the product? Yeah. Safety is super, super important. I would say that, like, and it's only going to get more important because the agents are already getting more autonomous and more independent. And I think that trend, we expect it to continue into 27, into 28. I would say for the product that we built and launched last month, there's a bunch of paradigms that already existed as it pertains to safety and being thoughtful around auditing the work, making sure that the model's not making a mistake that's going to impact business outcomes.
9:58
And we were able to lean heavily into those. And what I would say is the existing product that we launched is a productivity tool for knowledge workers working in the space. and so the idea behind it is there's a deep assumption that there's always a human in the loop, right? So the idea there is you're a financial analyst and you have a laundry list of things you want to get done that day and just like how software engineers do with Codex, for every one of those items, the first thing you do is try to build it with Chachapiti, right? And it might get you 30% of the way there, it might get you 80% of the way there depending on the task, but there's always this presumption of humans in the loop that are presiding over that work and that made it very easy for us to build a product where we could kind of pass off the responsibility of auditing review
10:39
into people's hands and into people's responsibility. And that worked for us well and it was easy to communicate. So that was wonderful. I will say we're thinking very deeply about what comes next and what comes next looks different than a productivity tool that sits and is always being monitored by a human. And I think as we think about what comes next, there's kind of two things we're thinking about. One is how do we kind of shape the security safety profile of the product into something that people understand intuitively.
11:09
And I would say something that people understand very intuitively is how to work with other people. And so our thesis is if we make the product very similar to how you work with your co-workers, your interns, people around you, that is something that people can pick up and learn very quickly, very easily. And therefore, we need to shape the security profile of that to look like a person. And so there's a few things I'll call out there.
11:32
One is there's this notion of read-write, even for people. You're a new software engineer at a company. You oftentimes, when I was at Google, you need something called readability. And so there's these kind of checks and balances and systems in place before you can start mucking around and touching the system. And so there's already systems in place to monitor the work of people. So if models behave more like people, then you can leverage those systems.
11:52
And that makes it very easy to kind of talk about and work through this with these financial institutions. So that's something we're leading into pretty heavily coming up. and the very last thing I would say is at least in the context of banking which is where we kind of started there's already this system in place for junior analysts doing a lot of the grant work, their work gets passed off to associates, associates are reviewing the work, then the associates are kind of adding their secret sauce, their magic, their reasoning that gets passed off to the VP, VP goes up to the director, managing director, so there's this kind of like a hierarchical system where there's this implicit work checking that's happening that's already been built out to try to prevent against mistakes that humans make, right?
12:27
Like humans make just as many mistakes as models. And so those systems are built there. And so we're also leaning heavily in terms of those systems as it pertains to building financial products. Okay.