Speaker 1: Welcome to tex Stuff, a production from I Heart Radio.
Speaker 1: This season of Smart Talks with IBM is all about
Speaker 1: new creators, the developers, data scientists, c t o s
Speaker 1: and other visionaries creatively applying technology in business to drive change.
Speaker 1: They use their knowledge and creativity to develop better ways
Speaker 1: of working, no matter the industry. Join hosts from your
Speaker 1: favorite Pushkin Industries podcasts as they use their expertise to
Speaker 1: deepen these conversations, and of course Malcolm Gladwell will guide
Speaker 1: you through the season as your host and provide his
Speaker 1: thoughts and analysis along the way. Look out for new
Speaker 1: episodes of Smart Talks with IBM on the I Heart
Speaker 1: Radio app, Apple Podcasts, or wherever you get your podcasts,
Speaker 1: and learn more at IBM dot com slash smart talks. Hello, Hello,
Speaker 1: welcome to a new season of Smart Talks with IBM,
Speaker 1: a podcast from pushed In Industries, I Heart Radio and IBM.
Speaker 1: I'm Malcolm Gladwell. This season we're talking to new creators,
Speaker 1: the developers, data scientists, c t o s and other
Speaker 1: visionaries who are creatively applying technology in business to drive change.
Speaker 1: Channeling their knowledge and expertise, they're developing more creative and
Speaker 1: effective solutions no matter the industry. Our guest today are
Speaker 1: Brian Young and Stephen Better, co founders of home lending Pal.
Speaker 1: Home lending Pal is a member of the IBM hyper
Speaker 1: Protect Accelerator, an investment readiness and technical mentorship program that
Speaker 1: supports impact focused startups leveraging highly sensitive data. Their story
Speaker 1: is a perfect place to start our season. They recognized
Speaker 1: a profound problem, the horrible process of getting a home loan,
Speaker 1: especially if you're part of an unders serve community, a
Speaker 1: process that, as you'll hear, is not only confusing and complex,
Speaker 1: but often deeply unfair. So Brian and Stephen teamed up
Speaker 1: to use technology to attack that problem in a bunch
Speaker 1: of creative ways. You'll hear how they're tapping into blockchain
Speaker 1: to make the home loan process more transparent and fair,
Speaker 1: using AI to help people learn how to qualify for
Speaker 1: a loan, and relying on IBM technology to store consumers
Speaker 1: most sensitive information safely in the cloud. Brian and Stephen
Speaker 1: talked with Jacob Goldstein, host of the pushkin podcast What's
Speaker 1: Your Problem. Jacob has covered technology and business for over
Speaker 1: a decade, first at The Wall Street Journal, then at MPR.
Speaker 1: Now let's get into the interview. Let's start this like
Speaker 1: a brom car How did you meet each other and
Speaker 1: decided to start a company together. Steven was supposed to
Speaker 1: come 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: for a billion, and I am, you know, living the
Speaker 1: Miami lifestyle. You know, I have a condo on the
Speaker 1: water and all the nice things to go with. A
Speaker 1: guy named Michael Ramsey had asked me, you know what,
Speaker 1: I helped him do mortgage lead generation and I was like,
Speaker 1: you know, 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 worldwithal to afford the mortgage. Also
Speaker 1: the credit risk individual presents. But there was this disconnect
Speaker 1: in that process where you have hidden action taking place
Speaker 1: on one side of the transaction while you have another
Speaker 1: 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 this is usually both sides of
Speaker 1: the negotia. Everybody's hiding stuff from everybody else. There's a
Speaker 1: absolutely and it's like sort of inadvertent as well too,
Speaker 1: right in that process, and things fall through the cracks,
Speaker 1: and you know, falling through the cracks means weaks without
Speaker 1: notification from a bar's perspective as to whether or not
Speaker 1: you know this deal is moving forward. Okay, So so
Speaker 1: the problem is a lack of information on both sides,
Speaker 1: and that winds up leading to bad outcomes. It winds
Speaker 1: up leading to long delays that are frustrating or scary
Speaker 1: for the for the borrower, yes, who are of consumers
Speaker 1: just don't have anywhere to go if you go online,
Speaker 1: everything is too broad engineering, especially if you know you're
Speaker 1: not ready to buy at that moment. Uh. If you
Speaker 1: talk to a lender or relatory, if you're not ready
Speaker 1: to buy at that moment, there they'll help you, but
Speaker 1: it's not the same level of help. But you're not
Speaker 1: gonna get that same level of support over months because
Speaker 1: you know, buying houses and like buying a piece of
Speaker 1: 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 two
Speaker 1: thousand seventeen and said, hey, you know, we need to
Speaker 1: do this together. You understand the back inside from a
Speaker 1: lender underwritish 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've got us are trying
Speaker 1: to solve when you start this company, and its simple
Speaker 1: assessence is data democratization, the ability to take complex information
Speaker 1: and simplify so that someone that isn't an expert like
Speaker 1: Stephen 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 really 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 all. So
Speaker 1: why it's important how we're sort of reframing of this
Speaker 1: data workflow. I want to get into the details of that.
Speaker 1: But just as we sort of understand the problem a
Speaker 1: little bit more, I mean, one piece of it that
Speaker 1: we haven't talked about is is race and the home
Speaker 1: ownership gap. Can you guys talk a little bit about
Speaker 1: that and how it fits with with what you're trying
Speaker 1: to do. Yeah, I mean, the home ownership gap, at
Speaker 1: least for African Americans is larger now than it was
Speaker 1: fifty years ago and segregation was legal, which is quite saddening.
Speaker 1: But it's not just African Americans. And when you look
Speaker 1: at declines, whether you are a woman, whether you are
Speaker 1: a minority, whether you're part of the l p G,
Speaker 1: t Q plus community, it shows that there's a higher
Speaker 1: level of declines for these communities than there are for
Speaker 1: for or white males. So you know, in our perspective,
Speaker 1: there has to be a lot that needs to be
Speaker 1: done in terms of resetting, reconfiguring the system to make
Speaker 1: it more fair and eggable for all. So, if I
Speaker 1: understand you correctly, you're saying, basically, in the current system,
Speaker 1: white men have an easier time getting a mortgage than
Speaker 1: anybody else. Well, you said it, I'll just agree with it.
Speaker 1: I think you said it. I think if I understood
Speaker 1: you're correctly said, yeah, that that is what the data shows.
Speaker 1: It's not just my perspect that's what the data shows.
Speaker 1: Is so, and so, how are you trying to help
Speaker 1: fix that problem by turning everybody into corn? By turning
Speaker 1: everybody into corn? I like it what do you mean
Speaker 1: by that? Through the power of math, right, cryptography specifically,
Speaker 1: we are able to make everyone look the same and
Speaker 1: the underwriter just simply understands the fundamental attributes that ought
Speaker 1: to drive that approval disapproval decision. Right, in order to
Speaker 1: help us and also to help our government understand where
Speaker 1: these biases are coming from, our lenders are required to
Speaker 1: ask you what your raise, what your sex, even your age,
Speaker 1: right Like, all of this comes with with this application scenario.
Speaker 1: But does this information inadvertingly create the bias? Can we
Speaker 1: make everyone look the same and start to remove or
Speaker 1: better identify where these issues are sort of coming from.
Speaker 1: So you're trying to use technology to blind all the
Speaker 1: decision makers in the home loan process to race, ethnicity,
Speaker 1: genre specifically blockchain. There are three big tech ideas behind
Speaker 1: home lending pal at least three of 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 alone. 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: HUMMED 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, especially
Speaker 1: when talking 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 road map towards allowing you to achieve your goals,
Speaker 1: which is probably one of the most creative things that
Speaker 1: I've ever done personally. But it also requires you to
Speaker 1: be willing to accept constructive criticism from the people that
Speaker 1: are going to be using it up front, and a
Speaker 1: lot of what we're doing is really trying to find
Speaker 1: creative ways just to get them involved in that conversation,
Speaker 1: to say that, hey, you know, we are trying to
Speaker 1: build this to help you. Right now, there's about twenty
Speaker 1: one million mortgage ready millennials today that are qualified to
Speaker 1: buy alone, even though they're not trying. They just don't know.
Speaker 1: We're trying to bring greater trust and transparency to this process. Yeah,
Speaker 1: I guess from my perspective, beyond just simply understanding the
Speaker 1: technology and what it's able to do, I think it
Speaker 1: takes the will to go ahead and take on that
Speaker 1: complexity to try something new. We were child is constantly
Speaker 1: with why not a simpler solution? Right? But in reality
Speaker 1: the problem is much more complicated than the simplicity these
Speaker 1: forces wanted to bring it into the table. You have
Speaker 1: to have vision, you have to have a desire to
Speaker 1: want to make fundamental change. Yeah, new tech built on
Speaker 1: the old, broken processes doesn't allow for systemic change. You know,
Speaker 1: you 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 ticularly Stephen mentioned
Speaker 1: and wanted us to do is simpler. Yeah. So one
Speaker 1: of the ideas you guys have is that transparency can
Speaker 1: help reduce bias. So in what we are, you're 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: where we're really talking about it is just the natural
Speaker 1: features of the blockchain. Right. It's transparent because all participants
Speaker 1: within this framework have access to this decentralized ledger, So
Speaker 1: we 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 tends to form within certain systems. Um, we're
Speaker 1: not saying that we're going to remove all bias, but
Speaker 1: what we're saying is that we feel very confident that
Speaker 1: we'll be able to reduce it said nificantly without regulatory
Speaker 1: reinforcement by the simple nature of this technology stack that
Speaker 1: we're developing. So was there some moment when you guys
Speaker 1: had the like light bulb, the high idea that you
Speaker 1: could do this. The moment that made me realize that
Speaker 1: this was doable was when our first group of lenders invested.
Speaker 1: There was a group called the Mortgage Collaborative. They are
Speaker 1: a collection of about three hundred five lenders I believe
Speaker 1: across the country. They represent about the overall originations that
Speaker 1: happened in the US. When they kind of stepped in
Speaker 1: and we're like, hey, you know, we're gonna lead your
Speaker 1: your development before your Series A, We're going to try
Speaker 1: to help you there. I think that was the moment
Speaker 1: for for me and then we had shortly after that.
Speaker 1: Joining that round was a group called Quo Mutual or
Speaker 1: CMfg Adventures their discovery fund, and they are the one
Speaker 1: of the largest collections of credit unions in the industry. So,
Speaker 1: you know, typically you have an issue where you know,
Speaker 1: consumers feel like there's a problem that's not truly being solved.
Speaker 1: But to see that lenders were looking to try to
Speaker 1: find solutions like ours, I think that was the moment
Speaker 1: for me. They said, hey, you know this could be
Speaker 1: feasible for us, that the people who will actually have
Speaker 1: to work with you want to help you. Like, that's
Speaker 1: exactly great, but just tell me how will it work? Like,
Speaker 1: walk me through. I'm an ordinary person. I want to
Speaker 1: get alone. I come to home lending Pal. What happens
Speaker 1: when when you're fully you know, fully up and running.
Speaker 1: How's it gonna work? Yes, So you will spend about
Speaker 1: five minutes going through our onboarding process where you're connecting
Speaker 1: your online bank accounts, you're authorizing and soft FCO. Cool.
Speaker 1: There's a credit report basically a credit report. You're here.
Speaker 1: Most people don't realize so so lenders are utilizing your
Speaker 1: FICO scores and most of the places online that you're
Speaker 1: able to go to your showing a vantage score. So
Speaker 1: that's kind of the first level of disconnect and so
Speaker 1: we're solving for that first. So you go to that
Speaker 1: process and then after you signed up, our virtual assistant
Speaker 1: keV begins doing his he's analyzing your profile. Uh. He's
Speaker 1: really a geared towards helping you understand really three or
Speaker 1: four critical elements. You know, one your likelihood for success
Speaker 1: or approval to some financial modeling and forecasts and give
Speaker 1: you a better understanding of when you should begin the
Speaker 1: process to apply for for a home. So how long
Speaker 1: will it take you to become a homeowner or to
Speaker 1: close on the the home? Three the best loan product for you,
Speaker 1: and then for the lenders within our ecosystem, they present
Speaker 1: the best chance of success with them as well. So,
Speaker 1: so you mentioned a virtual advisor, keV virtual meaning it's
Speaker 1: not a guy named keV, right, it's it's named after
Speaker 1: one of my my good friends from college that passed
Speaker 1: from a rare form of germ sale cancer. He's probably
Speaker 1: one of the most helpful, friendly people that you've ever met,
Speaker 1: and it didn't matter who you were, So we really
Speaker 1: wanted to encompass his personality into the solution itself. But yes, keV,
Speaker 1: it becomes a friend pal. You know, so even if
Speaker 1: you're not ready to buy, he just doesn't pass you
Speaker 1: off and say hey, I'm not going to help. It
Speaker 1: really analyzes your ber 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 alan, and what loan makes the most sense
Speaker 1: for you, and how long the whole process is likely
Speaker 1: to take. You can ask keV questions and it will
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: asked questions link. Brian says he thinks a lot of
Speaker 1: people might be more comfortable talking with an AI powered
Speaker 1: virtual assistant then with a human loan officer at a bank.
Speaker 1: I think it really solves a cultural problem there. There
Speaker 1: are cultural barriers that prevent different segments from becoming homeowners
Speaker 1: or at least impact they're buying decisions in terms of
Speaker 1: how they explore homeownership. So the first part is to
Speaker 1: try to use this virtual assistant just to make them
Speaker 1: feel comfortable getting into the process of what homeownership could
Speaker 1: look like. And then from there it is about preparing them,
Speaker 1: getting them better qualify so that once they are ready
Speaker 1: to say, hey, I want to come home owner, I
Speaker 1: found the house that I love, allowing that transaction, that
Speaker 1: process to be a lot smoother and easier through the
Speaker 1: use of blockchain. Basically, so when you say cultural, I
Speaker 1: mean does that include in part race and ethnicity, people
Speaker 1: who have traditionally been excluded from the banking sector from housing.
Speaker 1: Is the dream that sort of AI can help people
Speaker 1: who have been excluded become more included. Yes, most white
Speaker 1: people have resources. They have other friends and family who
Speaker 1: have gone through this process successfully multiple times as opposed
Speaker 1: to just the one time. Within our communities, is difficult
Speaker 1: just to find the one person that you can discuss
Speaker 1: this process with, and most of the time that one
Speaker 1: person has gone through a negative experience in that right.
Speaker 1: Brian's parents have experienced difficulty in this instry. My parents
Speaker 1: have experienced difficulty in this process too. Isn't until you
Speaker 1: get to our generation where you have family members that
Speaker 1: have gone through this process multiple times and have been successful.
Speaker 1: So when we speak to keV being culturally relevant, it's
Speaker 1: because keV is there to provide you accurate support that
Speaker 1: historically hasn't been available to these marginalized groups. Stephen, you
Speaker 1: mentioned your own families, your and Brian's famili's experience with
Speaker 1: getting home loans with the banking system. Do you guys
Speaker 1: mind us talking about that specifically? What have been in
Speaker 1: your family's experiences with getting loan? Yeah? Yeah, I mean
Speaker 1: back in the subprime mortgage crisis, and you know, my
Speaker 1: mom nearly lost her dream home that I bought for
Speaker 1: those primarily because we were in an arm even though
Speaker 1: we should have been an av A loan because she
Speaker 1: has a military veteran and an armed an adjustable rate
Speaker 1: loans that was way worse than the mortgage. It was
Speaker 1: way worse. I mean, you know, it started out better
Speaker 1: just because you pay less, but once that interest rate flips,
Speaker 1: it becomes way worse if you're not prepared for it.
Speaker 1: And I think you know, again, when when we talk
Speaker 1: about these cultural factors, there's really five that you deal with.
Speaker 1: There's there's cultural itself. So things like the subprime mortage
Speaker 1: crisis where African Americans are hurt the most coming out
Speaker 1: of that, you have red lining, reverse rate lining, etcetera.
Speaker 1: Red Lining is basically the history of lenders not making
Speaker 1: loans to people in predominantly black neighborhoods. Essential exactly, we're
Speaker 1: picking in which areas they will lend to specific groups. Yes,
Speaker 1: and those areas were predominantly white historically hamper dom wa
Speaker 1: why Yes. So you have those elements. You have the
Speaker 1: economic elements where there's this concept of its just being
Speaker 1: unattainable for us. You have the psychological elements of being
Speaker 1: misunderstood thinking that the only way I can buy a
Speaker 1: home is having down to put down towards of down payment,
Speaker 1: and that's just not true. So our ultimate objective is
Speaker 1: just really to make that more attainable for for everyone.
Speaker 1: And it's really for all load of monitor income borrowers
Speaker 1: these days, just because with rates increasing, with the supply
Speaker 1: shortages that we have, you know, homeownership is really going
Speaker 1: to become a lot more difficult for a lot of people,
Speaker 1: regardless of their age, sex, and race. So you have
Speaker 1: this industry that suffers from a lack of transparency, from
Speaker 1: historical bias in terms of race and gender. You start
Speaker 1: this technology driven company to try and fix those things.
Speaker 1: As you're building the company, how do you come to
Speaker 1: work with IBM um our need for data protection and security?
Speaker 1: So you're talking about digitizing documents, digitizing information to allow
Speaker 1: greater access to underserved underrepresented groups. And IBM had their
Speaker 1: hyper Protect Accelerator which was entirely focused on that, taking
Speaker 1: small startups like ours and allowing them to basically run
Speaker 1: the palace that we ran without having to worry about
Speaker 1: people's information getting stolen in essence, and then Steve and
Speaker 1: I were just very aggressive in terms of just reaching
Speaker 1: out 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 in
Speaker 1: in essence, here's the third big tech idea in the
Speaker 1: home Lending Pal 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. IBMS 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 Pal 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 Pal 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 to do it on your own
Speaker 1: as well. So so people have to trust you to
Speaker 1: use home landing Piller right Like, I'm giving you everything,
Speaker 1: how do you can into me? How do you convince
Speaker 1: customers that you're going to keep their data safe absolutely. Um.
Speaker 1: Part of it is doing stuff like this where we're
Speaker 1: acknowledging and making the consumers aware of our relationship with
Speaker 1: IBM and how IBM is handling our storage of the
Speaker 1: data and the sensitive data itself. Technically, the IBM description
Speaker 1: of it is their confidential computing services or cloud services,
Speaker 1: and it's basically saying that even though the information is
Speaker 1: stored in the cloud, IBM is going to do a
Speaker 1: lot to help Home Lending Pal protect this sensitive data.
Speaker 1: Part of it is being able to show IBM s
Speaker 1: logo on our website. You'll you'll be surprised how much
Speaker 1: logo recognition helps people understand that this is a legit business,
Speaker 1: a legit company, if you will. And then there's also
Speaker 1: stuff like you know, people seeing the address of the business,
Speaker 1: contact information for the business, like all this stuff factors
Speaker 1: into why people will be willing to give us their data.
Speaker 1: But a lot of that is very contingental, just people
Speaker 1: seeing the IBM logo and saying that, hey, you know,
Speaker 1: we can if we don't trust Home Lending Path, we
Speaker 1: definitely trust IBM with this expect of the business. So
Speaker 1: what is the sort of story of 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 to build the thing
Speaker 1: you need? We told them what we wanted. I think
Speaker 1: there's a certain special relationship that we have with IBM.
Speaker 1: As I mentioned, Steve and I are are very aggressive
Speaker 1: of internally and externally in terms of getting things change
Speaker 1: in this industry, especially when talk about systemic change, and
Speaker 1: sometimes that requires you to make very big ask, you know,
Speaker 1: swing for the fences and see what happens. And as
Speaker 1: we found out more, as we we hired better talent,
Speaker 1: as we understood more of what we were trying to do,
Speaker 1: it made it a lot easier for us to really
Speaker 1: share this vision with IBM. And then now they're able
Speaker 1: to recommend products to say, we see you're trying to
Speaker 1: do it this way, but maybe you want to use
Speaker 1: our internal product and do it with this instead, And
Speaker 1: so that makes it a lot easier for us to
Speaker 1: try to bring artificial intelligence and blockchain to an industry
Speaker 1: that hasn't historically accepted new technology that well. So where
Speaker 1: are you in your journey as a company. I know
Speaker 1: you're still sort of working on it. What can customers
Speaker 1: do now with your product? They can get recommendations right now.
Speaker 1: We're fully licensed Colorado, Florida and in North Carolina, so
Speaker 1: right now, customers from those days can expect to be
Speaker 1: connected with the 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: Have any of your customers told you about how it's
Speaker 1: helped them. Surprisingly, a lot of our customers will reach
Speaker 1: out to us and give us use cases we've had
Speaker 1: local TV interviews, what they've interviewed them. Without those success
Speaker 1: stores will have customers that will reach out to us
Speaker 1: what challenges that they're having and hoping that we can
Speaker 1: help them through those, even if we have to manually
Speaker 1: connect the borrower to a lender and a state that
Speaker 1: we don't operate, and we're more than happy to do that.
Speaker 1: In exchange for that, they're basically helping us build out
Speaker 1: this new process, and so that's kind of the beauty
Speaker 1: of the system is that you know, customers are coming
Speaker 1: in at all stages of the buying cycle. You have
Speaker 1: some that are still renting at that day dreaming phase
Speaker 1: where they're really trying to understand, you know, is home
Speaker 1: ownership a feasible option for me? And you have some
Speaker 1: that are you know, trying to test out new features
Speaker 1: like optimal character recognition software where they're able to upload
Speaker 1: documents and see how those documents transferred to lenders. So
Speaker 1: I really think that is the beauty about what we're
Speaker 1: building is that the people have helped us build it
Speaker 1: so far. Are there any particular stories you've heard from
Speaker 1: customers that have stayed with you? Um? Honestly, I think
Speaker 1: the one that's most relevant to me, that sticks closest
Speaker 1: to my heart is my mom. You know, she was
Speaker 1: looking to try to buy another house. We were able
Speaker 1: to get her approved for a little bit over six
Speaker 1: D fifty thousand, which was about fifty thousand more than
Speaker 1: what she had heard from anyone else in the area. Uh, So,
Speaker 1: you know, we've really been excited, at least I had
Speaker 1: really been excited about that one. That's great, you know,
Speaker 1: to do better than your mom, right, that's the whole
Speaker 1: reason why I built the system, so you know, So
Speaker 1: that one really sticks closest to me is because, uh,
Speaker 1: we've asked some users that have gone through the entire
Speaker 1: process and have helped us go from our initial phase
Speaker 1: and we've really been launching in phases where at first
Speaker 1: it was more show just showing the affordability amount, like
Speaker 1: you know, what was the amount of home that you
Speaker 1: could afford. Now, as Steven mentioned, we're getting into this
Speaker 1: much more interactive, uh, conversational dialogue where consumers are not
Speaker 1: only showing kind of what they want to buy, but
Speaker 1: also getting into kind of what their feelings are, what
Speaker 1: is what are their sentiments that they're looking for in
Speaker 1: a potential relationship with they lender. Uh. So we're really
Speaker 1: excited when consumers come in and they test new features
Speaker 1: and they say, hey, this is working, great, this isn't working, Uh,
Speaker 1: you know what about this? And we think that's really
Speaker 1: going to lead into our series A raise here in
Speaker 1: the next couple of months, where we'll go out and
Speaker 1: raise hopefully eight thiggers or more to really flush out
Speaker 1: the features that consumers have said they wanted the most.
Speaker 1: Is really what we're most excited about. What's your dream
Speaker 1: for home landing Pale If you think whatever, I don't know.
Speaker 1: Five years in the future, ten years in the future,
Speaker 1: where are you. I want to see at least a
Speaker 1: million people, hopefully on a million minorities, become homeowners by
Speaker 1: utilizing our product. You know, we operate in an industry
Speaker 1: that's very lucrative for a lot of people. Having supported
Speaker 1: IBM will hopefully help us from a business perspective, But
Speaker 1: I don't want us to lose sight of our social
Speaker 1: impact goals and the things that we're really set out before,
Speaker 1: which was to make the process more agguiable for everyone.
Speaker 1: You know, I think if we were to be acquired
Speaker 1: or to to do an initial public offering in five
Speaker 1: years and we're not doing that, then for me it
Speaker 1: would not be as as sweet as if it were
Speaker 1: to ensure that we're actually doing stuff to close the
Speaker 1: gap for people. Thank you guys so much for your time.
Speaker 1: I really it was great to talk with you. Pleasure.
Speaker 1: Thank you absolutely. Malcolm glave all here to end today's show,
Speaker 1: I want to talk about someone who we didn't hear
Speaker 1: from in the interview, but who we heard about, Brian's mom,
Speaker 1: because her story really is the story of Home Lending Pall.
Speaker 1: Remember how Brian told us that back in the odds,
Speaker 1: his mom got that crappy mortgage, the one that left
Speaker 1: her paying higher interest rates than she should have been paying.
Speaker 1: That happened to a lot of people, particularly people of color.
Speaker 1: It was that story and others like it that really
Speaker 1: inspired Brian to team up with Stephen to build Home
Speaker 1: Lending Pall. They wanted to fix a home lending system
Speaker 1: that had been opaque and unfair basically forever. Most people
Speaker 1: applying for mortgages aren't thinking about the technology that's behind
Speaker 1: the scenes. We all just want a good mortgage with
Speaker 1: fair terms. And because Brian and Stephen made creative use
Speaker 1: of IBM technology using AI, blockchain and cloud to rethink
Speaker 1: the home loan process, that is now possible for all
Speaker 1: of us. On the next episode of Smart Talks with IBM,
Speaker 1: as AI becomes more widespread, how do we ensure that
Speaker 1: it is built and deployed responsibly, We talked with Pedra
Speaker 1: Bonadira's trustworthy AI practice leader within IBM Consulting. Smart Talks
Speaker 1: with IBM is produced by Molly Sosha, Alexandra Garraton, Royston
Speaker 1: Preserve and Edith Rousselo with Jacob Goldstein. We're edited by
Speaker 1: Jan Guerra. Our engineers are Jason Gambrel, Sarah Brogare and
Speaker 1: Ben Holliday. Theme song by Gramoscope. Special thanks to Colly
Speaker 1: Migliari and Kelly Kathy Callaghan and the eight Bar and
Speaker 1: IBM teams, as well as the Pushkin marketing team. Smart
Speaker 1: Talks with IBM is a production of Pushkin Industries and
Speaker 1: I Heart Media. To find more Pushkin podcasts, listen on
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Speaker 1: listen to podcasts. I'm Malcolm Gladwell. This is a paid
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