Remy Blaire: The relationship between traditional commercial banks and private credit direct lenders has evolved from competition into a codependent hybrid partnership model. Rather than ceding corporate lending to non-bank capital funds or taking excessive leverage onto bank balance sheets, major financial institutions are creating integrated co-lending structures as well as asset-backed partnerships and risk transfer platforms.
At the same time, artificial intelligence is transforming consumer loans within private credit, and key areas include underwriting beyond FICO, along with dynamic pricing and real-time risk profiling. Also, agentic AI for continuous portfolio monitoring and early collections, as well as scalability for forward flow agreements.
Well, joining us this afternoon to break all of this down is Elliott Lorenz, co-founder and CEO of EdgeFocus. Elliott, good afternoon. Thank you so much for joining us. So here we are in the final quarter of 2026. Give us your take on the state of private credit right now.
Elliott Lorenz: There’s been a lot of scrutiny in private credit, certainly over the past year. Private credit’s now too big for regulators and investors not to ask what’s going on underneath. You know, interest rates, of course, are a really large part of why private credit is growing, but we’re not seeing overexuberance. Investors are still asking tough questions, demanding transparency.
And as you just mentioned, Remy, banks and private credit firms really aren’t separate anymore. They sit in the same part of the chain, different tranches, potentially, of that chain. So the real questions are, you know, where is that risk ending up? Who’s watching it? And the question on our mind really is not whether private credit grows, but whether it grows, you know, with underwriting, transparency, and monitoring to ultimately back it up.
Remy Blaire: Yeah. And Elliott, as you mentioned, those are all questions that we are asking. But I do also want to bring in artificial intelligence, because that is affecting industries as well as the role of labor. But when it comes to the role of AI within the space, what does it actually mean for consumer loans?
Elliott Lorenz: One of the biggest roles, in our view, is forecasting. So each month, really understanding how models update, making predictions about loss, early payoffs, [unclear] loan pool, really understanding at the individual loan level what’s happening. You know, AI certainly helps close the loop with underwriting. You know, it allows us to ask questions about borrowers who are paying, different segments potentially performing better or worse than others, and seeing how quickly that can ultimately be fixed.
The great part about generative AI is that it never stops working. You know, it reads everything that the numbers miss. It looks at servicer notes, loan documents, borrower messages, and flags patterns as they come up. And finally, you know, as an investor, you can ask things of generative AI in really plain English. Ask as many questions as you want, looking at very unstructured data, so you don’t have to wait for an analyst to build a report. You can really ask the question as an investor right away.
So a lot of this monitoring is really moving from being, you know, more of a quarterly reporting mechanism to constant oversight. And then lenders and investors and ratings agencies, these are all kind of looking at the same loan-level data at the same time, which is adding a lot more transparency to the asset class.
Remy Blaire: Yeah, and speaking of which, can you take us through how this tech-driven underwriting is actually helping to increase access to fairly priced loans for consumers?
Elliott Lorenz: Credit scores, generally speaking, put people in pretty broad buckets. We use a lot of machine learning and AI internally, and that allows us to look at more data to ultimately price borrowers individually and more accurately than they may have gotten otherwise. And so what that ultimately means is we’re able to find more good borrowers that don’t fit the typical profile, and get those borrowers approved at fair rates instead of them being turned down or ultimately overcharged.
You know, there are still humans in the loop. People are setting rules, checking the models. The technology is applied in a very consistent way across millions and millions of decisions. You know, internally at EdgeFocus, our partners used our credit model to originate more than $2 billion worth of loans last year to over 100,000 Americans.
Remy Blaire: And Elliott, finally, before I let you go, as mega managers out there, the likes of Apollo and Blackstone, continue to expand into asset-based finance as well as consumer specialty credit, how do you maintain your competitive moat?
Elliott Lorenz: It’s about focusing on the basics: understanding the data that’s important, having a lot of attention to detail from a modeling standpoint, and then ultimately having really good access. You know, our firm connects large private credit investors to consumer lending platforms. And it’s really important for us to have best-in-class data and best-in-class modeling, to ultimately find the very best borrowers and provide that value to our investors.
Remy Blaire: Well, Elliott, great talking to you. Thank you so much for joining us this afternoon and weighing in. Appreciate your time.
Elliott Lorenz: Good to be here. Thank you.
