Private credit’s tech awakening

In his role as managing director of portfolio strategy at Northleaf Capital, Jon McKeown has witnessed the remarkable growth of private credit, and the importance of technology in fuelling that growth. He tells Alternative Credit Investor how his team is working to integrate private credit technology in a way that works for both fund managers and their investors.

Alternative Credit Investor (ACI): What technologies are you using day-to-day at the moment as part of your job?

Jon McKeown (JM): In my role overseeing portfolio strategy, my team and I utilise data from across our platform, which means relying on a few different technologies. The key is the portfolio management system – on the private credit side we utilise a system called iLevel, which is provided by S&P. It is a relatively customisable solution that allows you to configure the system to the needs of your particular portfolio and the types of transactions that you are undertaking.

At the front-end, we make use of a process management software to support the closing process for new transactions, from the point where we commit to a transaction through to funding. It allows all the different functions at Northleaf to communicate and exchange information in an efficient manner, in addition to reducing operational risk and providing a repository for historical information.

We also use Salesforce, which started life as a CRM system for Limited Partner relationships but is also used on the ‘money-out’ side in terms of managing relationships with market counterparties, sponsors, and providers of fund-level financing, and capturing useful market level data. We then use Power BI to provide dashboards allowing us to make sense of that origination and pricing data.

Like many organisations we also have a data lake where we pull data from disparate sources together, a data warehouse solution which organises that data and produces the current reporting for internal users, and to a certain degree allows people to manipulate the data themselves to answer their own questions, again using a Power BI overlay.

Finally, I would acknowledge that while a focus of recent years has been the migration of core processes away from Excel, this is still a valuable tool for one-off and bespoke analysis, such as modelling the pricing and economics of new fund offerings or conducting certain types of forward-looking scenario analysis.

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ACI: What operational challenges are faced by private debt managers today?

JM: The private debt asset class contains a significant amount of complexity due to a combination of the many details that underpin an individual loan structure, plus the detailed financial data that we receive from our borrowers as part of our asset monitoring processes. Add to this the customisation that is undertaken to meet the needs of both borrowers and investors. The big challenge, to my mind, is to create a process that is repeatable, and which can handle the large amounts of data that we work with, while also accommodating the variations that result from customisation.

You could think of it as being a little bit like if you had a car production line and you are mass producing Ford Model T’s. But then you’ve got these exceptions to the rules. Someone wants it in a different colour, or they want a different feature or a different chassis entirely. We are trying to include all that customisation. There’s probably as many different data points as there are parts in a motor vehicle.

ACI: How is AI being used in private credit?

JM: On the whole I’m excited about it. Northleaf has trialled a couple of different AI solutions, and we have already seen some modest gains for our junior investment professionals.

There are two big gains that I see in AI – one is the efficiency on the investing side, where the team is looking at individual transactions. AI has shown the ability to ingest and provide an initial synthesis of one or more initial documents, which the junior investment professional can then augment with additional analysis and insights before providing a deal screen for broader review. AI addresses more of the ‘grunt’ data work, allowing the analyst to focus on the ‘so what’ implications.

And then secondly, at the portfolio level, AI offers the potential to access and look at a broader array of data and synthesise it to develop insights, which aren’t going to tell us what to do, but are going to help inform questions that we have, give us greater conviction in certain directions that we might go or prompt new questions that we perhaps haven’t thought about. This is an exciting path to explore, but one that is at an early stage.

ACI: Is there a willingness within the private credit sector to share data?

JM: It makes a lot of sense to be able to pull data from across a broader array of providers and then have a third party aggregate that data to provide a comprehensive index. You do see individual platforms providing their view, but I think that it would be a great industry initiative to do so in a more comprehensive manner. The challenge, of course, is a classic economic one of how to encourage sharing that increases overall utility, despite the incentives to protect what is viewed as proprietary data. But who is to say that private debt can’t overcome these impediments, given the quantitative and data-driven nature of the business, and the value of that data to all market participants?

ACI: What keeps private credit CTOs up at night?

JM:  I can’t speak directly for CTOs, but from my seat, the main thing is ensuring we keep building the right capabilities within the team. That means more of what you might call biathletes – people who understand the investment and capital raising process well enough to participate in defining our key questions, but who also have the coding skills and ability to evaluate third party solutions that will provide the tools that help us answer those questions.

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ACI: What are your predictions for the future of technology and private credit?

JM: The progress we’ve had in terms of computing power and now AI does fundamentally raise the ceiling of what is possible. But there’s a large gap between potential and actual realisation. And part of the effort to close that gap will be in terms of the technology choices that organisations make, their success in implementing technology projects from a hardware point of view, getting the right system, and customising it to the business.

But there’s a very large part of that value realisation mobilisation story, which is to do with organisation design, change management, and education of the broader team. So as important as it is to make the right decisions and have the right people in the core technology roles, it’s probably going to be even more important to be thoughtful about the design of how this then integrates to business processes and, critically, how it integrates to decision making.

Ultimately, you want to put the power of the technology in people’s own hands so that they can be using Power BI or whatever else it may be to answer their own questions.

Our clients quite understandably see us as black boxes, or at least semi-opaque ones, and they want transparency. So, what we are building for internal purposes, we are going to have to extend over to the client so that they can have visibility into what we are investing in on their behalf. And that will give them more assurance, more confidence, and will also allow them to make better decisions at their level.

Read more: Beyond Excel: Special report on private credit technology