WEBVTT - Home Lending Pal: Disrupting the Mortgage Industry with Cloud and AI

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<v Speaker 1>Welcome to tex Stuff, a production from I Heart Radio.

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<v Speaker 1>This season of Smart Talks with IBM is all about

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<v Speaker 1>new creators, the developers, data scientists, c t o s

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<v Speaker 1>and other visionaries creatively applying technology in business to drive change.

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<v Speaker 1>They use their knowledge and creativity to develop better ways

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<v Speaker 1>of working, no matter the industry. Join hosts from your

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<v Speaker 1>favorite Pushkin Industries podcasts as they use their expertise to

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<v Speaker 1>deepen these conversations, and of course Malcolm Gladwell will guide

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<v Speaker 1>you through the season as your host and provide his

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<v Speaker 1>thoughts and analysis along the way. Look out for new

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<v Speaker 1>episodes of Smart Talks with IBM on the I Heart

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<v Speaker 1>Radio app, Apple Podcasts, or wherever you get your podcasts,

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<v Speaker 1>and learn more at IBM dot com slash smart talks. Hello, Hello,

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<v Speaker 1>welcome to a new season of Smart Talks with IBM,

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<v Speaker 1>a podcast from pushed In Industries, I Heart Radio and IBM.

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<v Speaker 1>I'm Malcolm Gladwell. This season we're talking to new creators,

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<v Speaker 1>the developers, data scientists, c t o s and other

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<v Speaker 1>visionaries who are creatively applying technology in business to drive change.

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<v Speaker 1>Channeling their knowledge and expertise, they're developing more creative and

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<v Speaker 1>effective solutions no matter the industry. Our guest today are

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<v Speaker 1>Brian Young and Stephen Better, co founders of home lending Pal.

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<v Speaker 1>Home lending Pal is a member of the IBM hyper

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<v Speaker 1>Protect Accelerator, an investment readiness and technical mentorship program that

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<v Speaker 1>supports impact focused startups leveraging highly sensitive data. Their story

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<v Speaker 1>is a perfect place to start our season. They recognized

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<v Speaker 1>a profound problem, the horrible process of getting a home loan,

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<v Speaker 1>especially if you're part of an unders serve community, a

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<v Speaker 1>process that, as you'll hear, is not only confusing and complex,

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<v Speaker 1>but often deeply unfair. So Brian and Stephen teamed up

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<v Speaker 1>to use technology to attack that problem in a bunch

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<v Speaker 1>of creative ways. You'll hear how they're tapping into blockchain

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<v Speaker 1>to make the home loan process more transparent and fair,

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<v Speaker 1>using AI to help people learn how to qualify for

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<v Speaker 1>a loan, and relying on IBM technology to store consumers

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<v Speaker 1>most sensitive information safely in the cloud. Brian and Stephen

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<v Speaker 1>talked with Jacob Goldstein, host of the pushkin podcast What's

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<v Speaker 1>Your Problem. Jacob has covered technology and business for over

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<v Speaker 1>a decade, first at The Wall Street Journal, then at MPR.

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<v Speaker 1>Now let's get into the interview. Let's start this like

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<v Speaker 1>a brom car How did you meet each other and

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<v Speaker 1>decided to start a company together. Steven was supposed to

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<v Speaker 1>come to a bachelor party in Miami and didn't show up,

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<v Speaker 1>and it broke my heart. There's more to the story

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<v Speaker 1>than just simply that one of my old employees introduced us.

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<v Speaker 1>I've just left Marcato. They've been acquired for one point

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<v Speaker 1>for a billion, and I am, you know, living the

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<v Speaker 1>Miami lifestyle. You know, I have a condo on the

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<v Speaker 1>water and all the nice things to go with. A

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<v Speaker 1>guy named Michael Ramsey had asked me, you know what,

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<v Speaker 1>I helped him do mortgage lead generation and I was like,

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<v Speaker 1>you know, sure, I'm not doing anything else? Why not?

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<v Speaker 1>And I meet Steve that he was in North Carolina.

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<v Speaker 1>I left a pretty fruitful career in banking. I was

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<v Speaker 1>an underwriter. Underwriting loans mean it's basically deciding who should

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<v Speaker 1>get a loan and at what interest rate? Right, Absolutely

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<v Speaker 1>due diligence, right, which is understanding whether or not this

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<v Speaker 1>particular individual has the worldwithal to afford the mortgage. Also

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<v Speaker 1>the credit risk individual presents. But there was this disconnect

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<v Speaker 1>in that process where you have hidden action taking place

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<v Speaker 1>on one side of the transaction while you have another

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<v Speaker 1>side of the transaction that that tends to hide information.

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<v Speaker 1>And just to be clear, it's the borrower who hides

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<v Speaker 1>information and the bank that hides the action the lender

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<v Speaker 1>in most cases, but this is usually both sides of

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<v Speaker 1>the negotia. Everybody's hiding stuff from everybody else. There's a

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<v Speaker 1>absolutely and it's like sort of inadvertent as well too,

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<v Speaker 1>right in that process, and things fall through the cracks,

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<v Speaker 1>and you know, falling through the cracks means weaks without

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<v Speaker 1>notification from a bar's perspective as to whether or not

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<v Speaker 1>you know this deal is moving forward. Okay, So so

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<v Speaker 1>the problem is a lack of information on both sides,

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<v Speaker 1>and that winds up leading to bad outcomes. It winds

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<v Speaker 1>up leading to long delays that are frustrating or scary

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<v Speaker 1>for the for the borrower, yes, who are of consumers

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<v Speaker 1>just don't have anywhere to go if you go online,

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<v Speaker 1>everything is too broad engineering, especially if you know you're

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<v Speaker 1>not ready to buy at that moment. Uh. If you

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<v Speaker 1>talk to a lender or relatory, if you're not ready

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<v Speaker 1>to buy at that moment, there they'll help you, but

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<v Speaker 1>it's not the same level of help. But you're not

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<v Speaker 1>gonna get that same level of support over months because

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<v Speaker 1>you know, buying houses and like buying a piece of

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<v Speaker 1>candy online, And so we really looked at, Okay, well,

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<v Speaker 1>how can we give people this safe environment to go

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<v Speaker 1>explore and understand when homeownership could look like for them

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<v Speaker 1>based on their personal information. And that's kind of when

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<v Speaker 1>I reach back out to Stephen around August of two

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<v Speaker 1>thousand seventeen and said, hey, you know, we need to

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<v Speaker 1>do this together. You understand the back inside from a

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<v Speaker 1>lender underwritish perspective, and I understand the plight of the consumers,

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<v Speaker 1>and if we come together, this could be something that

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<v Speaker 1>could be really unique. A capitalist solution to a social

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<v Speaker 1>challenge is probably the best way to put it. So Stephen,

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<v Speaker 1>you're sort of coming from the banking side, and Brian

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<v Speaker 1>you're sort of coming from the tech side. Absolutely, what

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<v Speaker 1>exactly is the problem that you've got us are trying

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<v Speaker 1>to solve when you start this company, and its simple

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<v Speaker 1>assessence is data democratization, the ability to take complex information

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<v Speaker 1>and simplify so that someone that isn't an expert like

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<v Speaker 1>Stephen can understand what's going on, and in this case specifically,

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<v Speaker 1>what is the data that you're trying to democratize underwriting data,

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<v Speaker 1>so the decision or the data that is utilized to

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<v Speaker 1>determine whether or not you are approved or declined for

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<v Speaker 1>a home loan. So right now, if I go apply

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<v Speaker 1>for a loan, they approve me or they decline me.

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<v Speaker 1>But do I know why not? Really? I mean, you

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<v Speaker 1>get a letter of an adverse letter, but it's still

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<v Speaker 1>very broad Engineeric, it doesn't really tell you what to

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<v Speaker 1>focus on next, but you do have a very high

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<v Speaker 1>level sense of why your decline. Yeah, there's no true

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<v Speaker 1>guidance from that point of rejection, right there's no fundamental

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<v Speaker 1>understanding as to what could I have done better? And

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<v Speaker 1>that's really what sets this platform apart and all. So

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<v Speaker 1>why it's important how we're sort of reframing of this

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<v Speaker 1>data workflow. I want to get into the details of that.

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<v Speaker 1>But just as we sort of understand the problem a

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<v Speaker 1>little bit more, I mean, one piece of it that

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<v Speaker 1>we haven't talked about is is race and the home

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<v Speaker 1>ownership gap. Can you guys talk a little bit about

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<v Speaker 1>that and how it fits with with what you're trying

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<v Speaker 1>to do. Yeah, I mean, the home ownership gap, at

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<v Speaker 1>least for African Americans is larger now than it was

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<v Speaker 1>fifty years ago and segregation was legal, which is quite saddening.

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<v Speaker 1>But it's not just African Americans. And when you look

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<v Speaker 1>at declines, whether you are a woman, whether you are

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<v Speaker 1>a minority, whether you're part of the l p G,

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<v Speaker 1>t Q plus community, it shows that there's a higher

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<v Speaker 1>level of declines for these communities than there are for

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<v Speaker 1>for or white males. So you know, in our perspective,

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<v Speaker 1>there has to be a lot that needs to be

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<v Speaker 1>done in terms of resetting, reconfiguring the system to make

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<v Speaker 1>it more fair and eggable for all. So, if I

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<v Speaker 1>understand you correctly, you're saying, basically, in the current system,

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<v Speaker 1>white men have an easier time getting a mortgage than

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<v Speaker 1>anybody else. Well, you said it, I'll just agree with it.

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<v Speaker 1>I think you said it. I think if I understood

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<v Speaker 1>you're correctly said, yeah, that that is what the data shows.

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<v Speaker 1>It's not just my perspect that's what the data shows.

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<v Speaker 1>Is so, and so, how are you trying to help

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<v Speaker 1>fix that problem by turning everybody into corn? By turning

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<v Speaker 1>everybody into corn? I like it what do you mean

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<v Speaker 1>by that? Through the power of math, right, cryptography specifically,

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<v Speaker 1>we are able to make everyone look the same and

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<v Speaker 1>the underwriter just simply understands the fundamental attributes that ought

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<v Speaker 1>to drive that approval disapproval decision. Right, in order to

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<v Speaker 1>help us and also to help our government understand where

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<v Speaker 1>these biases are coming from, our lenders are required to

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<v Speaker 1>ask you what your raise, what your sex, even your age,

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<v Speaker 1>right Like, all of this comes with with this application scenario.

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<v Speaker 1>But does this information inadvertingly create the bias? Can we

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<v Speaker 1>make everyone look the same and start to remove or

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<v Speaker 1>better identify where these issues are sort of coming from.

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<v Speaker 1>So you're trying to use technology to blind all the

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<v Speaker 1>decision makers in the home loan process to race, ethnicity,

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<v Speaker 1>genre specifically blockchain. There are three big tech ideas behind

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<v Speaker 1>home lending pal at least three of that we're going

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<v Speaker 1>to talk about today on the show, and blockchain is

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<v Speaker 1>big tech idea Number one. You may have heard of

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<v Speaker 1>blockchain because it's the key idea behind cryptocurrency, but the

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<v Speaker 1>idea of blockchain is bigger than just digital money and

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<v Speaker 1>much more than just a new way to store information

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<v Speaker 1>on the Internet. Blockchain is a shared immutable ledger that

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<v Speaker 1>facilitates the process of recording transactions and tracking assets in

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<v Speaker 1>a business network. Brian and Stephen want to use blockchain

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<v Speaker 1>to gather up the information on race and gender that's

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<v Speaker 1>required by law without showing it to the lenders making

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<v Speaker 1>the decisions about who gets alone. Our argument or our

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<v Speaker 1>thesis is that with the leverage of a mutable ledger

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<v Speaker 1>such as blockchain, you're able to still collect the information

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<v Speaker 1>that is necessary for the Home Mortgage Disclosure Act or

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<v Speaker 1>HUMMED as Stephen was referring to. But then with a

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<v Speaker 1>smart contract, you don't have to release that information, so

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<v Speaker 1>after the decision, the approval of decline is made for

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<v Speaker 1>the consumer. So you have this big idea for what

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<v Speaker 1>you want to do as a business, which you want

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<v Speaker 1>to do socially, but how do you make creative use

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<v Speaker 1>of technology to do the thing you want to do

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<v Speaker 1>to make it real? You know, we're trying to build

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<v Speaker 1>something that hasn't been done in the mortgage industry, especially

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<v Speaker 1>when talking about artificial intelligence and a virtual assistant. Most

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<v Speaker 1>people think of that it's just a one way street.

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<v Speaker 1>You know, we are trying to build this human like

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<v Speaker 1>interaction where it is able to not only understand, but

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<v Speaker 1>to respond, and then to leverage those responses and create

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<v Speaker 1>a road map towards allowing you to achieve your goals,

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<v Speaker 1>which is probably one of the most creative things that

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<v Speaker 1>I've ever done personally. But it also requires you to

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<v Speaker 1>be willing to accept constructive criticism from the people that

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<v Speaker 1>are going to be using it up front, and a

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<v Speaker 1>lot of what we're doing is really trying to find

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<v Speaker 1>creative ways just to get them involved in that conversation,

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<v Speaker 1>to say that, hey, you know, we are trying to

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<v Speaker 1>build this to help you. Right now, there's about twenty

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<v Speaker 1>one million mortgage ready millennials today that are qualified to

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<v Speaker 1>buy alone, even though they're not trying. They just don't know.

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<v Speaker 1>We're trying to bring greater trust and transparency to this process. Yeah,

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<v Speaker 1>I guess from my perspective, beyond just simply understanding the

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<v Speaker 1>technology and what it's able to do, I think it

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<v Speaker 1>takes the will to go ahead and take on that

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<v Speaker 1>complexity to try something new. We were child is constantly

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<v Speaker 1>with why not a simpler solution? Right? But in reality

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<v Speaker 1>the problem is much more complicated than the simplicity these

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<v Speaker 1>forces wanted to bring it into the table. You have

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<v Speaker 1>to have vision, you have to have a desire to

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<v Speaker 1>want to make fundamental change. Yeah, new tech built on

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<v Speaker 1>the old, broken processes doesn't allow for systemic change. You know,

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<v Speaker 1>you have to try to find ways to not only

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<v Speaker 1>just to make it easier for people to connect to lenders,

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<v Speaker 1>but at the core of what we were trying to build,

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<v Speaker 1>we really wanted to address the systemic issues in the

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<v Speaker 1>home buying process, and that required us to try something

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<v Speaker 1>different basically, and I think that's the most creative thing

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<v Speaker 1>you can do in an industry that ticularly Stephen mentioned

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<v Speaker 1>and wanted us to do is simpler. Yeah. So one

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<v Speaker 1>of the ideas you guys have is that transparency can

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<v Speaker 1>help reduce bias. So in what we are, you're using

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<v Speaker 1>technology to bring more transparency to the home buying process.

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<v Speaker 1>When we speak of transparency, when we speak of trust,

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<v Speaker 1>where we're really talking about it is just the natural

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<v Speaker 1>features of the blockchain. Right. It's transparent because all participants

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<v Speaker 1>within this framework have access to this decentralized ledger, So

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<v Speaker 1>we are all seeing how these pieces are sort of moving. Right,

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<v Speaker 1>we're playing poker with our cards facing up when we're

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<v Speaker 1>speaking to trust, right, we're speaking of the mutability of

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<v Speaker 1>this information, knowing that if an action is taken, it's

0:13:36.120 --> 0:13:39.080
<v Speaker 1>there on the ledger and we can't just simply remove it.

0:13:39.679 --> 0:13:47.000
<v Speaker 1>So these features lead to this forceful curing of certain

0:13:47.040 --> 0:13:51.800
<v Speaker 1>biases that tends to form within certain systems. Um, we're

0:13:51.800 --> 0:13:55.360
<v Speaker 1>not saying that we're going to remove all bias, but

0:13:55.440 --> 0:13:58.080
<v Speaker 1>what we're saying is that we feel very confident that

0:13:58.120 --> 0:14:02.960
<v Speaker 1>we'll be able to reduce it said nificantly without regulatory

0:14:03.080 --> 0:14:07.280
<v Speaker 1>reinforcement by the simple nature of this technology stack that

0:14:07.320 --> 0:14:11.880
<v Speaker 1>we're developing. So was there some moment when you guys

0:14:11.960 --> 0:14:15.520
<v Speaker 1>had the like light bulb, the high idea that you

0:14:15.559 --> 0:14:19.200
<v Speaker 1>could do this. The moment that made me realize that

0:14:19.280 --> 0:14:22.120
<v Speaker 1>this was doable was when our first group of lenders invested.

0:14:22.160 --> 0:14:24.800
<v Speaker 1>There was a group called the Mortgage Collaborative. They are

0:14:24.840 --> 0:14:28.680
<v Speaker 1>a collection of about three hundred five lenders I believe

0:14:28.720 --> 0:14:32.840
<v Speaker 1>across the country. They represent about the overall originations that

0:14:32.880 --> 0:14:35.520
<v Speaker 1>happened in the US. When they kind of stepped in

0:14:35.600 --> 0:14:37.480
<v Speaker 1>and we're like, hey, you know, we're gonna lead your

0:14:38.200 --> 0:14:40.400
<v Speaker 1>your development before your Series A, We're going to try

0:14:40.440 --> 0:14:41.880
<v Speaker 1>to help you there. I think that was the moment

0:14:41.960 --> 0:14:44.560
<v Speaker 1>for for me and then we had shortly after that.

0:14:44.680 --> 0:14:46.880
<v Speaker 1>Joining that round was a group called Quo Mutual or

0:14:46.880 --> 0:14:50.320
<v Speaker 1>CMfg Adventures their discovery fund, and they are the one

0:14:50.360 --> 0:14:52.960
<v Speaker 1>of the largest collections of credit unions in the industry. So,

0:14:53.280 --> 0:14:55.640
<v Speaker 1>you know, typically you have an issue where you know,

0:14:55.720 --> 0:14:58.640
<v Speaker 1>consumers feel like there's a problem that's not truly being solved.

0:14:58.640 --> 0:15:00.840
<v Speaker 1>But to see that lenders were looking to try to

0:15:00.880 --> 0:15:03.320
<v Speaker 1>find solutions like ours, I think that was the moment

0:15:03.360 --> 0:15:05.400
<v Speaker 1>for me. They said, hey, you know this could be

0:15:05.440 --> 0:15:07.840
<v Speaker 1>feasible for us, that the people who will actually have

0:15:08.000 --> 0:15:11.400
<v Speaker 1>to work with you want to help you. Like, that's

0:15:11.440 --> 0:15:17.440
<v Speaker 1>exactly great, but just tell me how will it work? Like,

0:15:17.520 --> 0:15:20.440
<v Speaker 1>walk me through. I'm an ordinary person. I want to

0:15:20.480 --> 0:15:24.240
<v Speaker 1>get alone. I come to home lending Pal. What happens

0:15:24.560 --> 0:15:26.920
<v Speaker 1>when when you're fully you know, fully up and running.

0:15:26.920 --> 0:15:30.600
<v Speaker 1>How's it gonna work? Yes, So you will spend about

0:15:30.640 --> 0:15:33.720
<v Speaker 1>five minutes going through our onboarding process where you're connecting

0:15:33.720 --> 0:15:36.960
<v Speaker 1>your online bank accounts, you're authorizing and soft FCO. Cool.

0:15:37.480 --> 0:15:40.640
<v Speaker 1>There's a credit report basically a credit report. You're here.

0:15:40.680 --> 0:15:43.200
<v Speaker 1>Most people don't realize so so lenders are utilizing your

0:15:43.200 --> 0:15:46.040
<v Speaker 1>FICO scores and most of the places online that you're

0:15:46.080 --> 0:15:47.960
<v Speaker 1>able to go to your showing a vantage score. So

0:15:47.960 --> 0:15:50.160
<v Speaker 1>that's kind of the first level of disconnect and so

0:15:50.200 --> 0:15:53.240
<v Speaker 1>we're solving for that first. So you go to that

0:15:53.320 --> 0:15:58.000
<v Speaker 1>process and then after you signed up, our virtual assistant

0:15:58.120 --> 0:16:02.960
<v Speaker 1>keV begins doing his he's analyzing your profile. Uh. He's

0:16:03.000 --> 0:16:06.520
<v Speaker 1>really a geared towards helping you understand really three or

0:16:06.560 --> 0:16:10.400
<v Speaker 1>four critical elements. You know, one your likelihood for success

0:16:10.480 --> 0:16:14.480
<v Speaker 1>or approval to some financial modeling and forecasts and give

0:16:14.520 --> 0:16:17.280
<v Speaker 1>you a better understanding of when you should begin the

0:16:17.360 --> 0:16:19.480
<v Speaker 1>process to apply for for a home. So how long

0:16:19.480 --> 0:16:21.920
<v Speaker 1>will it take you to become a homeowner or to

0:16:21.920 --> 0:16:25.280
<v Speaker 1>close on the the home? Three the best loan product for you,

0:16:25.960 --> 0:16:28.320
<v Speaker 1>and then for the lenders within our ecosystem, they present

0:16:28.400 --> 0:16:31.120
<v Speaker 1>the best chance of success with them as well. So,

0:16:31.320 --> 0:16:37.160
<v Speaker 1>so you mentioned a virtual advisor, keV virtual meaning it's

0:16:37.200 --> 0:16:41.720
<v Speaker 1>not a guy named keV, right, it's it's named after

0:16:41.760 --> 0:16:44.320
<v Speaker 1>one of my my good friends from college that passed

0:16:44.320 --> 0:16:47.080
<v Speaker 1>from a rare form of germ sale cancer. He's probably

0:16:47.080 --> 0:16:49.240
<v Speaker 1>one of the most helpful, friendly people that you've ever met,

0:16:49.240 --> 0:16:51.720
<v Speaker 1>and it didn't matter who you were, So we really

0:16:51.720 --> 0:16:57.560
<v Speaker 1>wanted to encompass his personality into the solution itself. But yes, keV,

0:16:59.040 --> 0:17:01.440
<v Speaker 1>it becomes a friend pal. You know, so even if

0:17:01.440 --> 0:17:04.000
<v Speaker 1>you're not ready to buy, he just doesn't pass you

0:17:04.040 --> 0:17:06.120
<v Speaker 1>off and say hey, I'm not going to help. It

0:17:06.160 --> 0:17:09.040
<v Speaker 1>really analyzes your ber profile and begins to create a

0:17:09.080 --> 0:17:13.720
<v Speaker 1>path that you can follow to become a homeowner. We

0:17:13.760 --> 0:17:17.720
<v Speaker 1>have arrived at big tech idea number two. keV the

0:17:17.840 --> 0:17:23.560
<v Speaker 1>Virtual Assistant is built using powerful artificial intelligence tools. The

0:17:23.720 --> 0:17:27.960
<v Speaker 1>AI takes the potential homebuyers information and runs it through

0:17:28.000 --> 0:17:31.280
<v Speaker 1>algorithms that tell you things like how likely you are

0:17:31.320 --> 0:17:33.960
<v Speaker 1>to get alan, and what loan makes the most sense

0:17:34.000 --> 0:17:36.840
<v Speaker 1>for you, and how long the whole process is likely

0:17:36.880 --> 0:17:40.320
<v Speaker 1>to take. You can ask keV questions and it will

0:17:40.359 --> 0:17:43.520
<v Speaker 1>give you answers. But keV is more than your average

0:17:43.560 --> 0:17:48.400
<v Speaker 1>responder chatbot. It speaks conversationally, It knows who you are,

0:17:48.680 --> 0:17:52.800
<v Speaker 1>understands your needs, and helps beyond just providing a frequently

0:17:52.840 --> 0:17:55.960
<v Speaker 1>asked questions link. Brian says he thinks a lot of

0:17:55.960 --> 0:17:58.880
<v Speaker 1>people might be more comfortable talking with an AI powered

0:17:58.960 --> 0:18:02.639
<v Speaker 1>virtual assistant then with a human loan officer at a bank.

0:18:03.680 --> 0:18:06.439
<v Speaker 1>I think it really solves a cultural problem there. There

0:18:06.440 --> 0:18:10.639
<v Speaker 1>are cultural barriers that prevent different segments from becoming homeowners

0:18:10.720 --> 0:18:13.560
<v Speaker 1>or at least impact they're buying decisions in terms of

0:18:13.560 --> 0:18:16.640
<v Speaker 1>how they explore homeownership. So the first part is to

0:18:16.680 --> 0:18:18.800
<v Speaker 1>try to use this virtual assistant just to make them

0:18:18.800 --> 0:18:22.400
<v Speaker 1>feel comfortable getting into the process of what homeownership could

0:18:22.440 --> 0:18:25.400
<v Speaker 1>look like. And then from there it is about preparing them,

0:18:25.400 --> 0:18:28.640
<v Speaker 1>getting them better qualify so that once they are ready

0:18:28.680 --> 0:18:30.959
<v Speaker 1>to say, hey, I want to come home owner, I

0:18:30.960 --> 0:18:34.800
<v Speaker 1>found the house that I love, allowing that transaction, that

0:18:35.200 --> 0:18:37.840
<v Speaker 1>process to be a lot smoother and easier through the

0:18:37.880 --> 0:18:41.040
<v Speaker 1>use of blockchain. Basically, so when you say cultural, I

0:18:41.040 --> 0:18:45.520
<v Speaker 1>mean does that include in part race and ethnicity, people

0:18:45.560 --> 0:18:50.720
<v Speaker 1>who have traditionally been excluded from the banking sector from housing.

0:18:51.320 --> 0:18:54.040
<v Speaker 1>Is the dream that sort of AI can help people

0:18:54.080 --> 0:18:58.360
<v Speaker 1>who have been excluded become more included. Yes, most white

0:18:58.359 --> 0:19:02.520
<v Speaker 1>people have resources. They have other friends and family who

0:19:02.520 --> 0:19:06.359
<v Speaker 1>have gone through this process successfully multiple times as opposed

0:19:06.359 --> 0:19:09.560
<v Speaker 1>to just the one time. Within our communities, is difficult

0:19:09.600 --> 0:19:12.840
<v Speaker 1>just to find the one person that you can discuss

0:19:12.920 --> 0:19:16.040
<v Speaker 1>this process with, and most of the time that one

0:19:16.080 --> 0:19:19.720
<v Speaker 1>person has gone through a negative experience in that right.

0:19:20.000 --> 0:19:23.159
<v Speaker 1>Brian's parents have experienced difficulty in this instry. My parents

0:19:23.400 --> 0:19:27.200
<v Speaker 1>have experienced difficulty in this process too. Isn't until you

0:19:27.240 --> 0:19:30.879
<v Speaker 1>get to our generation where you have family members that

0:19:30.920 --> 0:19:34.800
<v Speaker 1>have gone through this process multiple times and have been successful.

0:19:35.200 --> 0:19:39.760
<v Speaker 1>So when we speak to keV being culturally relevant, it's

0:19:39.880 --> 0:19:44.880
<v Speaker 1>because keV is there to provide you accurate support that

0:19:45.080 --> 0:19:49.720
<v Speaker 1>historically hasn't been available to these marginalized groups. Stephen, you

0:19:49.840 --> 0:19:54.440
<v Speaker 1>mentioned your own families, your and Brian's famili's experience with

0:19:54.960 --> 0:19:57.400
<v Speaker 1>getting home loans with the banking system. Do you guys

0:19:57.400 --> 0:20:00.080
<v Speaker 1>mind us talking about that specifically? What have been in

0:20:00.119 --> 0:20:06.240
<v Speaker 1>your family's experiences with getting loan? Yeah? Yeah, I mean

0:20:06.600 --> 0:20:09.159
<v Speaker 1>back in the subprime mortgage crisis, and you know, my

0:20:09.200 --> 0:20:11.119
<v Speaker 1>mom nearly lost her dream home that I bought for

0:20:11.800 --> 0:20:13.960
<v Speaker 1>those primarily because we were in an arm even though

0:20:13.960 --> 0:20:15.680
<v Speaker 1>we should have been an av A loan because she

0:20:15.720 --> 0:20:18.719
<v Speaker 1>has a military veteran and an armed an adjustable rate

0:20:18.760 --> 0:20:22.240
<v Speaker 1>loans that was way worse than the mortgage. It was

0:20:22.240 --> 0:20:24.120
<v Speaker 1>way worse. I mean, you know, it started out better

0:20:24.200 --> 0:20:26.560
<v Speaker 1>just because you pay less, but once that interest rate flips,

0:20:26.600 --> 0:20:28.919
<v Speaker 1>it becomes way worse if you're not prepared for it.

0:20:28.960 --> 0:20:31.680
<v Speaker 1>And I think you know, again, when when we talk

0:20:31.720 --> 0:20:34.600
<v Speaker 1>about these cultural factors, there's really five that you deal with.

0:20:34.640 --> 0:20:38.080
<v Speaker 1>There's there's cultural itself. So things like the subprime mortage

0:20:38.119 --> 0:20:41.440
<v Speaker 1>crisis where African Americans are hurt the most coming out

0:20:41.440 --> 0:20:44.440
<v Speaker 1>of that, you have red lining, reverse rate lining, etcetera.

0:20:45.160 --> 0:20:49.000
<v Speaker 1>Red Lining is basically the history of lenders not making

0:20:49.080 --> 0:20:53.280
<v Speaker 1>loans to people in predominantly black neighborhoods. Essential exactly, we're

0:20:53.280 --> 0:20:56.960
<v Speaker 1>picking in which areas they will lend to specific groups. Yes,

0:20:57.000 --> 0:21:00.159
<v Speaker 1>and those areas were predominantly white historically hamper dom wa

0:21:00.200 --> 0:21:03.439
<v Speaker 1>why Yes. So you have those elements. You have the

0:21:03.440 --> 0:21:07.080
<v Speaker 1>economic elements where there's this concept of its just being

0:21:07.240 --> 0:21:11.560
<v Speaker 1>unattainable for us. You have the psychological elements of being

0:21:11.760 --> 0:21:14.080
<v Speaker 1>misunderstood thinking that the only way I can buy a

0:21:14.119 --> 0:21:17.040
<v Speaker 1>home is having down to put down towards of down payment,

0:21:17.440 --> 0:21:21.320
<v Speaker 1>and that's just not true. So our ultimate objective is

0:21:21.359 --> 0:21:24.040
<v Speaker 1>just really to make that more attainable for for everyone.

0:21:24.080 --> 0:21:26.120
<v Speaker 1>And it's really for all load of monitor income borrowers

0:21:26.240 --> 0:21:29.560
<v Speaker 1>these days, just because with rates increasing, with the supply

0:21:29.640 --> 0:21:32.399
<v Speaker 1>shortages that we have, you know, homeownership is really going

0:21:32.400 --> 0:21:35.240
<v Speaker 1>to become a lot more difficult for a lot of people,

0:21:35.280 --> 0:21:38.879
<v Speaker 1>regardless of their age, sex, and race. So you have

0:21:38.960 --> 0:21:41.880
<v Speaker 1>this industry that suffers from a lack of transparency, from

0:21:42.000 --> 0:21:45.200
<v Speaker 1>historical bias in terms of race and gender. You start

0:21:45.280 --> 0:21:47.960
<v Speaker 1>this technology driven company to try and fix those things.

0:21:48.280 --> 0:21:50.000
<v Speaker 1>As you're building the company, how do you come to

0:21:50.040 --> 0:21:55.480
<v Speaker 1>work with IBM um our need for data protection and security?

0:21:55.640 --> 0:22:00.919
<v Speaker 1>So you're talking about digitizing documents, digitizing information to allow

0:22:01.200 --> 0:22:06.400
<v Speaker 1>greater access to underserved underrepresented groups. And IBM had their

0:22:06.480 --> 0:22:10.480
<v Speaker 1>hyper Protect Accelerator which was entirely focused on that, taking

0:22:10.520 --> 0:22:14.560
<v Speaker 1>small startups like ours and allowing them to basically run

0:22:14.600 --> 0:22:17.680
<v Speaker 1>the palace that we ran without having to worry about

0:22:17.680 --> 0:22:21.320
<v Speaker 1>people's information getting stolen in essence, and then Steve and

0:22:21.359 --> 0:22:23.919
<v Speaker 1>I were just very aggressive in terms of just reaching

0:22:23.920 --> 0:22:28.160
<v Speaker 1>out to different vps, different executives at IBM, kind of saying,

0:22:28.200 --> 0:22:29.560
<v Speaker 1>you know, here's what we want to do, here's what

0:22:29.600 --> 0:22:32.959
<v Speaker 1>we need, will you help us? And being in an

0:22:33.000 --> 0:22:36.760
<v Speaker 1>industry that is so regulated, it helped us really get

0:22:36.760 --> 0:22:39.560
<v Speaker 1>to that door, just because you know, every bank has

0:22:39.800 --> 0:22:43.600
<v Speaker 1>a vendor on boarding process that requires a very high

0:22:43.680 --> 0:22:46.480
<v Speaker 1>level of data security to even work with them. In in

0:22:46.400 --> 0:22:52.000
<v Speaker 1>in essence, here's the third big tech idea in the

0:22:52.080 --> 0:22:57.080
<v Speaker 1>home Lending Pal story protecting data in the cloud. Think

0:22:57.080 --> 0:22:59.919
<v Speaker 1>about the problem this one is solving. Brian and Stephen

0:23:00.119 --> 0:23:03.720
<v Speaker 1>have this little startup. They need to collect supersensitive data

0:23:03.800 --> 0:23:06.640
<v Speaker 1>from people. Everything you have to show the bank when

0:23:06.640 --> 0:23:09.800
<v Speaker 1>you want to get a mortgage, This data has to

0:23:09.840 --> 0:23:15.040
<v Speaker 1>be secure. IBMS hyper Protect Accelerator enables small businesses to

0:23:15.119 --> 0:23:18.960
<v Speaker 1>store sensitive data in the cloud and keep that data secure.

0:23:19.800 --> 0:23:22.959
<v Speaker 1>Brian says, it lets home lending Pal do something they

0:23:23.000 --> 0:23:27.280
<v Speaker 1>would never do on their own. From a technical perspective,

0:23:27.320 --> 0:23:29.840
<v Speaker 1>you have different compliance checks that you have to meet

0:23:29.880 --> 0:23:33.320
<v Speaker 1>to work with banking institutions or financial institutions. So it

0:23:33.359 --> 0:23:37.000
<v Speaker 1>allows a small startup like home Lending Pal to still

0:23:37.040 --> 0:23:40.240
<v Speaker 1>be able to meet those checks and balances to bring

0:23:40.320 --> 0:23:44.040
<v Speaker 1>an innovative solution to the table for a financial institution,

0:23:44.119 --> 0:23:46.439
<v Speaker 1>where more than likely as a startup, you're not going

0:23:46.480 --> 0:23:47.960
<v Speaker 1>to have the ability to do that on your own,

0:23:48.000 --> 0:23:51.000
<v Speaker 1>just because it is so expensive to either have internal

0:23:51.040 --> 0:23:52.680
<v Speaker 1>servers or to try to do it on your own

0:23:52.680 --> 0:23:55.639
<v Speaker 1>as well. So so people have to trust you to

0:23:56.000 --> 0:23:58.520
<v Speaker 1>use home landing Piller right Like, I'm giving you everything,

0:23:59.080 --> 0:24:01.280
<v Speaker 1>how do you can into me? How do you convince

0:24:01.320 --> 0:24:06.240
<v Speaker 1>customers that you're going to keep their data safe absolutely. Um.

0:24:06.440 --> 0:24:08.919
<v Speaker 1>Part of it is doing stuff like this where we're

0:24:08.960 --> 0:24:12.040
<v Speaker 1>acknowledging and making the consumers aware of our relationship with

0:24:12.119 --> 0:24:14.879
<v Speaker 1>IBM and how IBM is handling our storage of the

0:24:14.960 --> 0:24:18.960
<v Speaker 1>data and the sensitive data itself. Technically, the IBM description

0:24:19.000 --> 0:24:22.480
<v Speaker 1>of it is their confidential computing services or cloud services,

0:24:22.520 --> 0:24:24.840
<v Speaker 1>and it's basically saying that even though the information is

0:24:24.880 --> 0:24:27.399
<v Speaker 1>stored in the cloud, IBM is going to do a

0:24:27.400 --> 0:24:30.600
<v Speaker 1>lot to help Home Lending Pal protect this sensitive data.

0:24:31.160 --> 0:24:33.080
<v Speaker 1>Part of it is being able to show IBM s

0:24:33.119 --> 0:24:36.000
<v Speaker 1>logo on our website. You'll you'll be surprised how much

0:24:36.000 --> 0:24:39.960
<v Speaker 1>logo recognition helps people understand that this is a legit business,

0:24:40.000 --> 0:24:42.960
<v Speaker 1>a legit company, if you will. And then there's also

0:24:43.000 --> 0:24:46.440
<v Speaker 1>stuff like you know, people seeing the address of the business,

0:24:46.520 --> 0:24:49.240
<v Speaker 1>contact information for the business, like all this stuff factors

0:24:49.280 --> 0:24:51.800
<v Speaker 1>into why people will be willing to give us their data.

0:24:52.119 --> 0:24:54.000
<v Speaker 1>But a lot of that is very contingental, just people

0:24:54.040 --> 0:24:56.159
<v Speaker 1>seeing the IBM logo and saying that, hey, you know,

0:24:56.240 --> 0:24:58.480
<v Speaker 1>we can if we don't trust Home Lending Path, we

0:24:58.480 --> 0:25:01.639
<v Speaker 1>definitely trust IBM with this expect of the business. So

0:25:01.680 --> 0:25:04.439
<v Speaker 1>what is the sort of story of of working with

0:25:04.520 --> 0:25:07.200
<v Speaker 1>IBM on this. I mean, did you just figure out

0:25:07.240 --> 0:25:09.160
<v Speaker 1>that they had the thing you need or did they

0:25:09.200 --> 0:25:11.239
<v Speaker 1>sort of work with you to to build the thing

0:25:11.280 --> 0:25:16.720
<v Speaker 1>you need? We told them what we wanted. I think

0:25:16.760 --> 0:25:21.160
<v Speaker 1>there's a certain special relationship that we have with IBM.

0:25:21.240 --> 0:25:24.320
<v Speaker 1>As I mentioned, Steve and I are are very aggressive

0:25:24.400 --> 0:25:27.240
<v Speaker 1>of internally and externally in terms of getting things change

0:25:27.280 --> 0:25:30.320
<v Speaker 1>in this industry, especially when talk about systemic change, and

0:25:30.400 --> 0:25:33.879
<v Speaker 1>sometimes that requires you to make very big ask, you know,

0:25:33.960 --> 0:25:37.640
<v Speaker 1>swing for the fences and see what happens. And as

0:25:37.640 --> 0:25:40.159
<v Speaker 1>we found out more, as we we hired better talent,

0:25:40.200 --> 0:25:42.280
<v Speaker 1>as we understood more of what we were trying to do,

0:25:43.000 --> 0:25:44.640
<v Speaker 1>it made it a lot easier for us to really

0:25:44.720 --> 0:25:48.560
<v Speaker 1>share this vision with IBM. And then now they're able

0:25:48.600 --> 0:25:51.040
<v Speaker 1>to recommend products to say, we see you're trying to

0:25:51.080 --> 0:25:52.879
<v Speaker 1>do it this way, but maybe you want to use

0:25:52.880 --> 0:25:55.159
<v Speaker 1>our internal product and do it with this instead, And

0:25:55.160 --> 0:25:56.679
<v Speaker 1>so that makes it a lot easier for us to

0:25:56.680 --> 0:26:00.000
<v Speaker 1>try to bring artificial intelligence and blockchain to an industry

0:25:59.880 --> 0:26:05.399
<v Speaker 1>that hasn't historically accepted new technology that well. So where

0:26:05.440 --> 0:26:08.119
<v Speaker 1>are you in your journey as a company. I know

0:26:08.200 --> 0:26:11.280
<v Speaker 1>you're still sort of working on it. What can customers

0:26:11.320 --> 0:26:16.800
<v Speaker 1>do now with your product? They can get recommendations right now.

0:26:17.560 --> 0:26:23.400
<v Speaker 1>We're fully licensed Colorado, Florida and in North Carolina, so

0:26:23.600 --> 0:26:26.119
<v Speaker 1>right now, customers from those days can expect to be

0:26:26.119 --> 0:26:31.240
<v Speaker 1>connected with the lender with full guidance as to what

0:26:31.400 --> 0:26:35.440
<v Speaker 1>exactly they're getting into and what pricing expectations they ought

0:26:35.480 --> 0:26:39.160
<v Speaker 1>to be presented with. Have you heard back? I mean,

0:26:39.200 --> 0:26:41.879
<v Speaker 1>I know that this is kind of a weird question,

0:26:41.880 --> 0:26:45.200
<v Speaker 1>given that the whole point is that people can be anonymized,

0:26:45.280 --> 0:26:48.000
<v Speaker 1>but are you able to talk to your customers? Have

0:26:48.000 --> 0:26:50.800
<v Speaker 1>Have any of your customers told you about how it's

0:26:50.800 --> 0:26:54.760
<v Speaker 1>helped them. Surprisingly, a lot of our customers will reach

0:26:54.760 --> 0:26:56.840
<v Speaker 1>out to us and give us use cases we've had

0:26:56.920 --> 0:26:59.680
<v Speaker 1>local TV interviews, what they've interviewed them. Without those success

0:26:59.680 --> 0:27:02.600
<v Speaker 1>stores will have customers that will reach out to us

0:27:02.600 --> 0:27:04.480
<v Speaker 1>what challenges that they're having and hoping that we can

0:27:04.480 --> 0:27:07.400
<v Speaker 1>help them through those, even if we have to manually

0:27:07.560 --> 0:27:09.879
<v Speaker 1>connect the borrower to a lender and a state that

0:27:09.880 --> 0:27:12.080
<v Speaker 1>we don't operate, and we're more than happy to do that.

0:27:12.560 --> 0:27:15.080
<v Speaker 1>In exchange for that, they're basically helping us build out

0:27:15.119 --> 0:27:18.159
<v Speaker 1>this new process, and so that's kind of the beauty

0:27:18.160 --> 0:27:20.240
<v Speaker 1>of the system is that you know, customers are coming

0:27:20.280 --> 0:27:23.159
<v Speaker 1>in at all stages of the buying cycle. You have

0:27:23.320 --> 0:27:26.399
<v Speaker 1>some that are still renting at that day dreaming phase

0:27:26.400 --> 0:27:28.560
<v Speaker 1>where they're really trying to understand, you know, is home

0:27:28.640 --> 0:27:31.560
<v Speaker 1>ownership a feasible option for me? And you have some

0:27:31.640 --> 0:27:34.120
<v Speaker 1>that are you know, trying to test out new features

0:27:34.119 --> 0:27:36.919
<v Speaker 1>like optimal character recognition software where they're able to upload

0:27:36.960 --> 0:27:39.960
<v Speaker 1>documents and see how those documents transferred to lenders. So

0:27:40.320 --> 0:27:42.640
<v Speaker 1>I really think that is the beauty about what we're

0:27:42.640 --> 0:27:45.040
<v Speaker 1>building is that the people have helped us build it

0:27:45.160 --> 0:27:49.080
<v Speaker 1>so far. Are there any particular stories you've heard from

0:27:49.080 --> 0:27:53.520
<v Speaker 1>customers that have stayed with you? Um? Honestly, I think

0:27:53.560 --> 0:27:56.000
<v Speaker 1>the one that's most relevant to me, that sticks closest

0:27:56.000 --> 0:27:57.680
<v Speaker 1>to my heart is my mom. You know, she was

0:27:57.720 --> 0:28:00.560
<v Speaker 1>looking to try to buy another house. We were able

0:28:00.600 --> 0:28:02.880
<v Speaker 1>to get her approved for a little bit over six

0:28:03.200 --> 0:28:05.480
<v Speaker 1>D fifty thousand, which was about fifty thousand more than

0:28:05.520 --> 0:28:08.760
<v Speaker 1>what she had heard from anyone else in the area. Uh, So,

0:28:08.920 --> 0:28:10.439
<v Speaker 1>you know, we've really been excited, at least I had

0:28:10.480 --> 0:28:13.000
<v Speaker 1>really been excited about that one. That's great, you know,

0:28:13.240 --> 0:28:16.560
<v Speaker 1>to do better than your mom, right, that's the whole

0:28:16.600 --> 0:28:18.679
<v Speaker 1>reason why I built the system, so you know, So

0:28:18.760 --> 0:28:21.520
<v Speaker 1>that one really sticks closest to me is because, uh,

0:28:21.720 --> 0:28:23.680
<v Speaker 1>we've asked some users that have gone through the entire

0:28:23.760 --> 0:28:27.000
<v Speaker 1>process and have helped us go from our initial phase

0:28:27.040 --> 0:28:30.280
<v Speaker 1>and we've really been launching in phases where at first

0:28:30.320 --> 0:28:33.000
<v Speaker 1>it was more show just showing the affordability amount, like

0:28:33.040 --> 0:28:34.359
<v Speaker 1>you know, what was the amount of home that you

0:28:34.400 --> 0:28:37.280
<v Speaker 1>could afford. Now, as Steven mentioned, we're getting into this

0:28:37.400 --> 0:28:42.160
<v Speaker 1>much more interactive, uh, conversational dialogue where consumers are not

0:28:42.240 --> 0:28:44.360
<v Speaker 1>only showing kind of what they want to buy, but

0:28:44.480 --> 0:28:47.040
<v Speaker 1>also getting into kind of what their feelings are, what

0:28:47.320 --> 0:28:49.680
<v Speaker 1>is what are their sentiments that they're looking for in

0:28:49.800 --> 0:28:52.560
<v Speaker 1>a potential relationship with they lender. Uh. So we're really

0:28:52.560 --> 0:28:55.600
<v Speaker 1>excited when consumers come in and they test new features

0:28:55.600 --> 0:28:58.800
<v Speaker 1>and they say, hey, this is working, great, this isn't working, Uh,

0:28:58.840 --> 0:29:00.640
<v Speaker 1>you know what about this? And we think that's really

0:29:00.680 --> 0:29:03.360
<v Speaker 1>going to lead into our series A raise here in

0:29:03.360 --> 0:29:04.720
<v Speaker 1>the next couple of months, where we'll go out and

0:29:04.800 --> 0:29:07.760
<v Speaker 1>raise hopefully eight thiggers or more to really flush out

0:29:07.800 --> 0:29:10.000
<v Speaker 1>the features that consumers have said they wanted the most.

0:29:10.360 --> 0:29:14.520
<v Speaker 1>Is really what we're most excited about. What's your dream

0:29:14.560 --> 0:29:16.600
<v Speaker 1>for home landing Pale If you think whatever, I don't know.

0:29:16.680 --> 0:29:18.719
<v Speaker 1>Five years in the future, ten years in the future,

0:29:19.160 --> 0:29:21.120
<v Speaker 1>where are you. I want to see at least a

0:29:21.160 --> 0:29:24.600
<v Speaker 1>million people, hopefully on a million minorities, become homeowners by

0:29:24.680 --> 0:29:27.840
<v Speaker 1>utilizing our product. You know, we operate in an industry

0:29:27.880 --> 0:29:31.440
<v Speaker 1>that's very lucrative for a lot of people. Having supported

0:29:31.520 --> 0:29:34.120
<v Speaker 1>IBM will hopefully help us from a business perspective, But

0:29:34.840 --> 0:29:36.560
<v Speaker 1>I don't want us to lose sight of our social

0:29:36.560 --> 0:29:39.400
<v Speaker 1>impact goals and the things that we're really set out before,

0:29:39.440 --> 0:29:41.760
<v Speaker 1>which was to make the process more agguiable for everyone.

0:29:41.800 --> 0:29:44.520
<v Speaker 1>You know, I think if we were to be acquired

0:29:44.600 --> 0:29:47.040
<v Speaker 1>or to to do an initial public offering in five

0:29:47.120 --> 0:29:48.920
<v Speaker 1>years and we're not doing that, then for me it

0:29:48.920 --> 0:29:51.240
<v Speaker 1>would not be as as sweet as if it were

0:29:51.320 --> 0:29:54.000
<v Speaker 1>to ensure that we're actually doing stuff to close the

0:29:54.040 --> 0:29:57.720
<v Speaker 1>gap for people. Thank you guys so much for your time.

0:29:57.760 --> 0:30:00.520
<v Speaker 1>I really it was great to talk with you. Pleasure.

0:30:01.120 --> 0:30:11.320
<v Speaker 1>Thank you absolutely. Malcolm glave all here to end today's show,

0:30:11.560 --> 0:30:13.960
<v Speaker 1>I want to talk about someone who we didn't hear

0:30:14.000 --> 0:30:18.960
<v Speaker 1>from in the interview, but who we heard about, Brian's mom,

0:30:19.000 --> 0:30:22.200
<v Speaker 1>because her story really is the story of Home Lending Pall.

0:30:22.960 --> 0:30:25.440
<v Speaker 1>Remember how Brian told us that back in the odds,

0:30:25.760 --> 0:30:28.560
<v Speaker 1>his mom got that crappy mortgage, the one that left

0:30:28.560 --> 0:30:31.680
<v Speaker 1>her paying higher interest rates than she should have been paying.

0:30:32.440 --> 0:30:35.680
<v Speaker 1>That happened to a lot of people, particularly people of color.

0:30:36.400 --> 0:30:39.600
<v Speaker 1>It was that story and others like it that really

0:30:39.680 --> 0:30:43.240
<v Speaker 1>inspired Brian to team up with Stephen to build Home

0:30:43.320 --> 0:30:47.040
<v Speaker 1>Lending Pall. They wanted to fix a home lending system

0:30:47.320 --> 0:30:52.080
<v Speaker 1>that had been opaque and unfair basically forever. Most people

0:30:52.120 --> 0:30:55.800
<v Speaker 1>applying for mortgages aren't thinking about the technology that's behind

0:30:55.800 --> 0:30:58.840
<v Speaker 1>the scenes. We all just want a good mortgage with

0:30:58.920 --> 0:31:03.400
<v Speaker 1>fair terms. And because Brian and Stephen made creative use

0:31:03.480 --> 0:31:09.160
<v Speaker 1>of IBM technology using AI, blockchain and cloud to rethink

0:31:09.240 --> 0:31:13.240
<v Speaker 1>the home loan process, that is now possible for all

0:31:13.320 --> 0:31:19.720
<v Speaker 1>of us. On the next episode of Smart Talks with IBM,

0:31:19.760 --> 0:31:23.120
<v Speaker 1>as AI becomes more widespread, how do we ensure that

0:31:23.200 --> 0:31:27.880
<v Speaker 1>it is built and deployed responsibly, We talked with Pedra

0:31:27.960 --> 0:31:35.680
<v Speaker 1>Bonadira's trustworthy AI practice leader within IBM Consulting. Smart Talks

0:31:35.680 --> 0:31:40.360
<v Speaker 1>with IBM is produced by Molly Sosha, Alexandra Garraton, Royston

0:31:40.440 --> 0:31:45.080
<v Speaker 1>Preserve and Edith Rousselo with Jacob Goldstein. We're edited by

0:31:45.160 --> 0:31:49.560
<v Speaker 1>Jan Guerra. Our engineers are Jason Gambrel, Sarah Brogare and

0:31:49.680 --> 0:31:54.880
<v Speaker 1>Ben Holliday. Theme song by Gramoscope. Special thanks to Colly

0:31:54.960 --> 0:31:59.000
<v Speaker 1>Migliari and Kelly Kathy Callaghan and the eight Bar and

0:31:59.080 --> 0:32:03.720
<v Speaker 1>IBM teams, as well as the Pushkin marketing team. Smart

0:32:03.760 --> 0:32:06.960
<v Speaker 1>Talks with IBM is a production of Pushkin Industries and

0:32:07.120 --> 0:32:11.000
<v Speaker 1>I Heart Media. To find more Pushkin podcasts, listen on

0:32:11.040 --> 0:32:14.920
<v Speaker 1>the i Heart Radio app, Apple Podcasts, or wherever you

0:32:15.000 --> 0:32:19.720
<v Speaker 1>listen to podcasts. I'm Malcolm Gladwell. This is a paid

0:32:19.800 --> 0:32:30.960
<v Speaker 1>advertisement from IBM.