Women in Financial Markets (WIFM) is a 501c3 non-profit organization whose mission is to connect, elevate, and advance female professionals in the financial industry through education, mentorship, and access to a global network of influential women. We deliver expert-led programming and curated events that provide opportunities to broaden industry knowledge, enhance leadership skills, and further careers.
Lisa Schirf, managing director and global head of data and analytics at Tradeweb, discusses how AI helps pricing strategies for fixed income markets in conversation with Mary Ellen Egan, Senior Editor, Women's Program.
Transcript
Mary Ellen Egan:
Hi, I am Mary Ellen Egan. I'm senior editor for Women's Programs at American Banker, and I'm here today with Lisa Schirf, who is a managing director and global head of data and analytics at Tradeweb. And we're here today at the Women In Financial Markets inaugural conference. So thank you, Women In Financial Markets for hosting us today. So Lisa, let's talk a little bit about what Tradeweb is and the clients you serve. So I guess we'll start with a little background about Tradeweb.
Lisa Schirf:
Sure. Tradeweb is a large global electronic marketplace primarily for fixed income securities as well as some equities. There's over $1.4 trillion a day that changes hands on the platform, which is huge. I joined Tradeweb in May of 2020 with a very broad mandate, which is, Tradeweb has a whole bunch of data we need to figure out what to do with it. And I really broke that down into three parts. The first part is how do we use data and analytics to help our clients better interact with the platform? Secondly, how do we use data and analytics to better run our business? And lastly, we have a whole bunch of data and how do we get that data to our clients in the industry in an effective way to do this? I have a really amazing team. We have over 40 people split between New York and London. I also have a few people who work fully remotely, but it's a really amazing group of people and I'm honored to work with them every day.
Mary Ellen Egan:
So where does the data come from? Is it all data that you generate? Is it from other platforms? Is it publicly held data? Where does it come from? And then how is it put into play?
Lisa Schirf:
Sure. The data is largely generated on the Tradeweb platform. We anonymize and aggregate the data in order to use it. We look at the real, the platform's running real time, and we look at that. We use that to enrich end of day data. What that does is it helps avoid this problem that many industry participants have where the end of day data is disconnected from the intraday data. And so we're able to build products that build out a seamless ecosystem where the intraday and end of day, there's no noise in your portfolio because of disconnects between those two areas.
Mary Ellen Egan:
Since you've taken over, you're doing something a little bit new or different with the end of day data? Correct?
Lisa Schirf:
Yes. We've been leveraging the intraday data to build out end of day pricing. So we've built out benchmark pricing for U.K. guilds, for European government bonds, as well as US treasuries, and there's more to come.
Mary Ellen Egan:
As far as who are the clients that you serve? Are they broadly, are they mostly institutional? Is it retail? What are the different kind of client services that you
Lisa Schirf:
The Tradeweb platform covers three distinct market areas, dealer or wholesale markets, institutional markets, and retail markets. It's a global platform, so U.S., Europe, Asia, Latin America, and there's over 2,500 clients globally really covering the who's who in fixed income markets in the data business. We also cover some clients that aren't transacting on the platform, for example, research companies that are interested in what's going on in financial markets, and that's helps us diversify our client base as well.
Mary Ellen Egan:
The hot topic now is AI. How are you using AI currently and then maybe perhaps where you think it might go in the future?
Lisa Schirf:
Sure. We're using AI in a number of different ways. First, we're using it for pricing products. It's actually a great use case for artificial intelligence. We are essentially replicating what a human would do to build if they were to price a security, but we're essentially giving them superpowers. And machines are much more efficient at leveraging data and consuming huge amounts of data quickly to produce a price. We're also using AI in order to build out execution algorithms. We're using it to do better financial planning within our finance team. We are also using it for LLM technology mostly for more productivity enhancement tools, but we're also looking at other ways we could use it as well.
Mary Ellen Egan:
So currently using AI in several ways, but where do you think it may be going in the near term or in the future? I know we're still early days. There's some concern obviously about use that might violate privacy. So how are you thinking on those terms and where do you think AI might be benefiting you even more in the future?
Lisa Schirf:
The future in financial markets, fixed income markets have been evolving tremendously over a number of years. The electronification of those markets has been accelerating further aided by the COVID crisis, which really pushed that forward. Also, there's been more transparency and more liquidity because of that. And we're looking at LLM technology because there's so much data, people are drowning in the amount of data out in the ecosystem and trying to turn it into something of value. LLM technology is actually one of the tools that is going to help do that. So we think that things are going to get more transparent and more open in financial services, especially more precision and pricing. Things are getting faster and faster and having more data and more information, but being able to get to the right answer faster is going to be very important.
Mary Ellen Egan:
And I assume that's a huge bonus for your company and what you do with all the data you have is because I think it's me as if I'm a client, I'd be drowning in data and not know what to do with it. And I think one of the things that we had discussed before is that it's really important to give the client what they need and cut out the extra noise.
Lisa Schirf:
Yes, yes. And we're helping clients do that every day, really taking all the information in the ecosystem and providing a service to narrow that down into stuff that really helps solve real problems and narrow down that information to pieces of information that are actually going to be helpful to solve a problem for a client.
Mary Ellen Egan:
So I want to, excuse me, change directions and talk about your career journey, because you went to MIT, you have a very interesting, very data-driven background, and so I always liked to hear everybody's origin story.
Lisa Schirf:
So my background's largely in investment banking, asset management technology. I've done a number of different roles in my career. I've been a portfolio manager, a trader, a investment banker, a venture investor, COO of two different asset management firms. And most recently prior to Tradeweb, I was the chief operating officer of the data strategies group and artificial intelligence research at a large global hedge fund. So I've learned a ton in all of those roles. I really think that my diverse experience has given me a unique vantage point to view the financial ecosystem, and that's helped me in my current role and other roles that I've had as well. I really love what I do. I really love bringing together diverse groups of people to help solve really complicated problems. And it's a lot of fun.
Mary Ellen Egan:
Correct me if I'm wrong, but didn't you, veer off into a startup for a little bit. Which is a different experience. How is that different for you than working in a larger, bigger company?
Lisa Schirf:
It's a different experience overall. I did that while I was doing venture investing as well and had a startup of my own. And instead of finding a developer to build a prototype for me, I taught myself to code and I built it myself full stack, front to back. And so I used to go to lots of hacker meetups with my backpack and hoodie. And it was a great experience and I really loved what I learned and it was really formative and helping me be able to do the roles that I've been doing since.
Mary Ellen Egan:
And if we want to encourage more women to go into your field data and analytics and into STEM general, do you have any advice for them or any thoughts about how we can maybe make this, I think there's a continuing drive to make women more interested and more confident about going into a field like yours.
Lisa Schirf:
Yeah, I think be curious. Read a lot, read a lot about what's going on, not just in your own field, but what's going on in other ones. Especially with technology, a lot of the technology is getting passed from one industry to the other. For example, ad tech was very forefront on machine learning and leveraging it for predictive analytics, and that's kind of filtered its way into other industries as well. So remain curious, learn about development tools, learn, take a SQL class or a Python class. You don't have to become an expert in it, but it's important to understand how those tools work and how you can use them as well as all these other tools that are coming out, large language models, for example, but stay open and curious and there's so much to learn and everything is moving very quickly.
Mary Ellen Egan:
Yeah, I think that's the big lesson is everything moves quickly and you got to keep up in order not to fall behind. Yes. Thank you so much, Lisa, for joining us today. And thank you again. Women In Financial Markets for hosting us.