Private Credit Under Pressure: Liquidity Risks, Fraud & the AI Shift

A key market focus of the first quarter of this year was the turmoil of private credit.

Well the we feel that is haunting the industry is not a sudden crash, but a slow burn credit crunch that does build over time and rising interest burdens as well as AI disruptions and trapped investor liquidity forcing a silent wave of restructurings that could ultimately choke off credit availability to middle market businesses across the economy and private credit names haven't moved past these.

Concerns while Apollo Global has nicely bounced off loads, Blackstone, Blue Owl and KKR were all trading below their 200 day moving averages and we're also learning that Apollo's private credit fund received 16.8% redemption requests, and this does mean a significant portion of its investors are trying to pull their cash out all at once, even as Apollo caps requests at 5%.

Now because the fund hit its limit, Apollo has had to enforce prorated redemptions.

Investors who asked.

For their money back received a fraction of it while the rest is locked in the fund.

Well joining me live here at the New York Stock Exchange this morning is CEO and co-founder of that.

Good morning.

Great to have you here.

Thank you so much for joining me.

Thanks for having me.

Well, as we reflect on the first half of 2026, we know that private credit has been an area of concern.

So when we peel back the layers of the onion, what is happening and what is happening when it comes to the systemic level.

So I think there's a lot of anxiety in private credit because we're talking about retirement savings and people invest in private credit to get a steady return.

They're not looking for a moonshot like SpaceX outcome.

So when the value of the assets falls, there's a lot of anxiety.

And I really think we're conflating two issues in private credit.

So first, you have direct lending, and there's been billions of dollars invested in software companies over the last decade.

And now in a post-AI world there's concern about the value of those companies.

So there may be some challenges with recovery.

I think the second category is asset-backed.

So any capital backing real assets like cars, houses, receivables, the credit quality there is very strong, but there's been some fraud, so.

Tra First Brands, we think that's a governance issue.

It's a really important issue to fix for the investors, but it is fixable.

So I think what you're going to see with private credit is that it will continue to be a really important part of kind of the retirement income category, but you're going to see a rotation from direct lending and software companies to asset backed.

Yes, so I think two key words that you mentioned there are fraud as well as rotation.

So how can fraud Happen in such a closely watched market.

Yes, so let's take Trey Color as an example.

So an auto lender, basically the industry works where you can send emails, Excel, and FTP folders and advance billions of dollars of capital.

In Trey Color's case, they advanced $2.2 billion against $1.4 billion of real assets.

That was $800 million fraud that directly resulted in the bottom line for those investors behind it.

And the industry really kind of runs on this kind of you can submit an Excel that's not verified and you wire against it.

We make the analogy to kind of the paper check industry.

So it used to be decades ago that I would send you a check that would get routed to your bank.

They'd eyeball the signature.

That's obviously very slow and error prone.

Now we have ACH.

That's a standard protocol.

With data verification and settlement, and we do $86 trillion a year now through ACH.

I think the ABF market asset backed market has not yet had the ACH moment.

I think some of this fraud could be a catalyst that has some of these large investors rethinking the practices that are in place that do enable fraud like.

Yes, so identifying these blind spots is key.

So what are you doing to address these blind spots?

So Subpoint is my company and we're trying to create a new standard for asset-backed finance.

So we don't want any more of this kind of email, Excel, PDF trading back and forth.

We connect directly to the systems of record.

We organize and verify all the asset data at the asset level.

We do all the calculations for waterfalls, so who owes whom what and compliance.

And then we have all the workflows for approvals and changes.

We think when you put that together, you have something that would prevent things like reor makes the market more liquid, it makes it faster, less error prone, and they are real results.

So when we onboard new clients, whether it's a borrower or a lender, we kind of upload their existing reports and we see mistakes about 7.5% of the time in those existing reports.

And again, these are kind of reports that are being used.

To funnel billions of dollars between these companies and Stuart, finally, before I let you go, all of us are keeping a close eye on artificial intelligence.

So when it comes to the role of AI in private credit, tell us what you're seeing right now and what your expectations are.

Yes, so I think AI in finance in general is both kind of underused and overhyped.

So underused, there's about 3.5 million people in the US.

Some of the most talented people in our economy that are doing kind of soul crushing manual tasks like looking at two Excel files and reconciling numbers, I think AI can help elevate those people and automate a lot of those tasks.

That said, I think it's also overhyped.

So if you look at some of the benchmarks for creating a financial model, the best performing models right now perform at about 30%, and that's where you have chained arithmetic, synthetic reasoning, all the things that are needed to create a financial model.

And so you can't just 0.1 of these AI tools at a problem and have it completed end to end.

So you know we think it's really something that is very powerful but kind of has to be used in the right way.

And less than 60 seconds here.

So we're talking about a heavily regulated industry for a good reason for consumer protection as well.

So when it comes to the intersection of AI and private credit, what are your expectations moving forward?

Yes, so I think.

The models will continue to get better, but I think what's interesting is there's a difference between kind of coding tasks and financial tasks.

So in coding you have this huge training library through GitHub you can train on.

You've got a deterministic layer, and you can compile so you can see if the code is correct or not and self-correct.

None of those things at best exist in the financial market.

The data is private.

It's very difficult to tell if the model is right or not.

There's not kind of this software orchestration layer.

So I think to win you kind of need three things.

You need the financial experts.

So I think those.

0.5 million people become more, not less valuable.

You need the AI which can do things faster and kind of automate some of these mundane tasks, and you need an orchestration layer that does the workflows, some of the calculations that are really important.

I think if you put those three things together, you get something that's fast, more accurate, and without the downsides of AI.

So it's zero defect and audible, which I think is really important for this industry.

Well, Stuart, it was great having you on the show this morning.

Thank you so much for breaking it down.

I appreciate your perspective.

Thank you.