Is AI the solution to data analytics challenges?

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Anthony Booth: One of the challenges that our clients have today is actually navigating the decision making and coming up with a very focused solution. And I think where Fusion comes into it is it starts to converge and simplify their operating model by bringing disparate data sets together with really comprehensive analytics reporting and data distribution capabilities.

Jason Mirsky: Hi, you're listening to ‘Market Matters,’ our market series here on J.P. Morgan's Making Sense. I'm Jason Mirsky, head of Data Solutions in Securities Services at J.P. Morgan. In today's episode, we'll be discussing some of the challenges that institutional investors face in terms of their analytics and what we might expect also with the growth of AI. I'm joined today by Anthony Booth, head of Fusion Analytics and Reporting at J.P. Morgan. Welcome Anthony.

Anthony Booth: Hi, Jason. Great to be here.

Jason Mirsky: Excellent. Before we dive in, maybe you can give us a little background on yourself and your work history at J.P. Morgan.

Anthony Booth: Sure. So I've been with J.P. Morgan for 16 years and I've been in the industry for 30 years overall with a focus on analytics and reporting. What my job or role entails is I'm a global product manager, operations manager in post-trade analytics and compliance, specifically in performance risk exposure analytics.

Jason Mirsky: That's great because I'm planning on asking you a lot of analytics questions today. So let's set the scene. What kind of analytics are you seeing our clients express interest in?

Anthony Booth: So the first one is in the performance measurement and attribution space. So your rate of return calculations, different attribution analysis, contribution analysis. Then we can get into the ex post risk and ex-ante risk analytics, which start to look at things like tracking error, information ratio, value at risk analysis, or stress test scenarios. We then have a liquidity risk offering, which looks at the liquidity profile of the funds, how quickly you could liquidate those or even just manage the dilution effect for subscriptions and redemptions. We also have an exposure analytics offering that starts to look at geographical exposure, sector exposure, issuer exposure, currency exposure, and other dimensions that sit behind that. We also have an ESG analytics or sustainable investments frame to that where we want to bring in the different ESG attributes into either the performance risk or even just standalone reporting to support different regulatory or just investment analysis. And then we look at it across different asset classes and different books of records to bring together a holistic post-trade analytics and reporting.

Jason Mirsky: Great. So it's a full suite of analytics that you've described. Let's talk a little bit more about performance analytics. What sorts of needs do you hear from our institutional investor clients?

Anthony Booth: It's a good question, Jason. It always starts with precise performance measurement results, where you've got integrity of those results from the security level, the individual holding all the way up the investment hierarchy to the whole of portfolio view. And that needs to be delivered not only in an accurate way, but also in a timely basis. Secondly, the different books of records. So if we look at the accounting book of record that's seen as the official book of record for performance results, generally delivered on a monthly basis where you could have restatements or corrections that will occur. Thirdly, the investment manager or trader view, which is focused on the investment book of record where no restatements occur and it's generally looking at the last day performance. In addition to that, if we look at attribution analysis above and beyond the rate of return calculations, you can have different types of methods and models and that could start with a top-down total plan attribution model. You could then look at different asset classes, two or three factor Brinson methods or even decision-based attribution models that start to look at your strategic asset allocation horizons and the different decisions that occur across that. And there could be seven different decisions from your multi-year strategic asset allocation to the implementation this month. So there are different methodologies that can be used.

Jason Mirsky: That's a lot of options there. It must present a lot of challenges. So what sort of challenges are clients facing today that impact the delivery of their performance analytics? Specifically, are there data challenges, analytical challenges, reporting challenges, visualization challenges?

Anthony Booth: So from a data point of view, it all starts with high quality data and our clients are generally dealing with multiple sources of accounting data, bringing that together, curating it, running their quality control checks. On top of that, they could have restatements, they could have corrections that are running through. From an analytical perspective, there are different methodologies, be that cash flow timing, rates of return calculations, attribution methods, and actually picking the right methods and the right models to fit in with the asset classes that you're implementing. From a reporting perspective, making sure that we can put the data in the right shape so that we can present the right reports, the right visualizations, but also make sure that we've got the right controls on that. So when they report that to the investment committees, we have high quality information going out in a consistent manner. Two other challenges that are less related to performance specifically, but one is scale. So generally in the market we're seeing our clients having to operate with reduced head count, but also with time compression, but also delivering high quality information. And then from a governance perspective, again, those investment committee reports, making sure they can trace back the data and the controls that sit with that.

Jason Mirsky: So slightly different topic, but with the rise of private assets over the last decade, portfolio complexity is also a challenge for institutional investors seeking to track their performance. What aspects present unique challenges in the private asset space?

Anthony Booth: That's a great question, Jason. So one is like there's no standard identifiers for the private investments and the ability to get transparency into those funds and bringing that into a data space where you can run the analytics off that. So then once you get right security masters transparency positions, how do I then curate and orchestrate a scalable operating model to make sure I get accurate analytics? Secondly, for the different vehicles within the private asset space, there are different time horizons and they all require different historical cash flows to support the analytics. So again, it comes back to a data challenge of actually bringing together the right data through the right scale to run the analytics. Thirdly, the timing of the valuations and the triggers of when to run those analytics. So in the public market space, you've got a regular cadence of where you get your valuations and you run your performance. For the private assets they may be running on a quarterly or a semi-annual basis. So what triggers are you using to calculate the analytics? Finally, from a reporting perspective, you need to be able to provide the in-depth private asset analytics so that you get the right information in terms of an investment decision perspective, but also at a whole of portfolio performance perspective, you need to be able to get accurate and a singular whole of performance number.

Jason Mirsky: What sorts of infrastructure and operating model challenges are clients experiencing? I mean, you mentioned already trying to do faster with less people. Are there other operating model challenges they're up against?

Anthony Booth: Yeah, there are. So firstly, if we look at just which systems and how do they want to operate from a competency perspective, the first question that our clients are looking at is, “Do I want to partner with vendor from a third-party perspective?” or “Do I want to do this in-house and take control of the analytics and the reporting?” Secondly to that is “Am I using an established platform?” or “Am I going from more of a cloud-based platform like a Snowflake or Databricks?” “How do I actually get to the root of the exceptions in terms of my operational process, know where I am in that daily or monthly cycle?” And that will have a different challenge for clients in terms of scale, whether you're dealing with 10 portfolios, a thousand portfolios or 10000, which creates a different challenge. Then they've got to look at their exception rate or restatement rates, and what that means is, we really want to drive a high straight through processing rate so that we can focus on complex asset classes and the exceptions or more qualitative and quantitative results in terms of the interpretation than just checking every single metric that comes through.

Jason Mirsky: We've spoken about a lot of different kinds of analytics, a lot of different types of challenges that our clients are facing, but maybe we can talk a bit about solutions. How do you see AI being used or investigated for use in this space? Do you think it's going to be helpful?

Anthony Booth: Absolutely. I think AI can help and I think it can help in two different ways. The first way is how can we explore the usage of AI, agentic AI chatbots, et cetera, to deliver operational efficiency? And I think that's very early in the maturity cycle. So I think people are just playing around in terms of what's the best method for using that to drive operational scale. Then they're also looking at how can I incorporate AI into the analysis of the performance results and together with not only the performance results, but other analytics for their portfolios. So now can I go out and look at research? Can I go look at fundamentals? Can I use deterministic reporting to produce some level of commentary above and beyond the numbers?

Jason Mirsky: Interesting. So it's probably the right time to talk about how J.P. Morgan can help. Within data solutions, we've been working on Fusion by J.P. Morgan, and Fusion consists of two main components. One is the enterprise data management component and the other is Fusion analytics. How can Fusion analytics play into these series of pinpoints, hopefully, in a helpful way?

Anthony Booth: So I think where Fusion comes into it is, it enables bringing together disparate data points into a well curated data shape. It then has very strong integration with its analytics partners, top-tier solutions that will deliver comprehensive performance analytics, risk analytics with a strong operating model, with a strong governance traceability that sits behind those results. And then it has the flexibility, from a reporting point of view, to bring together multiple analytics onto one page or a very flexible and modern data distribution channel where you could get data via an API, you could get it via Snowflake, you get it via Databricks or other channels as the client sees fit.

Jason Mirsky: Excellent. Thank you Anthony. So before we close, if you could offer one piece of advice for our clients, what would it be?

Anthony Booth: The thing we haven't talked about, which maybe links into the analytics and the value prop that sits with that is that our clients are actually facing a, a very complex situation where there are a lot of different providers or choices that are available, right? So they could look at what sort of reporting options do they have, what sort of analytical systems do they want, what sort of data management platforms do they want? And if they will have maybe two or three or four choices for each one of those components of their stack. And if you looked back maybe 20 years ago with maybe some of the technology constraints that are available, it made things a little bit easier in terms of how they were trying to execute on their investment objectives. One of the challenges that our clients have today is actually navigating the decision making and coming up with a very focused solution. And I think where Fusion comes into it is it starts to converge and simplify their operating model by bringing disparate data sets together with really comprehensive analytics reporting and data distribution capabilities. So if I was talking to our clients in terms of thinking about what sort of advice I'd be saying, “What is the core competency that you are really chasing? What do you want to be excellent at? Who do you want to partner with to help you get there so that you can deliver on the strategic results that you're looking at?”

Jason Mirsky: That's a great answer. Thanks Anthony, and thanks for joining us today.

Anthony Booth: Thank you, Jason,

Jason Mirsky: And to everybody out there, thanks for listening to Market Matters, our market series here on J.P. Morgan's Making Sense.

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