Building AI Systems for Capital Markets

**Allison Nathan** (0:05)
Welcome to Goldman Sachs Exchanges. I'm Allison Nathan, and I'm here together with George Lee, who is the co-head of the Goldman Sachs Global Institute. Together, we're hosting a series of episodes exploring the rise of AI and everything it could mean for companies, investors, and economies.
George, great to see you again.

**George Lee** (0:24)
Good to see you.

**Allison Nathan** (0:25)
So today we're digging into another fascinating topic. What it takes to actually build products in the AI age, the ingredients, the challenges, and the approaches behind building solutions that first and foremost actually work, but also actually matter.
I really just wanted to first get your thoughts, George, because as we've discussed many times, you're talking to companies, firms, clients about these topics every day. So how much real progress do you see being made here?

**George Lee** (0:55)
Well, I'm very excited for this discussion because in our prior sessions, we've spent a bunch of time talking about the foundational elements of AI, the infrastructural dimensions and the topic of building applications in this environment I think is very timely and useful and we have a great guest for that. But look, I think there are a lot of questions in the ecosystem about what is the perimeter of the foundational dimensions of this relative to the application part of this stack.
Again, our guests will be able to untangle that for us in a really productive way.

**Allison Nathan** (1:27)
All right, so let's bring in our guest. It is Chris Churchman. Chris is the head of Marquee, the firm's digital platform for institutional and corporate clients.
He's also co-chair of the firm's Global Banking & Markets AI Working Group. So you're on the front lines, Chris, on all of these matters in terms of bringing new AI products to the market. So welcome to the show.

**Chris Churchman** (1:47)
Thank you. Excited to be here.

**George Lee** (1:49)
Great. Well, Chris, let's start first by just giving our listeners a little bit of background on what Marquee is, what your core mission is, and then let's talk a little bit about how you're starting to fold in some AI capabilities there.

**Chris Churchman** (2:00)
Sure. Yeah. So excited for this conversation. Thanks for having me.
So Marquee is the firm's platform for institutional corporate clients, and really our mission is to help our clients make decisions under great uncertainty. So we want to become an integral part of our client's investment process. And so as you think about layering in GenAI capabilities into the platform, you can imagine what we've built, maybe it's actually a better way to describe the platform is how we've led in GenAI capabilities because you've got to start with the problem to be solved, not just, oh, let's use AI on that and that and that, which is people start with AI, but let's start with the investment process. And how would a GenAI solution help you be the concierge that helps you down that investment process funnel?
So you start on the top of the funnel, if you like, which is just millions of research articles on different companies, sectors, economic forecasts, et cetera. But then you've also got all of the trading floor commentary about flows, trade ideas, et cetera. And so that's overwhelming for people to find what's relevant to them.
And then you've got all of the analytics that you'd need to look at, all of the data from dozens of vendors produced. We've got an ecosystem, fortunately, called Market View, which contains these, you can think of it like Pinterest for capital markets, where each widget is produced by an expert in their domain. They take the relevant vendor data. They then create an insight on that. And now we have millions of those in the ecosystem. And then we've got pre-trade analytics, like, okay, what trade am I going to do? I can do scenario analysis. I can do back testing. I can do all of these things. But from a user experience point of view, it's very difficult. And I think the paradigm shift that we're seeing in software is that we've gone from a world where users learn software to where software learns users.
And so now we're at the point where you can just express your intent to the system. And the system fully understands all of the capabilities of Marquee and can deliver them to the user. Because that's always been the bottleneck, is expressing intent in natural language as a very low bandwidth channel. But actually you mean way more than what you've actually said. And so Marquee AI, which is only available internally right now, you ask Marquee AI a question which is very targeted to what you care about. But then it will take what's happened recently to break it out into a number of relevant research topics for each of those research topics that will then find all the relevant research, all the relevant trading floor commentary, all the relevant widgets in market view, so data and analytics, and then assemble that into a cohesive thought. And then if it needs to do a calculation, it will pull the widget that contains that data, reliable data from an expert, and then run a plan, do a calculation, like write and run some Python, and give back the answer, and then give you a full, well grounded, every single sentence in the document is grounded to something someone at the firm has said or a calculation that you can audit. And that is the hardest bit to layer on at the end. And then we can talk more about it, but the generative ability of it is key, but it's very, very, very different. We can talk more about it to tame the hallucinations.