AI promises an enormous opportunity for lenders, but the real story isn’t adoption. It’s a transformation, and most organizations are still only scratching the surface. In this conversation, Optimal Blue’s Kevin Foley speaks with Allison LaForgia about the difference between simply deploying AI tools and building an organization that runs on AI-powered insight.
From market forecasting to operational decision-making, Foley explains why mortgages are uniquely positioned for AI innovation and how technologies like Virtual Economist are helping lenders interpret complex market signals faster than ever before. The discussion also highlights the real challenges lenders face, from data readiness to strategic alignment as the industry moves to the next era of intelligent lending.
#AIMortgage #MortgageTech #OptimalBlue #LendingInnovation
From market forecasting to operational decision-making, Foley explains why mortgages are uniquely positioned for AI innovation and how technologies like Virtual Economist are helping lenders interpret complex market signals faster than ever before. The discussion also highlights the real challenges lenders face, from data readiness to strategic alignment as the industry moves to the next era of intelligent lending.
#AIMortgage #MortgageTech #OptimalBlue #LendingInnovation
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00:06From Las Vegas, Nevada, I'm Allison LaForgia, and today I am sitting with Optimal Blue's Kevin
00:12Foley, who is the Director of Product Management. Kevin, thank you for joining me today. Yeah,
00:17happy to be here, Allison. So there are no shortage of conversations surrounding AI and
00:23mortgage right now. From your perspective, what are the most important shifts that we should be
00:29paying attention to? So, honestly, the most important shift that I would say is that AI is here
00:36and AI is here to stay. So if lenders are out there and you're still thinking about AI as a
00:44buzzword
00:45or a fad or a distraction from your business, that's the big shift that lenders need to be making
00:51now is to understand that AI is here to stay. And the question in my mind is not whether lenders
00:57will
00:58adopt AI, but how lenders will adapt to the reality of AI becoming ubiquitous.
01:03But I'd make one important point there, which is that we're not talking about replacing humans.
01:09We're talking about AI augmenting human capabilities. So that's something that we call human in the loop,
01:15where AI is serving up insights for humans to ultimately continue to be the decision maker
01:22and ultimately still own that decision. But that's certainly not without its challenges. So
01:28implementing AI is a whole other story and there are lots of real challenges to that.
01:33So I think you've made it very clear that the potential is clear to the industry.
01:38Let's talk a little bit about what challenges going into a little more depth on your last answer that
01:44lenders face when moving from that, just adopting AI to adapting to AI.
01:52So it's a great question because I see, so when I try and get a pulse of where lenders are
01:58at today,
01:59the sense that I get is there are a lot of lenders who are out there in the experimentation phase.
02:04So you might have some more technically minded folks within your organization who have leading
02:09initiatives or helping educate the organization about AI and all that it has to offer. And that's great.
02:16But the real value from AI is going to come not from keeping this a tech focused conversation,
02:24but turning this into a people focused conversation and thinking about process change.
02:30So the real value of AI is going to require a wider set of cross-functional stakeholders within your
02:38organization to get into alignment about how to implement AI, embed it within your workflows,
02:44embed it within your processes, embed it within your governance structures, your accountability
02:48structures. How do you establish trust within your organization? How do you measure the success?
02:54All of that is a people focused conversation. And those are the real challenges for lenders being able to
03:03make the most of their AI investment. It's not simply a tech conversation,
03:07it's a people conversation. Let's dig a little bit more into what makes the mortgage industry
03:13particularly well suited for AI driven insight. So that's a great question. I think we have a lot
03:20of benefits. And one of the main ones is that the mortgage industry, compared to other industries,
03:25we sit on a lot of structured data. And so what do I mean by structured data versus unstructured data?
03:31So unstructured data, that's going to be like the transcript of this, of this conversation,
03:35right? Free flowing, you know, we're talking about a lot of things. Structured data would be
03:40a condensed set of bullet points about what we're talking about, right? And when you translate that
03:45into mortgage, so you have your loan, you have everything that's part of your loan, you know,
03:52one facet of that's going to be your rate lock. And then within your rate lock, you're going to have
03:56your lock date, your lock expiration date. But you have all of this information that's packaged up
04:02in a way where we can understand the objects and relationships within that data. That's very
04:09important, because when we're feeding in structured data into AI, it doesn't need to guess or try and
04:15interpret a lot of those objects or relationships. So we're already a step ahead. And the mortgage industry,
04:21we have a ton of structured data, we have organizations like Mismo, Optimal Blue is a
04:25Mismo partner that are all about data standardization, we have a lot of structured data within Optimal Blue,
04:31and that helps us move the ball forward, it kind of provides us, you know, a few steps beyond the
04:37starting line so that we can kind of make the most of that AI investment. And what it allows us
04:42to do
04:43is, you know, two things, really. One is AI can save people time by generating insights faster.
04:51And the second is that we can generate insights that humans just can't reliably see.
04:56Kevin, let's talk a little bit about what this actually looks like, and what strategic problems
05:02Optimal Blue is looking at tackling with AI, and where you see it actually solving real operational
05:10challenges today. Yeah, well, that's a focus that we take into everything that we're doing,
05:15we have to we have to solve real problems that, you know, our lenders are facing. But they really
05:22fall under two categories. So I'll talk about that. And that ties into my last answer, which is,
05:26number one, we are saving lenders time by generating insights faster. And the second is we are generating
05:33insights that lenders aren't going to reliably see on their own. So I'll give you a couple examples.
05:39When I got started on the tech side of this business, I was helping lenders understand their
05:44hedging positions and how those change day over day. So if there's, you know, big market movement,
05:50or there's a lot of loans coming into your out of your pipeline, you might have your secondary gain
05:55loss that changes more than you would expect, right. And so some some mornings I would come in,
06:01and I would spend like up to two hours figuring out what exactly drove the change in the secondary
06:08gain loss for lenders. So that's, you know, real time. And the thing is, when I was doing that work,
06:14and it was taking me longer than expected, trying to solve these tough problems, I was probably falling
06:19behind in other areas, right? Well, with Optimal Blues position assistance, that that whole process
06:27happens automatically as soon as the pipeline is run. So we have an AI summary showing lenders exactly why
06:34their position changed overnight, and explaining every detail behind that. So that's, that's a whole
06:40set of, you know, work that we don't need to do anymore. And if your lenders were doing this
06:47themselves, now their teams are freed up to work on more productive work. So that's one example of how
06:53we're generating insights faster and saving lenders time. The second example, I'll use originator assistant,
07:00and this is getting back to the point of generating insights that humans can't reliably see themselves.
07:06So originator assistant, it's embedded within our pricing engine. And on every search where we're
07:10looking for eligible products and pricing, we're serving up small changes in the loan scenario or
07:16the borrower profile that can help provide better terms for the borrower over the 30 year life of the
07:22loan. So these are small adjustments that you can make small changes to your loan amount,
07:26your credit score, things like that. And we're serving those up now on every single search that
07:31that is being run. If a loan officer was going to try and do that manually themselves,
07:37it just wouldn't work at scale. They wouldn't be able to reliably do this day over day.
07:42And a loan officer might also have some of their own biases about what product is going to be best
07:48for
07:48this borrower, where they're not getting a second opinion or another perspective on what could be
07:54best for these borrowers terms. So that's another example of how we're helping produce insights that
08:02humans just can't reliably do on their own using AI. Last example, we recently had our summit out in
08:10Scottsdale. It was a great success, great time. And we launched our virtual economists. And our virtual
08:16economists, it's our AI and machine learning based forecasting tool. And that's just another example
08:21of how we're bringing AI insights for lenders. So we have to talk about virtual economists.
08:27You just brought it up. For those who haven't seen it and weren't there, what is virtual economist
08:33and how does it change the way that teams approach market forecasting? So it's a great question. It's
08:40something that I worked on personally, very, very excited about it. And so the virtual economist,
08:46it's our forecasting tool. It helps provide forecasts for interest rates and lock volume.
08:52And it solves three of the major pain points that lenders have if they're trying to generate forecasts
08:56today. So everyone in our industry wants to know what's going to happen with interest rates. That
09:01helps us better understand how do we prepare to grow our business? How do we staff effectively?
09:07There's so much that goes into that. But every time that we go, if we're looking to produce forecasts,
09:14we run into three problems. The first is the maintenance of that. So even just in the last month
09:19between our Optimal Blue Summit and here at ICE experience, the market has changed pretty
09:24substantially, right? Interest rates have gone up. There's geopolitical shocks that are happening.
09:30And if you created a forecast a month ago, that forecast is now out of date. You are in a
09:35very
09:35different place. Exactly. Yes. So the virtual economist solves that problem by having continuously
09:41updated market data. So we're always pulling in the latest of what's happening so that you don't
09:47need to worry about your forecast becoming out of date. The second issue is explainability. So if
09:55you're generating forecasts, you're not always going to be able to generate the why behind the forecast.
09:59Okay, these are the numbers, but why is this what we're seeing? And the context is very important
10:05when you're having these conversations. Exactly. And the virtual economist solves that problem by
10:11just simply being able to provide that explanation. So you can ask it, how did you come up with this
10:17forecast? What goes into it? And then the third issue is interactivity. So unless you have the person in
10:26the room with you who's developed your forecast, you can't ask, well, what if this changes? Or what
10:31if that changes? How did the how did these numbers become adjusted? And the virtual economist, you can
10:37do exactly that. You can ask what happens to interest rates if the price of oil stays high for the
10:43rest of
10:43the year, or what happens to interest rates if the price of oil returns back to a baseline. Those are
10:50things
10:50that the virtual economist is tailor made to help provide those insights. So that's that's kind of
10:56an overview of what it does. And again, we're really excited about it. And looking forward to continuing
11:03to work on it. It sounds very exciting. So I want to end with asking you to get your crystal
11:09ball out and
11:10look at some future items. How do you think that AI will separate the lenders who succeed over those who
11:19struggle over the next few years? Well, I think it honestly goes back to a lot of what we've talked
11:26about here in this conversation today. So the things that are going to separate lenders who, you know,
11:33make the most of their AI investment are going to be the ones who are thinking about adapting, you know,
11:38more so than the the ones who are questioning whether they should adopt. It's going to be the ones who
11:44are
11:45thinking about AI, not just as a tech conversation, but as a people conversation, helping establish trust
11:51within your organization, embedding AI across your processes, your governance structures, your
11:57accountability structures, they're going to be the lenders who are focused on generating insights
12:01faster, or generating insights that humans can't reliably see. Those are all of the things that I
12:08think are going to help separate out lenders, you know, who are going to make the most of their,
12:12their AI investment versus, you know, ones who, who aren't, aren't, you know, going to,
12:18you know, going to be part of that, that same wave. And, you know, finally, all of this ties back
12:24to,
12:25to the bottom line. And I have to make a plug here because, you know, speaking of the bottom line,
12:31Optimal Blue in conjunction with MarketWise, we just did a study on ROI that was published yesterday,
12:38really excited to announce that. And what, what MarketWise looked at was the value that lenders
12:44are getting from using Optimal Blue's products across our whole ecosystem. And what MarketWise
12:51found was that lenders are seeing over a thousand dollars of value per loan by implementing Optimal
12:56Blue's products across, across our ecosystem. That's amazing. We're in an environment where
13:02loan volume isn't necessarily where I think we had hoped it would be because of market conditions
13:07and the cost of origination can still be very high. So that's a very exciting number to see.
13:12Yeah, we're really excited about it. I definitely encourage everyone to go check it out. And you can
13:16see so much more detail around the methodology, all the work that went into producing that. But it's
13:22something that we're very excited about. And again, ties back into the whole conversation of, you know,
13:26impacting the bottom line, how to make the most out of your investment. And, you know,
13:31that number reflects a lot of Optimal Blue's perspective that goes into the AI that we build,
13:36the tools that we roll out, and how we're able to generate that value for lenders. So I appreciate
13:40you letting me get that plug in there. Of course. Kevin, thank you so much for talking to me about
13:45what's going on at Optimal Blue, about a virtual economist. I can't wait to see what's next.
13:49Awesome. Thank you, Austin.
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