Longtime financial technology provider Fidelity National Information Services (FIS) has been steadily introducing artificial intelligence (AI) into its FIS Loan Services Suite, analyzing where it provides the most benefits to customers active in the broadly syndicated loan (BSL) and private credit markets. One of those customers, Napier Park, an alternative credit manager owned by First Eagle Investment Company, was one of the first FIS Loan Services Suite clients, working closely with the fintech for more than 15 years to hone the technology.
In a recent conversation with Asset Securitization Report, Heleen Brody, senior product manager at FIS, and Evan Odim, a managing director at Napier Park and head of its U.S. CLO portfolio operations team, discussed the data challenges faced by alternative credit managers, how technology has become the cornerstone of their investment platforms, and the increasingly critical role AI is playing.
ASR: Where are the key challenges for credit managers today?
Brody: One of the biggest is the volume of data, especially when market participants are less automated and maintain data in spreadsheets and databases. We're starting to see that change with AI. Reading indentures, developing compliance tools, reading credit agreements, pulling out key data to keep track of things like ratings and spreads, interest-rate floors and ceilings. Anytime you're reading data manually, you're also inputting data somewhere manually. So even if you find the right data, which most people do, there's always the risk of a hiccup and inputting it incorrectly.
ASR: Do you have an example of this input error?
Brody: The more sophisticated compliance tests have become, the more data is required to assess those tests, and the more opportunity there is for mistakes when the data is collected manually. The FIS Loan Services Suite looks at 75 to 100 fields—things like ratings and spreads—when reviewing credit agreements, to support clients' middle-offices. Clients who have positions in those loans are generally very cooperative in sending us the credit agreements so we can collect that information.
The more sophisticated compliance tests have become, the more data is required to assess those tests.
ASR: How does AI fit into this?
Brody: Taking a step back, documents like loan-agent notices, another source of data, came to us in mostly the same format, and optical character recognition was a good solution to read and input that data. That's not the case for credit agreements, which are authored by different law firms, very lengthy, and may require reading several sections of an agreement to generate a piece of data you need. AI is learning to do that.
ASR: How does that benefit credit managers?
Odim: AI and an overall focus on technology can create a ton of value from our perspective. If you're working with a strategic partner with a similar mindset, it opens up the doors to operational scalability—you can do a lot more with less. Setting up APIs for seamless data flow back and forth, rather than emailing, and data transparency—a surprising number of vendors don't have that mindset.
There are very few things that set managers apart right now, so we look at how we can add value at the margin. We need to have a business that's scalable, where we don't necessarily rely on more bodies, and where we are responsive to loan arrangers, investors and counterparties. A strategic technology partner helps us do that.
ASR: Can you provide an example of what that type of partner can do?
Odim: We're in a market now where spreads have come in and we can keep on printing deals, but there's also a timeliness aspect to that. If an investor with cash comes up and says, 'I want to print a deal,' you as a manager can no longer say, 'Yes, I'll get back to you in a few days.' Instead, it's, 'We'll get back to you in a few hours.' And to produce that data, that model portfolio, you need to have ready access to the data--it can't be housed on spreadsheets—and a technology partner you can rely on.
ASR: What can FIS provide at the front-end during the investment process?
Odim: We have a slew of portfolios, so how do you monitor that risk? There are hundreds of calculations that go into monitoring a portfolio, and you can take a vanilla, very general approach to it. FIS has the technology to take a very concentrated, focused approach to calculating these tests accurately, and that enables us to trust the data and the ability to make decisions and hopefully stay ahead of market dislocations.
Using technology to read credit agreements ... more people are doing those things because they have to, not because they would like to.
ASR: How might AI play a role, looking ahead?
Brody: Building on what Evan has said, the greater the volume of deals, the more rapidly information has to be shared and made available. The CLO market had a record year in 2024, and this year is expected to be similar, so all of that volume requires everybody to be working efficiently and effectively. AI and other tools, like extract, transform and load (ETL) tools, are the things everybody is using. So it's a question of how effectively you can use them.
Secondly, with ever better data and tools, we're starting to see more sophisticated deals, at least anecdotally, and they require market participants to have very accurate information, which AI can help with.
ASR: Do you have an example?
Brody: Compliance testing is increasingly complex. Even loan warehouses have become more complex than they ever used to be. Back in the day, you barely did compliance in a warehouse, and today—I'm told by compliance teams—the sophistication of some warehouses' compliance is very close to that of the CLOs and the indentures themselves. So AI tools that are fit for that purpose can help. The faster and more accurately these tasks can be completed, the more confidence teams have in the information they use to make decisions.
One of our foundational elements was using technology to free up our client services teams to be more available to provide value-added support to collateral managers. We could join them in a call with the rating agency, for example, if there was a disagreement about the rating or what an indenture permitted. AI buys us more of that time, freeing up our expertise to provide more value and support to clients.
Odim: Our developers are working with the FIS folks now to figure out how to scrape financials and ingest that data into the system, asking FIS to do the scraping while we partially build how to pull that information into our system and our GUI (graphical user interface). That's one good example.
Today, when building a credit manager's support system, technology has become the cornerstone, whereas before it may have been supplemental. Using technology to read credit agreements, indentures and financials, or query internal data with an AI chatbot—more people are doing those things because they have to, not because they would like to. And those future AI uses, such as predictive modeling or portfolio optimization, are the cases the industry is starting to work towards.
