Speaker 1: Hello, Hello, Welcome to a new season of Smart Talks
Speaker 1: with IBM, a podcast from Pushkin Industries, iHeartRadio and IBM.
Speaker 1: I'm Malcolm Glabwell. This season, we're talking to new creators,
Speaker 1: the developers, data scientists, CTOs, and other visionaries who are
Speaker 1: creatively applying technology in business to drive change. Channeling their
Speaker 1: knowledge and expertise, they're developing more creative and effective solutions
Speaker 1: no matter the industry. Our guests today are Brian Young
Speaker 1: and Stephen Better, co founders of home lending Pal. Home
Speaker 1: lending Pal is a member of the IBM hyper Protect Accelerator,
Speaker 1: an investment readiness and technical mentorship program that supports impact
Speaker 1: focused startups leveraging highly sensitive data. Their story is a
Speaker 1: perfect place to start our season. They recognize used a
Speaker 1: profound problem the horrible process of getting a home loan,
Speaker 1: especially if you're part of an underserved community, a process that,
Speaker 1: as you'll hear, is not only confusing and complex, but
Speaker 1: often deeply unfair. So Brian and Stephen teamed up to
Speaker 1: use technology to attack that problem in a bunch of
Speaker 1: creative ways. You'll hear how they're tapping into blockchain to
Speaker 1: make the home loan process more transparent and fair, using
Speaker 1: AI to help people learn how to qualify for a loan,
Speaker 1: and relying on IBM technology to store consumers most sensitive
Speaker 1: information safely in the cloud. Brian and Stephen talked with
Speaker 1: Jacob Goldstein, host of the pushkin podcast What's Your Problem.
Speaker 1: Jacob has covered technology and business for over a decade,
Speaker 1: first at The Wall Street Journal, then at MPR. Now
Speaker 1: let's get into the interview. Let's start this like a
Speaker 1: rom com. How did you meet each other and decide
Speaker 1: to start a company together. Stephen was supposed to come
Speaker 1: to a bachelor party in Miami and didn't show up,
Speaker 1: and it broke my heart. There's more to the story
Speaker 1: than just simply that one of my old employees introduced us.
Speaker 1: I've just left Marcato. They've been acquired for one point
Speaker 1: four billion, and I am, you know, living the Miami lifestyle.
Speaker 1: You know, I have a condo on the water and
Speaker 1: all the nice things that go with it. A guy
Speaker 1: named Michael Ramsey had asked me, you know, would I
Speaker 1: help him do mortgage lead generation? And I was like,
Speaker 1: you know, I'm sure I'm not doing anything else. Why not?
Speaker 1: And I meet Steve that he was in North Carolina.
Speaker 1: I left a pretty fruitful career in banking. I was
Speaker 1: an underwriter underwriting loans. Mean it's basically deciding who should
Speaker 1: get a loan and at what interest rate? Right, absolutely
Speaker 1: due diligence, right, which is understanding whether or not this
Speaker 1: particular individual has the wherewithal to afford the mortgage, also
Speaker 1: the credit risks that individual presents. But there was this
Speaker 1: disconnect in that process where you have hidden action taking
Speaker 1: place on one side of the transaction, while you have
Speaker 1: another side of the transaction that that tends to hide information.
Speaker 1: And just to be clear, it's the borrower who hides
Speaker 1: information and the bank that hides the action the lender
Speaker 1: in most cases, but it's usually both sides of the negotia.
Speaker 1: Everybody's hiding stuff from everybody else's absolutely, and it's like
Speaker 1: sort of inadvertent as well too. Right in that process,
Speaker 1: certain things fall through the cracks. And you know, falling
Speaker 1: through the cracks means weeks without notification from a bar's
Speaker 1: perspective as to whether or not you know this deal
Speaker 1: is moving forward. Okay, So the problem is a lack
Speaker 1: of information on both sides, and that winds up leading
Speaker 1: to bad outcomes. It winds up leading to long delays
Speaker 1: that are frustrating or scary for the borrower. Yes, a
Speaker 1: lot of consumers just don't have anywhere to go. If
Speaker 1: you go online, everything is too broad engineering, especially if
Speaker 1: you know you're not ready to buy at that moment.
Speaker 1: If you talk to a lender or relator, if you're
Speaker 1: not ready to buy at that moment, there they'll help you,
Speaker 1: but it's not the same level of help. But you're
Speaker 1: not going to get that same level of support over months,
Speaker 1: because you know, buying house isn't like buying a piece
Speaker 1: of candy online. And so we really looked at, Okay, well,
Speaker 1: how can we give people this safe environment to go
Speaker 1: explore and understand when homeownership could look like for them
Speaker 1: based on their personal information. And that's kind of when
Speaker 1: I reach back out to Stephen around August of twenty
Speaker 1: seventeen and said, Hey, you know, we need to do
Speaker 1: this together. You understand the back inside from a lender
Speaker 1: underwriter's perspective, and I understand the plight of the consumers,
Speaker 1: and if we come together, this could be something that
Speaker 1: could be really unique. A capitalist solution to a social
Speaker 1: challenge is probably the best way to put it. So Stephen,
Speaker 1: you're sort of coming from the banking side, and Brian
Speaker 1: you're sort of coming from the tech side. Absolutely, what
Speaker 1: exactly is the problem that you guys are trying to
Speaker 1: solve when you start this company, and its simplest essence
Speaker 1: is data democratization, the ability to take complex information and
Speaker 1: simplify so that someone that isn't an expert like Stephen
Speaker 1: can understand what's going on. And in this case specifically,
Speaker 1: what is the data that you're trying to democratize? Underwriting data,
Speaker 1: so the decision or the data that is utilized to
Speaker 1: determine whether or not you are approved or declined for
Speaker 1: a home loan. So right now, if I go apply
Speaker 1: for a loan, they approve me or they decline me.
Speaker 1: But do I know why not? Really? I mean, you
Speaker 1: get a letter of an adverse letter, but it's still
Speaker 1: very broad Engineeric, it doesn't always tell you what to
Speaker 1: focus on next, but you do have a very high
Speaker 1: level sense of why your decline. Yeah, there's no true
Speaker 1: guidance from that point of rejection, right there's no fundamental
Speaker 1: understanding as to what could I have done better? And
Speaker 1: that's really what sets this platform apart, and also why
Speaker 1: it's important how we're sort of reframing of this data workflow.
Speaker 1: I want to get into the details of that, but
Speaker 1: just as we sort of understand the problem a little
Speaker 1: bit more, I mean, one piece of it that we
Speaker 1: haven't talked about is race and the homeownership gap. Can
Speaker 1: you guys talk a little bit about that and how
Speaker 1: it fits with what you're trying to do. Yeah, I mean,
Speaker 1: the homeownership gap, at least for African Americans is larger
Speaker 1: now than it was fifty years ago and segregation was legal,
Speaker 1: which is quite saddening. But it's not just African Americans.
Speaker 1: And when you look at declines, whether you are a woman,
Speaker 1: whether you are a minority, whether you're a part of
Speaker 1: the LBTTQ plus community, it shows that there's a higher
Speaker 1: level of declines for these communities than there are for
Speaker 1: older white males. So you know, in our perspective, there
Speaker 1: has to be a lot that needs to be done
Speaker 1: in terms of resetting reconfiguring the system to make it
Speaker 1: more fair and equitable for all. So, if I understand
Speaker 1: you correctly, you're saying, basically, in the current system, white
Speaker 1: men have an easier time getting a mortgage than anybody else.
Speaker 1: Well you said it. I'll just agree with it. I
Speaker 1: think you said it. I think if I understood you're
Speaker 1: correctly you said, yeah, that is what the data shows us.
Speaker 1: And it's not just my perspective, that's what the data
Speaker 1: shows us. So and so, how are you trying to
Speaker 1: help fix that problem by turning everybody into corn? By
Speaker 1: turning everybody into corn? I like it, Yeah, what do
Speaker 1: you mean by that? Through the power of math, right,
Speaker 1: cryptography specifically, we are able to make everyone look the
Speaker 1: same and the underwriter just simply understands the fundamental attributes
Speaker 1: that ought to drive that approval disapproval decision. Right, in
Speaker 1: order to help us and also to help our government
Speaker 1: understand where these biases are coming from. Our lenders are
Speaker 1: required to ask you what your raise, what your sex,
Speaker 1: even your age? Right, Like, all of this comes with
Speaker 1: this application scenario. But does this information inadvertingly create the bias?
Speaker 1: Can we make everyone look the same and start to
Speaker 1: remove or better identify where these issues are sort of
Speaker 1: coming from? So you're trying to use technology to blind
Speaker 1: all the decision makers in the home loan process to
Speaker 1: race ethnicity, specifically blockchain. There are three big tech ideas
Speaker 1: behind home lending pal at least three that we're going
Speaker 1: to talk about today on the show, and blockchain is
Speaker 1: big tech idea number one. You may have heard of
Speaker 1: blockchain because it's the key idea behind cryptocurrency, but the
Speaker 1: idea of blockchain is bigger than just digital money and
Speaker 1: much more than just a new way to store information
Speaker 1: on the Internet. Blockchain is a shared, immutable ledger that
Speaker 1: facilitates the process of recording transactions and tracking assets in
Speaker 1: a business network. Brian and Stephen want to use blockchain
Speaker 1: to gather up the information on race and gender that's
Speaker 1: required by law without showing it to the lenders making
Speaker 1: the decisions about who gets alan. Our argument, or our
Speaker 1: thesis is that with the leverage of a mutable ledger
Speaker 1: such as blockchain, you're able to still collect the information
Speaker 1: that is necessary for the Home Mortgage Disclosure Act or
Speaker 1: HUMDA as Stephen was referring to. But then with a
Speaker 1: smart contract, you don't have to release that information, so
Speaker 1: after the decision, the approval of decline is made for
Speaker 1: the consumer. So you have this big idea for what
Speaker 1: you want to do as a business, which you want
Speaker 1: to do socially, but how do you make creative use
Speaker 1: of technology to do the thing you want to do,
Speaker 1: to make it real. You know, we're trying to build
Speaker 1: something that hasn't been done in the mortgage industry. Sho
Speaker 1: we talk about artificial intelligence and a virtual assistant, most
Speaker 1: people think of that it's just a one way street.
Speaker 1: You know, we are trying to build this human like
Speaker 1: interaction where it is able to not only understand, but
Speaker 1: to respond, and then to leverage those responses and create
Speaker 1: a roadmap towards allowing you to achieve your goals, which
Speaker 1: is probably one of the most creative things that I've
Speaker 1: ever done personally. But it also requires you to be
Speaker 1: willing to accept constructive criticism from the people that are
Speaker 1: going to be using it up front, and a lot
Speaker 1: of what we're doing is really trying to find creative
Speaker 1: ways just to get them involved in that conversation to
Speaker 1: say that, hey, you know, we are trying to build
Speaker 1: this to help you. Right now, there's about twenty one
Speaker 1: million mortgage reading millennials to day that are qualified to
Speaker 1: buy a loan, even though they're not trying. They just
Speaker 1: don't know. We're trying to bring greater trust and transparency
Speaker 1: to this process. Yeah, I guess, from my perspective, beyond
Speaker 1: just simply understanding the technology and what it's able to do,
Speaker 1: I think it takes the will to go ahead and
Speaker 1: take on that complexity to try something new. We were
Speaker 1: challenged constantly with why not a simpler solution, right, But
Speaker 1: in reality, the problem is much more complicated than the
Speaker 1: simplicity these forces wanted to bring into the table. You
Speaker 1: have to have vision, you have to have a desire
Speaker 1: to want to make fundamental change. Yeah, new tech built
Speaker 1: on old, broken processes doesn't allow for systemic change. You know,
Speaker 1: we have to try to find ways to not only
Speaker 1: just to make it easier for people to connect to lenders,
Speaker 1: but at the core of what we were trying to build,
Speaker 1: we really wanted to address the systemic issues in the
Speaker 1: home buying process, and that required us to try something
Speaker 1: different basically, and I think that's the most creative thing
Speaker 1: you can do in an industry that typically a Stephen
Speaker 1: mentioned wanted us to do its simpler. Yeah. So one
Speaker 1: of the ideas you guys have is that transparency can
Speaker 1: help reduce bias. So in what way are you using
Speaker 1: technology to bring more transparency to the home buying process?
Speaker 1: When we speak of transparency when we speak of trust,
Speaker 1: what we're really talking about is just the natural features
Speaker 1: of the blockchain. Right. It's transparent because all participants within
Speaker 1: this framework of access to this decentralized ledger. So we
Speaker 1: are all seeing how these pieces are sort of moving. Right,
Speaker 1: we're playing poker with our cards facing up when we're
Speaker 1: speaking to trust, right, we're speaking of the mutability of
Speaker 1: this information, knowing that if an action is taken, it's
Speaker 1: there on the ledger and we can't just simply remove it.
Speaker 1: So these features lead to this forceful curing of certain
Speaker 1: biases that tend to form within certain systems. We're not
Speaker 1: saying that we're going to move all biased, but what
Speaker 1: we're saying is that we feel very confident that we'll
Speaker 1: be able to reduce it significantly without regulatory reinforcement by
Speaker 1: the simple nature of this technology stack that we're developing. So,
Speaker 1: was there some moment when you guys had the light bulb,
Speaker 1: the a high idea that you could do this. The
Speaker 1: moment that made me realize that this was stable was
Speaker 1: when our first group of lenders invested. It was a
Speaker 1: group called the Mortgage Collaborative. They are a collection of
Speaker 1: about three hundred and twenty five lenders. I believe across
Speaker 1: the country they represent about twenty five percent the overall
Speaker 1: originations that happened in the US. When they kind of
Speaker 1: stepped in and we're like, hey, you know, we're going
Speaker 1: to lead her your development before your Series A, we're
Speaker 1: going to try to help you there, I think that
Speaker 1: was the moment for me. And then we had shortly
Speaker 1: after that joining that round was a group called Quino
Speaker 1: Mutual or CMfg Ventures their Discovery Fund, and they are
Speaker 1: the one of the largest collections of credit unions in
Speaker 1: the industry. So, you know, typically you have an issue
Speaker 1: where you know, consumers feel like there's a problem that's
Speaker 1: not truly being solved. But to see that lenders we're
Speaker 1: looking to try to find solutions like ours. I think
Speaker 1: that was the AHA moment for me and said, hey,
Speaker 1: you know this could be feasible for us, that the
Speaker 1: people who will actually have to work with you want
Speaker 1: to help you. Like, that's exactly great, but just tell
Speaker 1: me how will it work, Like walk me through. I'm
Speaker 1: an ordinary person. I want to get a loan. I
Speaker 1: come to home lending Pal. What happens when you're fully
Speaker 1: you know, fully up and running. How's it going to work? Yeah,
Speaker 1: So you will spend about five minutes going through our
Speaker 1: onboarding process where you're connecting your online bank accounts, you're
Speaker 1: authorizing a soft FCO pool. There's a credit report, basically
Speaker 1: a credit report. Yeah, you're here. Most people don't realize. So,
Speaker 1: so lenders are utilizing your FIICO scores and most of
Speaker 1: the places online that you're able to go to or
Speaker 1: showing advantage scores. So that's kind of the first level
Speaker 1: of disconnect and so we're solving for that first. So
Speaker 1: you go to that process and then after you signed up,
Speaker 1: our virtual assistant keV begins doing his work. He's analyzing
Speaker 1: your profile. He's really geared towards helping you understand really
Speaker 1: three or four critical elements. You know, One you're likelihood
Speaker 1: for success or approval to some financial modeling and forecast
Speaker 1: and give you a better understanding of when you should
Speaker 1: begin the process to apply for a home So how
Speaker 1: long will it take you to become a homeowner or
Speaker 1: to close them a home. Three, the best loan product
Speaker 1: for you and then for the lenders within our ecosystem
Speaker 1: that present the best chance of success with them as well.
Speaker 1: So you mentioned a virtual advisor keV virtual meaning it's
Speaker 1: not a guy named keV right. It's named after one
Speaker 1: of my good friends from college that passed from a
Speaker 1: rare form of germ sale cancer. He's probably one of
Speaker 1: the most helpful, friendly people that you've ever met, and
Speaker 1: it didn't matter who you were, so we really wanted
Speaker 1: to encompass his personality into the solution itself. But yes, keV,
Speaker 1: it becomes a friend of pal, you know, so even
Speaker 1: if you're not ready to buy, he just doesn't pass
Speaker 1: you off and say, hey, I'm not going to help.
Speaker 1: It really analyzes your profile and begins to create a
Speaker 1: path that you can follow to become a homeowner. We
Speaker 1: have arrived at big tech idea number two. keV the
Speaker 1: Virtual Assistant is built using powerful artificial intelligence tools. The
Speaker 1: AI takes the potential homebuyers information and runs it through
Speaker 1: algorithms that tell you things like how likely you are
Speaker 1: to get a loan, and what loan makes the most
Speaker 1: sense for you, and how long the whole process is
Speaker 1: likely to take. You can ask keV questions and it'll
Speaker 1: give you answers. But keV is more than your average
Speaker 1: responder chatbot. It speaks conversationally. It knows who you are,
Speaker 1: understands your needs, and helps beyond just providing a frequently
Speaker 1: ask questions link. Brian says he thinks a lot of
Speaker 1: people might be more comfortable talking with an aipowered virtual
Speaker 1: assistant than with a human loan officer at a bank.
Speaker 1: I think it really solves a cultural problem. There are
Speaker 1: cultural barriers that prevent different segments from becoming homeowners or
Speaker 1: at least impact their buying decisions in terms of how
Speaker 1: they explore homeownership. So the first part is to try
Speaker 1: to use this virtual assistant just to make them feel
Speaker 1: comfortable getting into the process of what homeownership could look like.
Speaker 1: And then from there it is about preparing them, getting
Speaker 1: them better qualify so that once they are ready to say, hey,
Speaker 1: I want to come home owner, I found the house
Speaker 1: that I love, allowing that transaction, that process to be
Speaker 1: a lot smoother and easier through the use of blockchain. Basically,
Speaker 1: so when you say cultural, I mean does that include
Speaker 1: in part race and ethnicity, people who have traditionally been
Speaker 1: excluded from the banking sector from housing. Is the dream
Speaker 1: that sort of a I can help people who've been
Speaker 1: excluded become more included. Yes, most white people have resources.
Speaker 1: They have other friends and family who have gone through
Speaker 1: this process successfully multiple times as opposed to just the
Speaker 1: one time. Within our communities, it is difficult just to
Speaker 1: find the one person that you can discuss this process with,
Speaker 1: and most of the time that one person has gone
Speaker 1: through a negative experience in that right. Brian's parents have
Speaker 1: experienced difficulty in this instry. My parents have experienced difficulty
Speaker 1: in this process too. Isn't until you get to our
Speaker 1: generation where you have family members that have gone through
Speaker 1: this process multiple times and have been successful. So when
Speaker 1: we speak to keV being culturally relevant, it's because keV
Speaker 1: is there to provide you accurate support that historically hasn't
Speaker 1: been available to these marginalized groups. Stephen, you mentioned your
Speaker 1: own families, your and Brian's families experience with getting home
Speaker 1: loans with the banking system. Do you guys mind just
Speaker 1: talking about that specifically? What have been your family's experiences
Speaker 1: with getting loan please? Yeah? Yeah, I mean back in
Speaker 1: the subprime mortgage crisis, and you know, my mom nearly
Speaker 1: lost her dream home that I bought for those primarily
Speaker 1: because we were in an arm even though we should
Speaker 1: have been an AVA loan because she is a military
Speaker 1: veteran and an arm an adjustable rate loans sold Alan
Speaker 1: that was way worse than the mortgages. It was way worse.
Speaker 1: I mean, you know, it started out better just because
Speaker 1: you pay less, but once that interest rate flips, it
Speaker 1: becomes way worse if you're not prepared for it. And
Speaker 1: I think you know, again, when when we talk about
Speaker 1: these cultural factors, there's really five that you deal with.
Speaker 1: There's there's cultural itself. So things like the subprime mortgage
Speaker 1: crisis where African Americans are hurt the most coming out
Speaker 1: of that, you have redlining, reverse redlining, etc. Redlining is
Speaker 1: basically the history of lenders not making loans to people
Speaker 1: in predominantly black neighborhoods. Essential exactly, we're picking which areas
Speaker 1: they will lend to specific groups. Yes, and those areas
Speaker 1: were predominantly white historically and predominantly why yes. So you
Speaker 1: have those elements. You have the economic elements where there's
Speaker 1: this concept of as just being unattainable for us. You
Speaker 1: have the psychological elements of being misunderstood thinking that the
Speaker 1: only way I can buy a home is having twenty
Speaker 1: percent down to put down towards a down payment, and
Speaker 1: that's just not true. So our ultimate objective is just
Speaker 1: really to make that more attainable for everyone, and it's
Speaker 1: really for all load of modern income bar hours these days,
Speaker 1: just because with rates increasing, with the supply shortages that
Speaker 1: we have, you know, homeownership is really going to become
Speaker 1: a lot more difficult for a lot of people, regardless
Speaker 1: of their age, sex, and race. So you have this
Speaker 1: industry that suffers from a lack of transparency, from historical
Speaker 1: bias in terms of race and gender. You start this
Speaker 1: technology driven company to try and fix those things. As
Speaker 1: you're building the company, how do you come to work
Speaker 1: with IBM our need for data protection and security? So
Speaker 1: you're talking about digitizing documents, digitizing information to allow greater
Speaker 1: access to underserved, underrepresented groups. And IBM had their hyper
Speaker 1: Protect Accelerator which was entirely focused on that, taking small
Speaker 1: startups like Hours and allowing them to basically run the
Speaker 1: pilots that we ran without having to worry about people's
Speaker 1: information getting stolen in essence, and then Steve and I
Speaker 1: were just very aggressive in terms of just reaching out
Speaker 1: to different vps, different executives at IBM kind of saying,
Speaker 1: you know, here's what we want to do, here's what
Speaker 1: we need. Will you help us? And being in an
Speaker 1: industry that is so regulated, it helped us really get
Speaker 1: to that door, just because you know, every bank has
Speaker 1: a vendor on boarding process that requires a very high
Speaker 1: level of data security to even work with them. In
Speaker 1: an essence, here's the third big tech idea in a
Speaker 1: Home Lending Pulse story, protecting data in the cloud. Think
Speaker 1: about the problem this one is solving. Brian and Stephen
Speaker 1: have this little startup. They need to collect supersensitive data
Speaker 1: from people. Everything you have to show the bank when
Speaker 1: you want to get a mortgage. This data has to
Speaker 1: be secure. IBM's hyper Protect Accelerator enables small businesses to
Speaker 1: store sensitive data in the cloud and keep that data secure.
Speaker 1: Brian says, it lets Home Lending Pall do something they
Speaker 1: would never do on their own. From a technical perspective,
Speaker 1: you have different compliance checks that you have to meet
Speaker 1: to work with banking institutions or financial institutions. So it
Speaker 1: allows a small startup like Home Lending Pall to still
Speaker 1: be able to meet those checks and balances. To bring
Speaker 1: an innovative solution to the table for a financial institution
Speaker 1: where more than likely as a startup, you're not going
Speaker 1: to have the ability to do that on your own,
Speaker 1: just because it is so expensive to either have internal
Speaker 1: servers or to try do it on your own as well.
Speaker 1: So people have to trust you to use Homelanding Piller, right, Like,
Speaker 1: I'm giving you everything, how do you convince me? How
Speaker 1: do you convince customers that you're going to keep their
Speaker 1: data safe? Absolutely? Part of it is doing stuff like
Speaker 1: this where we're acknowledging and making the consumers aware of
Speaker 1: our relationship with IBM and how IBM is handling our
Speaker 1: storage of the data and the sensitive data itself. Technically,
Speaker 1: the IBM description of it is their confidential computing services
Speaker 1: or cloud services, and it's basically saying that even though
Speaker 1: the information is stored in the cloud, IBM is going
Speaker 1: to do a lot to help home Landing pill protect
Speaker 1: this sensitive data. Part of it is being able to
Speaker 1: show IBM's logo on our website. You'll be surprised how
Speaker 1: much logo recognition helps people understand that this is a
Speaker 1: legit business, a legit company, if you will. And then
Speaker 1: there's also stuff like you know, people seeing the address
Speaker 1: of the business, contact information for the business. Like all
Speaker 1: this stuff backers into why people will be willing to
Speaker 1: give us their data. But a lot of that's very
Speaker 1: contingent on just people seeing the IBM logo and saying that, hey,
Speaker 1: you know, we can if we don't trust Home Landing Path,
Speaker 1: we definitely trust IBM with this aspect of the business.
Speaker 1: So what is the sort of story of working with
Speaker 1: IBM on this? I mean, did you just figure out
Speaker 1: that they had the thing you need or did they
Speaker 1: sort of work with you to build the thing you need?
Speaker 1: We told them what we wanted. I think there's a
Speaker 1: certain special relationship that we have with IBM. As I mention,
Speaker 1: you know, Steve and I are very aggressive of internally
Speaker 1: and externally in terms of getting things change in this industry,
Speaker 1: especially when we talk about systemic change, and sometimes that
Speaker 1: requires you to make very big asks, you know, swing
Speaker 1: for the fences and see what happens. And as we
Speaker 1: found out more, as we hired better talent, as we
Speaker 1: understood more of what we were trying to do, it
Speaker 1: made it a lot easier for us to really share
Speaker 1: this vision with IBM. And then now they're able to
Speaker 1: recommend products to say we see you're trying to do
Speaker 1: it this way, but maybe you want to use our
Speaker 1: internal product, can do it with this instead, and so
Speaker 1: that makes a lot easier for us to try to
Speaker 1: bring artificial intelligence in blockchain to an industry that hasn't
Speaker 1: historically accepted new technology that well. So where are you
Speaker 1: in your journey as a company. I know you're still
Speaker 1: sort of working on it. What can customers do now
Speaker 1: with your product? They could get recommendations right now. We're
Speaker 1: fully licensed in Colorado, Florida, and in North Carolina, So
Speaker 1: right now, customers from those days can expect to be
Speaker 1: connected with a lender with full guidance as to what
Speaker 1: exactly they're getting into and what pricing expectations they ought
Speaker 1: to be presented with. Have you heard back? I mean,
Speaker 1: I know that this is kind of a weird question,
Speaker 1: given that the whole point is that people can be anonymized,
Speaker 1: But are you able to talk to your customers? Have
Speaker 1: any of your customers told you about how it's helped them? Surprisingly,
Speaker 1: a lot of our customers will reach out to us
Speaker 1: and give us use cases, as we've had local TV
Speaker 1: interviews where they've interviewed them about those success stories. Will
Speaker 1: have customers that all reach out to us with challenges
Speaker 1: that they're having and hoping that we can help them
Speaker 1: through those, even if we have to manually connect a
Speaker 1: borrower to a lender and a state that we don't operate,
Speaker 1: and we're more than happy to do that. In exchange
Speaker 1: for that, they're basically helping us build out this new process.
Speaker 1: And so that's kind of the beauty of the system
Speaker 1: is that, you know, customers are coming in at all
Speaker 1: stages of the buying cycle. You have some that are
Speaker 1: still renting, at that daydreaming phase where they're really trying
Speaker 1: to understand, you know, is home ownership a feasible option
Speaker 1: for me? And then you have some that are, you know,
Speaker 1: trying to test out new features like optimal character recognition
Speaker 1: software where they're able to upload documents and see how
Speaker 1: those documents transferred to lenders. So I really think that
Speaker 1: is the beauty about what we're building, is that the
Speaker 1: people have helped us build it so far. Are there
Speaker 1: any particular stories you've heard from customers that have stayed
Speaker 1: with you? So, I think the one that's most relevant
Speaker 1: to me, that sticks closest to my heart is my mom.
Speaker 1: You know, she was looking to try to buy another house,
Speaker 1: and we were able to get her approved for a
Speaker 1: little bit over six hundred and fifty thousand, which was
Speaker 1: about fifty thousand more than what she had heard from
Speaker 1: anyone else in the area. So, you know, we've really
Speaker 1: been excited. At least I'd really been excited about that one.
Speaker 1: That's great. You're not going to do better than your mom,
Speaker 1: right helped your mom. That was the whole reason I
Speaker 1: built the system, So you know, so that one really
Speaker 1: sticks closest to me is because we've had some users
Speaker 1: that have gone through the entire process and have helped
Speaker 1: us go from our initial phase and we've really been
Speaker 1: launching in phases where at first it was more showed
Speaker 1: just showing the affordability amount, like you know, what was
Speaker 1: the amount of home that you could afford, And now,
Speaker 1: as Steven mentioned, we're getting into this much more interactive
Speaker 1: conversational dialogue where consumers are not only showing kind of
Speaker 1: what they want to buy, but also getting into kind
Speaker 1: of what their feelings are, what is what are their
Speaker 1: sentiments that they're looking for in a potential relationship with
Speaker 1: they lender. And so we're really excited and consumers come
Speaker 1: in and they test new features and they say, hey,
Speaker 1: this is working, great, this isn't working you know what
Speaker 1: about this? And we think that's really going to lead
Speaker 1: into our series A raise here in the next couple
Speaker 1: of months, where we'll go out and raise hopefully eight
Speaker 1: figures or more to really flush out the features that
Speaker 1: consumers have set they want it the most. Is really
Speaker 1: what we're most excited about. What's your dream for homelanding pal?
Speaker 1: If you think whatever, I don't know, five years in
Speaker 1: the future, ten years in the future, where are you.
Speaker 1: I want to see at least a million people, hopefully
Speaker 1: a million minorities become homeowners by utilizing our product. You know,
Speaker 1: we operate in an industry that's very lucrative for a
Speaker 1: lot of people. Having supported IBM will hopefully help us
Speaker 1: from a business perspective, But I don't want us to
Speaker 1: lose sight of our social impact goals and the things
Speaker 1: that we're really set out before, which was to make
Speaker 1: the process more eggiable for everyone. You know, I think
Speaker 1: if we were to be acquired or to do an
Speaker 1: initial public offering in five years and we're not doing that,
Speaker 1: then for me it would not be as sweet as
Speaker 1: if it were. To ensure that we're actually doing stuff
Speaker 1: to close the gap for people. Thank you guys so
Speaker 1: much for your time. I really it was great to
Speaker 1: talk with you. Pleasure Well, thank you absolutely. Malcolm Glavell.
Speaker 1: Here to end today's show, I want to talk about
Speaker 1: someone who we didn't hear from in the interview, but
Speaker 1: who we heard about, Brian's mom, because her story really
Speaker 1: is the story of home Lending Pal. Remember how Brian
Speaker 1: told us that back in the adds, his mom got
Speaker 1: that crappy mortgage, the one that left her paying higher
Speaker 1: interest rates than she should have been paying. That happened
Speaker 1: to a lot of people, particularly people of color. It
Speaker 1: was that story and others like it that really inspired
Speaker 1: Brian to team up with Stephen to build Home Lending Pal.
Speaker 1: They wanted to fix a home lending system that had
Speaker 1: been opaque and unfair basically forever. Most people applying for
Speaker 1: mortgage aren't thinking about the technology that's behind the scenes.
Speaker 1: We all just want a good mortgage with fair terms.
Speaker 1: And because Brian and Stephen made creative use of IBM
Speaker 1: technology using AI, blockchain, and cloud to rethink the home
Speaker 1: loan process, that is now possible for all of us.
Speaker 1: On the next episode of Smart Talks with IBM. As
Speaker 1: AI becomes more widespread, how do we ensure that it
Speaker 1: is built and deployed responsibly? We talk with Pedra Bonadira's
Speaker 1: trustworthy AI practice leader within IBM Consulting. Smart Talks with
Speaker 1: IBM is produced by Molly Sosha, Alexandra Garatin, Royston Reserved,
Speaker 1: and Edith Rousselo with Jacob Goldstein. We're edited by Jen Guerra.
Speaker 1: Our engineers are Jason Gambrel, Sarah Bregere, and Ben Tolliday.
Speaker 1: Theme song by Gramoscope. Special thanks to Colli Migliori, Andy Kelly,
Speaker 1: Kathy Callahan and the eight Bar and IBM teams, as
Speaker 1: well as the Pushkin marketing team. Smart Talks with IBM
Speaker 1: is a production of Pushkin Industries and iHeartMedia. To find
Speaker 1: more Pushkin podcasts, listen on the iHeart Radio app, Apple Podcasts,
Speaker 1: or wherever you listen to podcasts. I'm Malcolm Gladwell. This
Speaker 1: is a paid advertisement from IBM.