Optimizing mortgage strategy: AI, data and tech trends with Optimal Blue leaders

HousingWire

What happens when trusted data meets powerful AI? In this conversation, top minds from Optimal Blue Erin Wester and Mike Vough, discuss how today’s leading lenders are using technology to power smarter, faster and more scalable decisions.   Erin explored AI’s Impact on product development, while Mike breaks down the real-world importance of clean, reliable data and what lenders risk when they get it wrong. From evaluating tech partners to driving innovation beyond buzzwords, this conversation offers actionable insights for lenders navigating the next era of mortgage tech.   #MortgageTech #AIinFinance #DataDrivenLending

Transcript

00:00From HousingWire in Dallas, Texas, I'm Diego Sanchez, President of HousingWire.

00:09Today I'm joined by Aaron Wester, Chief Product Officer at Optimal Blue, and Mike Vogue, Head

00:14of Corporate Strategy at Optimal Blue. Welcome, Aaron and Mike.

00:19Thanks for having us, Diego.

00:21Thanks for having us, Diego.

00:23So you both play pivotal roles at Optimal Blue. Can each of you share a little of your

00:29background and how your current roles shape your view on mortgage tech? And Aaron, we'll

00:35start with you.

00:36Yeah, happy to. So I've been with Optimal Blue about nine years, been in product the whole

00:42time, spent a majority of my fintech career in product. I started out in the loan origination

00:48space, which I think really helped kind of shape my view of how all the different players

00:54connect into all of the different areas of the mortgage process.

00:59And that really helped, especially when coming into the world of all things capital markets.

01:04But really looking forward to today's conversation as my background really resides in building

01:10and engaging customers in new product development.

01:15Yeah, how about you, Mike?

01:17Thanks, Diego. Yeah, my background here stems primarily in the capital market space.

01:21So, you know, my first entry into the mortgage world was actually serving as a TBA trader for

01:27a number of mortgage companies. And, you know, one of the things we had to deal with on like

01:31a daily basis was, you know, hey, is this risk position change or is this P&L going to bounce

01:37back because of some data issue? And you're constantly in that role trying to make snap decisions,

01:43but you don't know if the data is wrong or the data is right and or, oh, did this investor

01:48change their pricing? Is this really real, right? That was a common question that we would get asked

01:52as traders and you're working on the phone with clients that are lenders of all different shapes

01:57and sizes trying to maintain their interest rate risk neutrality. You know, most secondary

02:03departments aren't trying to make money. They're just trying to protect that profit margin.

02:05And so having that like bird's eye view of that process of, okay, we have the margin and here's

02:11where we end with that margin. And does the data actually like doesn't give you the ability to make

02:16those actual decisions? So I spent a number of years doing that and then moved over to the product

02:21side. And then about a year ago, I took over this role in the corp strategy part for Albemarle Blue.

02:27Yeah, that's really cool, Mike. So you're in a different perch now kind of looking over a bunch of

02:32different lenders as opposed to within a lender working on capital markets. Why is data more

02:39essential than ever for lenders of all sizes? Yeah. And, you know, I want to give you guys a

02:45shout out for having me on stage at the gathering to talk about this topic, but it doesn't really

02:50matter the size and that's of an institution. And we commonly get that when we talk to lenders,

02:54they're like, hey, you know, that's not really for us. We're a small shop or we're just a couple

02:58loan officers. But what data really is, is it's the results of your business, right? So being able

03:05to constantly review and fine tune how you, you know, run your business from an operational

03:11standpoint, it lives in your data. And if you're not reviewing that, if you're not looking at that,

03:17you're missing out on a big piece of the visibility of your business, right? Like if you're not reviewing

03:22that, you don't know if this, if this part of the business is moving at 10 times the speed of your

03:28peers or less. And so it's such a big part of how lenders could kind of keep honest with themselves

03:34about their performance in terms of reviewing their data. So my favorite topic, AI, Aaron, it's really

03:41changing the trajectory of everything, including mortgage tech and particularly in product development.

03:48What kind of changes are you seeing in the product team and in product development at Optimal Blue?

03:55Yeah, it was more important than ever after the AI kind of wave hit the tech industry on how you

04:04structured the exhaust from the system, the data from the system, like Mike mentioned, and really the

04:09foundational components that companies went with the storage management and continual digestion and

04:18management of their data was more important than ever. Being able to quickly act on engaging different

04:25AI co-pilots tools to be able to build something meaningful was easier to do if you had a clean way

04:33of managing your data set already, which we were fortunate enough to have at Optimal Blue. We've got a

04:38dedicated business intelligence team that that's all they do. They live and breathe the data warehouse and

04:43making sure we've got data coming in in a very structured, a repeatable and reliable fashion that

04:50really helped us kind of hit the ground running when we were building and deploying these different AI

04:55components across the product and pricing engine and across our hedging and trading platforms.

05:00So that was something that I think is kind of like the unsung hero for a lot of folks that are deploying

05:05AI products today is if you had a clean and of course paramount being accurate data set to be able to

05:12engage and act on, you were you were ready to kind of play ball, which we were we were in that fortunate

05:18position of being in. And I think another just on that unsung hero kind of thread, the the other components

05:25that customers may not see that are helping deliver R&D, deliver products and new features to the marketplace

05:33is how AI is being leveraged internally through development organizations. So being able to pick

05:39up and run on AI generated test cases, using AI to generate code in a faster, repeatable fashion is

05:48really helping accelerate the timeline of go to market for a lot of a lot of players. So hopefully people

05:55are going to be seeing if they're not already more in a faster time period without having to wait to

06:01you know, onboard people to be able to do more. The technology is really enabling you to do that.

06:06That's true on the lender side, as their partners and who they've chosen as their providers are

06:12delivering AI automation, but also for the partners themselves, for the financial technology companies

06:20being able to do more with less. And I think that's kind of the the overall tagline of AI. And then really

06:27being able to once you master that and get folks comfortable and confident with what you're

06:31delivering, then then I think that's where the real magic happens. Yeah, Mike Bridget for us.

06:36What do you think about that relationship between accurate data and the ability to deliver really

06:43trustworthy AI driven products and solutions? Yeah, I mean, I'll piggyback off one of Aaron's points.

06:50It all starts with the accuracy, right? If you can't price a loan as accurately as humanly possible,

06:57then the data downstream from that is as good as that, right? So if it's, if you misprice something

07:02up front, it's not going to be as good in the back end. And then you also have to think about

07:06the fact that it's, it's, we're not just thinking about this as like points in time.

07:11This is over the life cycle of a loan in a lot of ways, right? You can't make intelligent pricing

07:15decisions unless you knew where that loan was sold in the secondary market and for what price,

07:20or you didn't see how that loan performed once it was being serviced in house, right?

07:26So that like breadth of data is really, really important. But then also like the granularity

07:32of it. So how much are you tracking along the way? You know, one of the things I'll give a shout

07:36to one of our features that we put out here was, you know, using AI, we created a daily write up of why

07:41someone made or lost money day over day and they're in the capital market space.

07:46And that was only possible because of the fact that we had all of the data, right? We knew

07:51we had every little component part of the profitability of a loan tracked day over day.

07:55And so then you're able to then take that and then train models on top of it.

07:59And we, we found out things that were interesting that we wanted to go back and make code changes

08:03for after we started to take it out of tables and put it in the text. But like, if you don't have

08:08all these columns and rows of data that are accurate, any of these AI solutions, which are

08:14fantastic, but they, they all must sit on top of data that is trusted and, and, you know,

08:20maintained by a trusted partner in the industry. And that's a critical part of that.

08:25So Aaron, you've worked with a lot of mortgage clients in your journey to becoming chief product

08:31officer at Optimal Blue. How do you, and how should lenders evaluate partners

08:40when they're looking to implement new technology and new technology strategies?

08:46Yeah, great question. I think there's a couple of different things folks should keep in mind.

08:51A lot of it is, is tied to, you know, building, building trust with each other. And a lot of that has

08:57to do with track record. A lot of that has to do with referrals, right? There's different areas

09:02that you can lean on as a lender to see, you know, who's the right fit for my business, the way that I

09:08operate. And I think the sign of a, of a healthy relationship with your technology providers is that

09:15kind of, you know, trust fall relationship. You know, I think AI is a great example here. There's a lot

09:20of people still trying to get their arms around, what does this actually mean? How comfortable am I

09:26taking this? How far? And your technology providers should be making you more and more comfortable

09:33and confident in their delivery and packaging of these solutions to make you feel like we're

09:38carrying you along the way and helping you exercise that muscle to reach greater heights,

09:44not just, you know, punting things out there and just saying, hey, make it work because this is cool,

09:49right? It's more of a constant loop of communication and collaboration.

09:54And your partner should be with you every step of that journey and helping push you

10:00to be able to kind of expand those horizons. And then, you know, the other thing is just making

10:05sure that the providers that you're working with are operating on scalable, secure, reliable

10:11infrastructure. That's a big part of, you know, the process that goes into looking at documentation,

10:18you know, audits, being able to really understand the plumbing behind the providers you're choosing,

10:23and making sure they're the right partner for today and tomorrow. And at Optimal Blue,

10:29of course, we're happy to share all of that with our customers and our prospective customers.

10:35Yeah, Erin, just sticking with you for a moment, I love that response about making sure that you're in

10:41a partnership, and that the provider is taking your feedback and incorporating that.

10:46How do you listen to Optimal Blue clients and make sure that you're building for them

10:53as opposed to just building, you know, for yourself?

10:56Yes. Yeah, absolutely. So what we do, a lot of different forums for our customers to make sure

11:04that we're constantly listening. It's a big pillar of how we operate at Optimal Blue. We've adopted this.

11:11We no longer say no to any sort of strategic initiative that our customers are looking for

11:15from us. It's just a matter of when. How can we get them what they need in a timely fashion while

11:22supporting our other customers and executing on our roadmap? And a lot of times, those are very

11:27fruitful conversations where we say, okay, we understand what you need. Would you put that ahead

11:32of what we've already laid out? And luckily for us, more times often than not, they say, no,

11:37no, no, no, do it. Do that first and then come back. And it's great because it allows us to create

11:41a very healthy, mature, long-term roadmap that we know is tied to real-life use cases. And our forums

11:49are a great avenue to gain that kind of insight. We have our Capital Markets Forum in Nashville coming

11:56up in September that if you're not going to yet, please sign up. That's going to be absolutely amazing.

12:02And then we also are going to have our summit that will be accessible to all of our customers.

12:09And then always be on the lookout if you're an Optimal Blue customer for our work groups. Our work

12:13groups are really kind of that laser-focused, initiative-based way for us to garnish information

12:21and get feedback from our customers while we're knee-deep in the R&D process. But it is very critical

12:28for the voice of the customer to be leveraged every step along that process from roadmap creation

12:35to initiative requirement building to go-to-market and execution.

12:39Amazing. And so let's look ahead for a little bit. Where do you both see the biggest opportunities

12:48for meaningful progress in mortgage technology? And let's go beyond the obvious talking points,

12:55you know, like increased efficiency and ability to scale. Let's really dig in here. And Erin,

13:01why don't we start with you? Yeah. So I think a lot of AI is currently being thought of as add-ons.

13:09You know, how do I add this on or how do I supplement, you know, this, whatever task it is

13:14that I'm looking to automate or make more efficient? And how can I bring more value? When really, I think,

13:21the kind of lock and key for AI is embedding it into current workflows with no additional touch points.

13:29I think if we can start thinking of how do we get away from engage this over here to then make a

13:34decision over here to where this is always being engaged as you're going through your day-to-day

13:39operations, that'll naturally close the timeline of getting things done. So then you're able to minimize

13:47what you do across the board to create space to be able to do things that are, you know, that will

13:52really be innovative and bring new value and new processes to the mortgage space. And I think

14:00that's the sweet spot. And that's certainly what we're aiming for here at Optimal Blue.

14:03All right, Mike, build on that and then take us home.

14:07Yeah. To build on Erin's point there, you know, one of the things that we want to kind of close the gap

14:12on is that difference between the primary and the secondary market, right? When lenders are out

14:17there creating their pricing today that they put out to the street to originate loans, there's some

14:22expectation of profitability in that, right? That's how they foresee keeping the lights on in the future,

14:27how they foresee paying people, et cetera. And a big impact of that is investor demand for specific

14:32bonds and loans that are created out there. Well, right now, I would say it's very fragmented. Folks are

14:38you know, reviewing maybe the impact of their loan sales, maybe weekly, maybe once a month.

14:43And it takes humans looking at this data to then, okay, I'm going to look over here in this other

14:48system. I'm going to go over to this other system and I'm going to create my new margins and load them

14:51in and see if originations go up or down. And it's, it's very fragmented. And it takes sometimes from

14:58the data we see, maybe even weeks to months to see the impact of investor demand flow into

15:03front end pricing. So making sure that that is like a quick, you know, hey, investor one, two,

15:09three yesterday really paid up for my smaller loan amount loans. Well, I want to make sure that that's

15:15my pricing tomorrow, right? Like I need to make sure that that is, that is as live as humanly possible,

15:20especially in a market that we have today where folks are fighting for every basis point.

15:25We need to, we need to pull that feedback from the secondary market closer to the primary and vice versa.

15:30And right now that feedback loop is, is really disjointed. And we think the combination of

15:35the data that we have here and the technology that we're investing in will bring a lot of those

15:39insights closer to the people who are actually making those decisions and make it that they can

15:44make that decision really quickly with a ton of confidence is, is another one of our, of our goals

15:49here at Apple Blue. Well, Mike and Erin, this has been a really interesting, fascinating conversation.

15:55Thank you for joining me today. Thank you for having us. Appreciate the opportunity.

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