Home Lending Pal: Disrupting the Mortgage Industry with Cloud and AI

of working, no matter the industry. Join hosts from your favorite Pushkin Industries podcasts as they use their expertise to deepen these conversations, and of course Malcolm Gladwell will guide you through the season as your host and provide his thoughts and analysis along the way. Look out for new episodes of Smart Talks with IBM on the I Heart Radio app, Apple Podcasts, or wherever you get your podcasts,

and learn more at IBM dot com slash smart talks. Hello, Hello, welcome to a new season of Smart Talks with IBM, a podcast from pushed In Industries, I Heart Radio and IBM. I'm Malcolm Gladwell. This season we're talking to new creators, the developers, data scientists, c t o s and other visionaries who are creatively applying technology in business to drive change. Channeling their knowledge and expertise, they're developing more creative and

effective solutions no matter the industry. Our guest today are Brian Young and Stephen Better, co founders of home lending Pal. Home lending Pal is a member of the IBM hyper Protect Accelerator, an investment readiness and technical mentorship program that supports impact focused startups leveraging highly sensitive data. Their story

is a perfect place to start our season. They recognized a profound problem, the horrible process of getting a home loan, especially if you're part of an unders serve community, a process that, as you'll hear, is not only confusing and complex, but often deeply unfair. So Brian and Stephen teamed up to use technology to attack that problem in a bunch

of creative ways. You'll hear how they're tapping into blockchain to make the home loan process more transparent and fair, using AI to help people learn how to qualify for a loan, and relying on IBM technology to store consumers most sensitive information safely in the cloud. Brian and Stephen talked with Jacob Goldstein, host of the pushkin podcast What's Your Problem. Jacob has covered technology and business for over a decade, first at The Wall Street Journal, then at MPR.

Now let's get into the interview. Let's start this like a brom car How did you meet each other and decided to start a company together. Steven was supposed to come to a bachelor party in Miami and didn't show up, and it broke my heart. There's more to the story than just simply that one of my old employees introduced us. I've just left Marcato. They've been acquired for one point for a billion, and I am, you know, living the Miami lifestyle. You know, I have a condo on the

water and all the nice things to go with. A guy named Michael Ramsey had asked me, you know what, I helped him do mortgage lead generation and I was like, you know, sure, I'm not doing anything else? Why not? And I meet Steve that he was in North Carolina. I left a pretty fruitful career in banking. I was an underwriter. Underwriting loans mean it's basically deciding who should

get a loan and at what interest rate? Right, Absolutely due diligence, right, which is understanding whether or not this particular individual has the worldwithal to afford the mortgage. Also the credit risk individual presents. But there was this disconnect in that process where you have hidden action taking place on one side of the transaction while you have another side of the transaction that that tends to hide information.

And just to be clear, it's the borrower who hides information and the bank that hides the action the lender in most cases, but this is usually both sides of the negotia. Everybody's hiding stuff from everybody else. There's a absolutely and it's like sort of inadvertent as well too, right in that process, and things fall through the cracks, and you know, falling through the cracks means weaks without notification from a bar's perspective as to whether or not

you know this deal is moving forward. Okay, So so the problem is a lack of information on both sides, and that winds up leading to bad outcomes. It winds up leading to long delays that are frustrating or scary for the for the borrower, yes, who are of consumers just don't have anywhere to go if you go online, everything is too broad engineering, especially if you know you're

not ready to buy at that moment. Uh. If you talk to a lender or relatory, if you're not ready to buy at that moment, there they'll help you, but it's not the same level of help. But you're not gonna get that same level of support over months because you know, buying houses and like buying a piece of candy online, And so we really looked at, Okay, well, how can we give people this safe environment to go explore and understand when homeownership could look like for them

based on their personal information. And that's kind of when I reach back out to Stephen around August of two thousand seventeen and said, hey, you know, we need to do this together. You understand the back inside from a lender underwritish perspective, and I understand the plight of the consumers, and if we come together, this could be something that could be really unique. A capitalist solution to a social

challenge is probably the best way to put it. So Stephen, you're sort of coming from the banking side, and Brian

you're sort of coming from the tech side. Absolutely, what exactly is the problem that you've got us are trying to solve when you start this company, and its simple assessence is data democratization, the ability to take complex information and simplify so that someone that isn't an expert like Stephen can understand what's going on, and in this case specifically, what is the data that you're trying to democratize underwriting data, so the decision or the data that is utilized to

determine whether or not you are approved or declined for a home loan. So right now, if I go apply for a loan, they approve me or they decline me. But do I know why not? Really? I mean, you get a letter of an adverse letter, but it's still very broad Engineeric, it doesn't really tell you what to focus on next, but you do have a very high level sense of why your decline. Yeah, there's no true guidance from that point of rejection, right there's no fundamental

understanding as to what could I have done better? And that's really what sets this platform apart and all. So why it's important how we're sort of reframing of this data workflow. I want to get into the details of that. But just as we sort of understand the problem a little bit more, I mean, one piece of it that we haven't talked about is is race and the home ownership gap. Can you guys talk a little bit about that and how it fits with with what you're trying

to do. Yeah, I mean, the home ownership gap, at least for African Americans is larger now than it was fifty years ago and segregation was legal, which is quite saddening. But it's not just African Americans. And when you look at declines, whether you are a woman, whether you are a minority, whether you're part of the l p G, t Q plus community, it shows that there's a higher level of declines for these communities than there are for

for or white males. So you know, in our perspective, there has to be a lot that needs to be done in terms of resetting, reconfiguring the system to make it more fair and eggable for all. So, if I understand you correctly, you're saying, basically, in the current system, white men have an easier time getting a mortgage than anybody else. Well, you said it, I'll just agree with it. I think you said it. I think if I understood you're correctly said, yeah, that that is what the data shows.

It's not just my perspect that's what the data shows. Is so, and so, how are you trying to help fix that problem by turning everybody into corn? By turning everybody into corn? I like it what do you mean by that? Through the power of math, right, cryptography specifically, we are able to make everyone look the same and the underwriter just simply understands the fundamental attributes that ought

to drive that approval disapproval decision. Right, in order to help us and also to help our government understand where these biases are coming from, our lenders are required to ask you what your raise, what your sex, even your age, right Like, all of this comes with with this application scenario. But does this information inadvertingly create the bias? Can we make everyone look the same and start to remove or better identify where these issues are sort of coming from.

So you're trying to use technology to blind all the decision makers in the home loan process to race, ethnicity, genre specifically blockchain. There are three big tech ideas behind home lending pal at least three of that we're going to talk about today on the show, and blockchain is

big tech idea Number one. You may have heard of blockchain because it's the key idea behind cryptocurrency, but the idea of blockchain is bigger than just digital money and much more than just a new way to store information on the Internet. Blockchain is a shared immutable ledger that facilitates the process of recording transactions and tracking assets in

a business network. Brian and Stephen want to use blockchain to gather up the information on race and gender that's required by law without showing it to the lenders making the decisions about who gets alone. Our argument or our thesis is that with the leverage of a mutable ledger such as blockchain, you're able to still collect the information that is necessary for the Home Mortgage Disclosure Act or

HUMMED as Stephen was referring to. But then with a smart contract, you don't have to release that information, so after the decision, the approval of decline is made for the consumer. So you have this big idea for what you want to do as a business, which you want to do socially, but how do you make creative use of technology to do the thing you want to do to make it real? You know, we're trying to build something that hasn't been done in the mortgage industry, especially

when talking about artificial intelligence and a virtual assistant. Most people think of that it's just a one way street. You know, we are trying to build this human like interaction where it is able to not only understand, but to respond, and then to leverage those responses and create a road map towards allowing you to achieve your goals, which is probably one of the most creative things that

I've ever done personally. But it also requires you to be willing to accept constructive criticism from the people that are going to be using it up front, and a lot of what we're doing is really trying to find creative ways just to get them involved in that conversation, to say that, hey, you know, we are trying to build this to help you. Right now, there's about twenty one million mortgage ready millennials today that are qualified to

buy alone, even though they're not trying. They just don't know. We're trying to bring greater trust and transparency to this process. Yeah, I guess from my perspective, beyond just simply understanding the technology and what it's able to do, I think it takes the will to go ahead and take on that complexity to try something new. We were child is constantly with why not a simpler solution? Right? But in reality the problem is much more complicated than the simplicity these

forces wanted to bring it into the table. You have to have vision, you have to have a desire to want to make fundamental change. Yeah, new tech built on

the old, broken processes doesn't allow for systemic change. You know, you have to try to find ways to not only just to make it easier for people to connect to lenders, but at the core of what we were trying to build, we really wanted to address the systemic issues in the home buying process, and that required us to try something different basically, and I think that's the most creative thing you can do in an industry that ticularly Stephen mentioned

and wanted us to do is simpler. Yeah. So one of the ideas you guys have is that transparency can help reduce bias. So in what we are, you're using technology to bring more transparency to the home buying process. When we speak of transparency, when we speak of trust, where we're really talking about it is just the natural features of the blockchain. Right. It's transparent because all participants within this framework have access to this decentralized ledger, So

we are all seeing how these pieces are sort of moving. Right, we're playing poker with our cards facing up when we're speaking to trust, right, we're speaking of the mutability of this information, knowing that if an action is taken, it's there on the ledger and we can't just simply remove it. So these features lead to this forceful curing of certain

biases that tends to form within certain systems. Um, we're not saying that we're going to remove all bias, but what we're saying is that we feel very confident that we'll be able to reduce it said nificantly without regulatory reinforcement by the simple nature of this technology stack that we're developing. So was there some moment when you guys had the like light bulb, the high idea that you

could do this. The moment that made me realize that this was doable was when our first group of lenders invested. There was a group called the Mortgage Collaborative. They are a collection of about three hundred five lenders I believe across the country. They represent about the overall originations that happened in the US. When they kind of stepped in and we're like, hey, you know, we're gonna lead your your development before your Series A, We're going to try

to help you there. I think that was the moment for for me and then we had shortly after that. Joining that round was a group called Quo Mutual or CMfg Adventures their discovery fund, and they are the one of the largest collections of credit unions in the industry. So, you know, typically you have an issue where you know, consumers feel like there's a problem that's not truly being solved. But to see that lenders were looking to try to find solutions like ours, I think that was the moment

for me. They said, hey, you know this could be feasible for us, that the people who will actually have to work with you want to help you. Like, that's exactly great, but just tell me how will it work? Like, walk me through. I'm an ordinary person. I want to get alone. I come to home lending Pal. What happens when when you're fully you know, fully up and running. How's it gonna work? Yes, So you will spend about five minutes going through our onboarding process where you're connecting

your online bank accounts, you're authorizing and soft FCO. Cool. There's a credit report basically a credit report. You're here. Most people don't realize so so lenders are utilizing your FICO scores and most of the places online that you're able to go to your showing a vantage score. So that's kind of the first level of disconnect and so we're solving for that first. So you go to that process and then after you signed up, our virtual assistant

keV begins doing his he's analyzing your profile. Uh. He's really a geared towards helping you understand really three or four critical elements. You know, one your likelihood for success or approval to some financial modeling and forecasts and give you a better understanding of when you should begin the process to apply for for a home. So how long will it take you to become a homeowner or to

close on the the home? Three the best loan product for you, and then for the lenders within our ecosystem, they present the best chance of success with them as well. So, so you mentioned a virtual advisor, keV virtual meaning it's not a guy named keV, right, it's it's named after one of my my good friends from college that passed from a rare form of germ sale cancer. He's probably one of the most helpful, friendly people that you've ever met, and it didn't matter who you were, So we really

wanted to encompass his personality into the solution itself. But yes, keV, it becomes a friend pal. You know, so even if you're not ready to buy, he just doesn't pass you off and say hey, I'm not going to help. It really analyzes your ber profile and begins to create a path that you can follow to become a homeowner. We have arrived at big tech idea number two. keV the

Virtual Assistant is built using powerful artificial intelligence tools. The AI takes the potential homebuyers information and runs it through algorithms that tell you things like how likely you are to get alan, and what loan makes the most sense for you, and how long the whole process is likely to take. You can ask keV questions and it will give you answers. But keV is more than your average

responder chatbot. It speaks conversationally, It knows who you are, understands your needs, and helps beyond just providing a frequently asked questions link. Brian says he thinks a lot of people might be more comfortable talking with an AI powered virtual assistant then with a human loan officer at a bank.

I think it really solves a cultural problem there. There are cultural barriers that prevent different segments from becoming homeowners or at least impact they're buying decisions in terms of how they explore homeownership. So the first part is to try to use this virtual assistant just to make them feel comfortable getting into the process of what homeownership could

look like. And then from there it is about preparing them, getting them better qualify so that once they are ready to say, hey, I want to come home owner, I found the house that I love, allowing that transaction, that process to be a lot smoother and easier through the use of blockchain. Basically, so when you say cultural, I mean does that include in part race and ethnicity, people who have traditionally been excluded from the banking sector from housing.

Is the dream that sort of AI can help people who have been excluded become more included. Yes, most white people have resources. They have other friends and family who have gone through this process successfully multiple times as opposed to just the one time. Within our communities, is difficult just to find the one person that you can discuss this process with, and most of the time that one person has gone through a negative experience in that right.

Brian's parents have experienced difficulty in this instry. My parents have experienced difficulty in this process too. Isn't until you get to our generation where you have family members that have gone through this process multiple times and have been successful. So when we speak to keV being culturally relevant, it's because keV is there to provide you accurate support that

historically hasn't been available to these marginalized groups. Stephen, you mentioned your own families, your and Brian's famili's experience with getting home loans with the banking system. Do you guys mind us talking about that specifically? What have been in

your family's experiences with getting loan? Yeah? Yeah, I mean back in the subprime mortgage crisis, and you know, my mom nearly lost her dream home that I bought for those primarily because we were in an arm even though we should have been an av A loan because she has a military veteran and an armed an adjustable rate loans that was way worse than the mortgage. It was

way worse. I mean, you know, it started out better just because you pay less, but once that interest rate flips, it becomes way worse if you're not prepared for it. And I think you know, again, when when we talk about these cultural factors, there's really five that you deal with. There's there's cultural itself. So things like the subprime mortage crisis where African Americans are hurt the most coming out of that, you have red lining, reverse rate lining, etcetera.

Red Lining is basically the history of lenders not making loans to people in predominantly black neighborhoods. Essential exactly, we're picking in which areas they will lend to specific groups. Yes, and those areas were predominantly white historically hamper dom wa why Yes. So you have those elements. You have the economic elements where there's this concept of its just being

unattainable for us. You have the psychological elements of being misunderstood thinking that the only way I can buy a home is having down to put down towards of down payment, and that's just not true. So our ultimate objective is just really to make that more attainable for for everyone.

And it's really for all load of monitor income borrowers these days, just because with rates increasing, with the supply shortages that we have, you know, homeownership is really going to become a lot more difficult for a lot of people, regardless of their age, sex, and race. So you have this industry that suffers from a lack of transparency, from historical bias in terms of race and gender. You start this technology driven company to try and fix those things.

As you're building the company, how do you come to work with IBM um our need for data protection and security? So you're talking about digitizing documents, digitizing information to allow

greater access to underserved underrepresented groups. And IBM had their hyper Protect Accelerator which was entirely focused on that, taking small startups like ours and allowing them to basically run the palace that we ran without having to worry about people's information getting stolen in essence, and then Steve and I were just very aggressive in terms of just reaching out to different vps, different executives at IBM, kind of saying, you know, here's what we want to do, here's what

we need, will you help us? And being in an industry that is so regulated, it helped us really get to that door, just because you know, every bank has a vendor on boarding process that requires a very high level of data security to even work with them. In in in essence, here's the third big tech idea in the home Lending Pal story protecting data in the cloud. Think about the problem this one is solving. Brian and Stephen have this little startup. They need to collect supersensitive data

from people. Everything you have to show the bank when you want to get a mortgage, This data has to be secure. IBMS hyper Protect Accelerator enables small businesses to store sensitive data in the cloud and keep that data secure. Brian says, it lets home lending Pal do something they would never do on their own. From a technical perspective, you have different compliance checks that you have to meet

to work with banking institutions or financial institutions. So it allows a small startup like home Lending Pal to still be able to meet those checks and balances to bring an innovative solution to the table for a financial institution, where more than likely as a startup, you're not going to have the ability to do that on your own, just because it is so expensive to either have internal servers or to try to do it on your own

as well. So so people have to trust you to use home landing Piller right Like, I'm giving you everything, how do you can into me? How do you convince customers that you're going to keep their data safe absolutely. Um. Part of it is doing stuff like this where we're acknowledging and making the consumers aware of our relationship with IBM and how IBM is handling our storage of the

data and the sensitive data itself. Technically, the IBM description of it is their confidential computing services or cloud services, and it's basically saying that even though the information is stored in the cloud, IBM is going to do a lot to help Home Lending Pal protect this sensitive data. Part of it is being able to show IBM s logo on our website. You'll you'll be surprised how much logo recognition helps people understand that this is a legit business,

a legit company, if you will. And then there's also stuff like you know, people seeing the address of the business, contact information for the business, like all this stuff factors into why people will be willing to give us their data. But a lot of that is very contingental, just people seeing the IBM logo and saying that, hey, you know, we can if we don't trust Home Lending Path, we definitely trust IBM with this expect of the business. So what is the sort of story of of working with

IBM on this. I mean, did you just figure out that they had the thing you need or did they sort of work with you to to build the thing you need? We told them what we wanted. I think there's a certain special relationship that we have with IBM. As I mentioned, Steve and I are are very aggressive of internally and externally in terms of getting things change in this industry, especially when talk about systemic change, and sometimes that requires you to make very big ask, you know,

swing for the fences and see what happens. And as we found out more, as we we hired better talent, as we understood more of what we were trying to do, it made it a lot easier for us to really

share this vision with IBM. And then now they're able to recommend products to say, we see you're trying to do it this way, but maybe you want to use our internal product and do it with this instead, And so that makes it a lot easier for us to try to bring artificial intelligence and blockchain to an industry that hasn't historically accepted new technology that well. So where are you in your journey as a company. I know you're still sort of working on it. What can customers

do now with your product? They can get recommendations right now. We're fully licensed Colorado, Florida and in North Carolina, so right now, customers from those days can expect to be connected with the lender with full guidance as to what exactly they're getting into and what pricing expectations they ought to be presented with. Have you heard back? I mean, I know that this is kind of a weird question, given that the whole point is that people can be anonymized,

but are you able to talk to your customers? Have Have any of your customers told you about how it's helped them. Surprisingly, a lot of our customers will reach out to us and give us use cases we've had

local TV interviews, what they've interviewed them. Without those success stores will have customers that will reach out to us what challenges that they're having and hoping that we can help them through those, even if we have to manually connect the borrower to a lender and a state that we don't operate, and we're more than happy to do that.

In exchange for that, they're basically helping us build out this new process, and so that's kind of the beauty of the system is that you know, customers are coming in at all stages of the buying cycle. You have some that are still renting at that day dreaming phase where they're really trying to understand, you know, is home

ownership a feasible option for me? And you have some that are you know, trying to test out new features like optimal character recognition software where they're able to upload documents and see how those documents transferred to lenders. So I really think that is the beauty about what we're building is that the people have helped us build it so far. Are there any particular stories you've heard from

customers that have stayed with you? Um? Honestly, I think the one that's most relevant to me, that sticks closest to my heart is my mom. You know, she was looking to try to buy another house. We were able to get her approved for a little bit over six D fifty thousand, which was about fifty thousand more than what she had heard from anyone else in the area. Uh, So, you know, we've really been excited, at least I had

really been excited about that one. That's great, you know, to do better than your mom, right, that's the whole reason why I built the system, so you know, So that one really sticks closest to me is because, uh, we've asked some users that have gone through the entire process and have helped us go from our initial phase and we've really been launching in phases where at first it was more show just showing the affordability amount, like you know, what was the amount of home that you

could afford. Now, as Steven mentioned, we're getting into this much more interactive, uh, conversational dialogue where consumers are not only showing kind of what they want to buy, but also getting into kind of what their feelings are, what is what are their sentiments that they're looking for in a potential relationship with they lender. Uh. So we're really excited when consumers come in and they test new features and they say, hey, this is working, great, this isn't working, Uh,

you know what about this? And we think that's really going to lead into our series A raise here in the next couple of months, where we'll go out and raise hopefully eight thiggers or more to really flush out the features that consumers have said they wanted the most. Is really what we're most excited about. What's your dream for home landing Pale If you think whatever, I don't know. Five years in the future, ten years in the future,

where are you. I want to see at least a million people, hopefully on a million minorities, become homeowners by utilizing our product. You know, we operate in an industry that's very lucrative for a lot of people. Having supported IBM will hopefully help us from a business perspective, But I don't want us to lose sight of our social impact goals and the things that we're really set out before, which was to make the process more agguiable for everyone.

You know, I think if we were to be acquired or to to do an initial public offering in five years and we're not doing that, then for me it would not be as as sweet as if it were to ensure that we're actually doing stuff to close the gap for people. Thank you guys so much for your time. I really it was great to talk with you. Pleasure.

Thank you absolutely. Malcolm glave all here to end today's show, I want to talk about someone who we didn't hear from in the interview, but who we heard about, Brian's mom, because her story really is the story of Home Lending Pall. Remember how Brian told us that back in the odds, his mom got that crappy mortgage, the one that left her paying higher interest rates than she should have been paying. That happened to a lot of people, particularly people of color.

It was that story and others like it that really inspired Brian to team up with Stephen to build Home Lending Pall. They wanted to fix a home lending system that had been opaque and unfair basically forever. Most people applying for mortgages aren't thinking about the technology that's behind the scenes. We all just want a good mortgage with

fair terms. And because Brian and Stephen made creative use of IBM technology using AI, blockchain and cloud to rethink the home loan process, that is now possible for all of us. On the next episode of Smart Talks with IBM, as AI becomes more widespread, how do we ensure that it is built and deployed responsibly, We talked with Pedra Bonadira's trustworthy AI practice leader within IBM Consulting. Smart Talks with IBM is produced by Molly Sosha, Alexandra Garraton, Royston

Preserve and Edith Rousselo with Jacob Goldstein. We're edited by Jan Guerra. Our engineers are Jason Gambrel, Sarah Brogare and Ben Holliday. Theme song by Gramoscope. Special thanks to Colly Migliari and Kelly Kathy Callaghan and the eight Bar and IBM teams, as well as the Pushkin marketing team. Smart Talks with IBM is a production of Pushkin Industries and I Heart Media. To find more Pushkin podcasts, listen on the i Heart Radio app, Apple Podcasts, or wherever you

listen to podcasts. I'm Malcolm Gladwell. This is a paid advertisement from IBM.