Latest / The Steve Harvey Morning Show / Home Lending Pal: Disrupting the Mortgage Industry with Cloud and AI

Transcript

Home Lending Pal: Disrupting the Mortgage Industry with Cloud and AI · The Steve Harvey Morning Show

0:01
Speaker 1: Hello, Hello, Welcome to a new season of Smart Talks
0:04
Speaker 1: with IBM, a podcast from Pushkin Industries, iHeartRadio and IBM.
0:10
Speaker 1: I'm Malcolm Gladwell. This season, we're talking to new creators,
0:15
Speaker 1: the developers, data scientists, CTOs, and other visionaries who are
0:20
Speaker 1: creatively applying technology in business to drive change. Channeling their
0:26
Speaker 1: knowledge and expertise, they're developing more creative and effective solutions
0:31
Speaker 1: no matter the industry. Our guests today are Brian Young
0:35
Speaker 1: and Stephen Better, co founders of home lending Pal. Home
0:40
Speaker 1: lending Pal is a member of the IBM hyper Protect Accelerator,
0:45
Speaker 1: an investment readiness and technical mentorship program that supports impact
0:50
Speaker 1: focused startups leveraging highly sensitive data. Their story is a
0:56
Speaker 1: perfect place to start our season. They recognize a profound problem,
1:02
Speaker 1: the horrible process of getting a home loan, especially if
1:05
Speaker 1: you're part of an underserved community, a process that, as
1:09
Speaker 1: you'll hear, is not only confusing and complex, but often
1:13
Speaker 1: deeply unfair. So Brian and Stephen teamed up to use
1:17
Speaker 1: technology to attack that problem in a bunch of creative ways.
1:22
Speaker 1: You'll hear how they're tapping into blockchain to make the
1:25
Speaker 1: home loan process more transparent and fair, using AI to
1:29
Speaker 1: help people learn how to qualify for a loan, and
1:32
Speaker 1: relying on IBM technology to store consumer's most sensitive information
1:38
Speaker 1: safely in the cloud. Brian and Stephen talked with Jacob Goldstein,
1:43
Speaker 1: host of the pushkin podcast What's Your Problem. Jacob has
1:47
Speaker 1: covered technology and business for over a decade, first at
1:51
Speaker 1: the Wall Street Journal, then at MPR. Now let's get
1:55
Speaker 1: into the interview.
2:04
Speaker 2: Let's start this like a brom com. How did you
2:08
Speaker 2: meet each other and decide to start a company together.
2:11
Speaker 3: Stephen was suposed to come to a bachelor party in
2:13
Speaker 3: Miami and didn't show up, and it broke my heart.
2:16
Speaker 4: There's more to the story than just simply that.
2:19
Speaker 3: One of my old employees introduced us. I've just left
2:22
Speaker 3: marketto They've been acquired for one point four billion, and
2:25
Speaker 3: I am, you know, living the Miami lifestyle. You know,
2:28
Speaker 3: I have a condo on the water and all the
2:31
Speaker 3: nice things that go with it. A guy named Michael
2:33
Speaker 3: Ramsey had asked me, you know, would I help him
2:37
Speaker 3: do mortgage lead generation? And I was like, you know, sure,
2:39
Speaker 3: I'm not doing anything else? Why not? And I meet Steven.
2:43
Speaker 3: He was in North Carolina.
2:45
Speaker 4: I left a pretty fruitful career in banking.
2:48
Speaker 2: I was an underwriter underwriting loans. Me it's basically deciding
2:52
Speaker 2: who should get a loan and at what interest rate?
2:54
Speaker 5: Right, absolutely due diligence, right, which is understanding whether or
2:59
Speaker 5: not this particular individual has the wherewithal to afford the mortgage,
3:05
Speaker 5: also the credit risks that individual presents.
3:09
Speaker 4: But there was this disconnect in that process.
3:13
Speaker 5: Where you have hidden action taking place on one side
3:16
Speaker 5: of the transaction, while you have another side of the
3:19
Speaker 5: transaction that tends to hide information.
3:22
Speaker 2: And just to be clear, it's the borrower who hides
3:26
Speaker 2: information and the bank that hides the action the lender.
3:30
Speaker 5: In most cases, but it's usually both sides of the negotias.
3:34
Speaker 2: Everybody's hiding stuff from everybody else, absolutely.
3:38
Speaker 4: And it's like sort of inadvertent as well too.
3:41
Speaker 5: Right in that process, certain things fall through the cracks,
3:44
Speaker 5: And you know, falling through the cracks means weeks without
3:47
Speaker 5: notification from a bar's perspective as.
3:50
Speaker 4: To whether or not you know this deal is moving forward.
3:53
Speaker 2: Okay, So the problem is a lack of information on
3:56
Speaker 2: both sides, and that winds up leading to bad outcomes.
3:59
Speaker 2: It winds up to long delays that are frustrating or
4:03
Speaker 2: scary for the borrower.
4:05
Speaker 3: Yes, a lot of consumers just don't have anywhere to go.
4:09
Speaker 3: If you go online, everything is too broad and generic,
4:12
Speaker 3: especially if you know you're not ready to buy at
4:13
Speaker 3: that moment. If you talk to a lender or relative,
4:16
Speaker 3: if you're not ready to buy at that moment, they'll
4:18
Speaker 3: help you, but it's not the same level of help,
4:20
Speaker 3: and you're not going to get that same level of
4:22
Speaker 3: support over months, because you know, buying house isn't like
4:25
Speaker 3: buying a piece of candy online. And so we really
4:27
Speaker 3: looked at, Okay, well, how can we give people this
4:30
Speaker 3: safe environment to go explore and understand what home ownership
4:34
Speaker 3: could look like for them based on their personal information.
4:36
Speaker 3: And that's kind of when I reached back out to
4:38
Speaker 3: Stephen around August of twenty seventeen and said, hey, you know,
4:42
Speaker 3: we need to do this together. You understand the back
4:45
Speaker 3: ends die from a lender underwriter's perspective, and I understand
4:48
Speaker 3: the plight of the consumers, and if we come together,
4:51
Speaker 3: this could be something that could be really unique. A
4:53
Speaker 3: capitalist solution to a social challenge is probably the best
4:56
Speaker 3: way to put it.
4:57
Speaker 2: So, Stephen, you're sort of coming from the banking side,
5:00
Speaker 2: and Brian, you're sort of coming from the tech side. Absolutely,
5:05
Speaker 2: what exactly is the problem that you guys are trying
5:07
Speaker 2: to solve when you start this company.
5:09
Speaker 3: In its simplest essence, is data democratization, the ability to
5:13
Speaker 3: take complex information and simplify so that someone that isn't
5:17
Speaker 3: an expert like Stephen can understand what's going on.
5:21
Speaker 2: And in this case specifically, what is the data that
5:24
Speaker 2: you're trying to democratize?
5:25
Speaker 3: Underwriting data, so the decision or the data that is
5:29
Speaker 3: utilized to determine whether or not you are approved or
5:32
Speaker 3: declined for a home loan.
5:34
Speaker 2: So right now, if I go apply for a loan,
5:37
Speaker 2: they approve me or they decline me. But do I
5:41
Speaker 2: know why not?
5:43
Speaker 3: Really? I mean, you get a letter of an adverse letter,
5:46
Speaker 3: but it's still very broad and generic. It doesn't really
5:48
Speaker 3: tell you what to focus on next, but you do
5:51
Speaker 3: have a very high level sense of why you're decline.
5:54
Speaker 5: Yeah, there's no true guidance from that point of rejection,
5:58
Speaker 5: right there's no fundamental understanding as to what could I
6:02
Speaker 5: have done better? And that's really what sets this platform
6:06
Speaker 5: apart and also why it's important how we're sort of
6:10
Speaker 5: reframing this data workflow.
6:12
Speaker 2: I want to get into the details of that, but
6:14
Speaker 2: just as we sort of understand the problem a little
6:17
Speaker 2: bit more, I mean, one piece of it that we
6:19
Speaker 2: haven't talked about is race and the home ownership gap.
6:23
Speaker 2: Can you guys talk a little bit about that and
6:25
Speaker 2: how it fits with what you're trying to do.
6:29
Speaker 3: Yeah, I mean, the home ownership gap, at least for
6:33
Speaker 3: African Americans is larger now than it was fifty years
6:36
Speaker 3: ago when segregation was legal, which is quite saddening. But
6:40
Speaker 3: it's not just African Americans. And when you look at declines,
6:44
Speaker 3: whether you are a woman, whether you are a minority,
6:48
Speaker 3: whether you're a part of the LBGTQ plus community, it
6:51
Speaker 3: shows that there's a higher level of declines for these
6:54
Speaker 3: communities than there are for older white males. So you know,
6:57
Speaker 3: in our perspective, there has to be a lot that
7:00
Speaker 3: needs to be done in terms of resetting reconfiguring the
7:03
Speaker 3: system to make it more fair and equitable for all.
7:06
Speaker 2: So, if I understand you correctly, you're saying, basically, in
7:10
Speaker 2: the current system, white men have an easier time getting
7:14
Speaker 2: a mortgage than anybody else.
7:17
Speaker 3: Well, you said it I'll just agree with it.
7:19
Speaker 2: I think you said it. I think if I understand
7:21
Speaker 2: you're correctly you said.
7:23
Speaker 3: That is what the data shows us, and it's not
7:25
Speaker 3: just my perspective, that's what the data shows us.
7:27
Speaker 2: So and so, how are you trying to help fix
7:31
Speaker 2: that problem.
7:33
Speaker 4: By turning everybody into corn?
7:36
Speaker 2: By turning everybody into corn? I like it, Yeah, what
7:39
Speaker 2: do you mean by that?
7:40
Speaker 5: Through the power of math, right, cryptography specifically, we are
7:43
Speaker 5: able to make everyone look the same and the underwriter
7:47
Speaker 5: just simply understands the fundamental attributes that ought to drive
7:52
Speaker 5: that approval disapproval decision. Right, in order to help us
7:56
Speaker 5: and also to help our government understand where these biases
8:00
Speaker 5: are coming from. Our lenders are required to ask you
8:04
Speaker 5: what your raise, what your sex, even your age, right, Like,
8:07
Speaker 5: all of this comes with this application scenario. But does
8:11
Speaker 5: this information inadvertently create the bias? Can we make everyone
8:17
Speaker 5: look the same and start to remove or better identify
8:22
Speaker 5: where these issues are sort of coming from.
8:25
Speaker 2: So you're trying to use technology to behind all the
8:29
Speaker 2: decision makers in the home loan process, to race, ethnicity.
8:33
Speaker 3: Genrector specifically blockchain.
8:36
Speaker 1: There are three big tech ideas behind home lending pal
8:40
Speaker 1: at least three that we're going to talk about today
8:43
Speaker 1: on the show, and blockchain is big tech idea number one.
8:47
Speaker 1: You may have heard of blockchain because it's the key
8:50
Speaker 1: idea behind cryptocurrency, but the idea of blockchain is bigger
8:55
Speaker 1: than just digital money and much more than just a
8:58
Speaker 1: new way to store information on the Internet. Blockchain is
9:02
Speaker 1: a shared, immutable ledger that facilitates the process of recording
9:07
Speaker 1: transactions and tracking assets in a business network. Brian and
9:13
Speaker 1: Stephen want to use blockchain to gather up the information
9:16
Speaker 1: on race and gender that's required by law without showing
9:19
Speaker 1: it to the lenders making the decisions about who gets
9:22
Speaker 1: a loan.
9:24
Speaker 3: Our argument, or our thesis is that with the leverage
9:28
Speaker 3: of a mutable ledger such as blockchain, you're able to
9:31
Speaker 3: still collect the information that is necessary for the Home
9:34
Speaker 3: Mortgage Disclosure Act or HUMDA, as Stephen was referring to.
9:38
Speaker 3: But then with a smart contract, you don't have to
9:40
Speaker 3: release that information, so after the decision, the approval of
9:43
Speaker 3: decline is made for the consumer.
9:45
Speaker 2: So you have this big idea for what you want
9:47
Speaker 2: to do as a business, what you want to do socially,
9:50
Speaker 2: but how do you make creative use of technology to
9:53
Speaker 2: do the thing you want to do, to make it real.
9:56
Speaker 3: You know, we're trying to build something that hasn't been
9:58
Speaker 3: done in the mortgage industry. When we talk about artificial
10:01
Speaker 3: intelligence and a virtual assistant, most people think of that
10:03
Speaker 3: as just a one way street. You know, we are
10:06
Speaker 3: trying to build this human like interaction where it is
10:09
Speaker 3: able to not only understand, but to respond, and then
10:13
Speaker 3: to leverage those responses and create a roadmap towards allowing
10:18
Speaker 3: you to achieve your goals, which is probably one of
10:20
Speaker 3: the most creative things that I've ever done personally. But
10:23
Speaker 3: it also requires you to be willing to accept constructive
10:27
Speaker 3: criticism from the people that are going to be using
10:29
Speaker 3: get up front, and a lot of what we're doing
10:32
Speaker 3: is really trying to find creative ways just to get
10:33
Speaker 3: them involved in that conversation to say that, hey, you know,
10:37
Speaker 3: we are trying to build this to help you. Right now,
10:39
Speaker 3: there's about twenty one million mortgage reading millennials to day
10:42
Speaker 3: that are qualified to buy a loan, even though they're
10:45
Speaker 3: not trying. They just don't know. We're trying to bring
10:48
Speaker 3: greater trust and transparency to this process.
10:50
Speaker 5: Yeah, I guess, from my perspective, beyond just simply understanding
10:54
Speaker 5: the technology and what it's able to do, I think
10:58
Speaker 5: it takes the will to go ahead and take on
11:02
Speaker 5: that complexity to try something new. We were challenged constantly
11:08
Speaker 5: with why not a simpler solution, right, But in reality
11:12
Speaker 5: the problem is much more complicated than the simplicity these
11:17
Speaker 5: forces wanted to bring into the table. You have to
11:20
Speaker 5: have vision, you have to have a desire to want
11:23
Speaker 5: to make fundamental change.
11:25
Speaker 3: Yeah, new tech built on old, broken processes doesn't allow
11:29
Speaker 3: for systemic change. You have to try to find ways
11:32
Speaker 3: to not only just to make it easier for people
11:35
Speaker 3: to connect to lenders, but at the core of what
11:38
Speaker 3: we were trying to build, we really wanted to address
11:40
Speaker 3: the systemic issues in the home buying process, and that
11:42
Speaker 3: required us to try something different basically, and I think
11:45
Speaker 3: that's the most creative thing you can do in an
11:47
Speaker 3: industry that typically, if Steven mentioned wanted us to do,
11:50
Speaker 3: is simpler.
11:51
Speaker 4: Yeah.
11:52
Speaker 2: So one of the ideas you guys have is that
11:55
Speaker 2: transparency can help reduce by us. In what way are
12:01
Speaker 2: you using technology to bring more transparency to the home
12:05
Speaker 2: buying process?
12:07
Speaker 5: When we speak of transparency, when we speak of trust,
12:11
Speaker 5: what we're really talking about is just the natural features
12:15
Speaker 5: of the blockchain.
12:17
Speaker 4: Right.
12:17
Speaker 5: It's transparent because all participants within this framework have access
12:22
Speaker 5: to this decentralized ledger, So we are all seeing how
12:28
Speaker 5: these pieces are sort of moving. Right, we're playing poker
12:31
Speaker 5: with our cards facing up. When we're speaking to trust, right,
12:36
Speaker 5: we're speaking of the immutability of this information, knowing that
12:40
Speaker 5: if an action is taken, it's there on the ledger
12:43
Speaker 5: and we can't just simply remove it. So these features
12:49
Speaker 5: lead to this forceful curing of certain biases that tend
12:55
Speaker 5: to form within certain systems. We're not saying that we're
12:59
Speaker 5: going to re move all bias, but what we're saying
13:03
Speaker 5: is that we feel very confident that we'll be able
13:05
Speaker 5: to reduce it significantly without regulatory reinforcement by the simple
13:12
Speaker 5: nature of this technology stack that we're developing.
13:16
Speaker 2: So, was there some moment when you guys had the
13:19
Speaker 2: like light bulb, the aha idea that you could do this.
13:23
Speaker 3: The moment that made me realize that this was doable
13:27
Speaker 3: was when our first group of lenders invested. There was
13:29
Speaker 3: a group called the Mortgage Collaborative. They are a collection
13:32
Speaker 3: of about three hundred and twenty five lenders. I believe
13:35
Speaker 3: across the country they represent about twenty five percent overall
13:38
Speaker 3: originations that happened in the US when they kind of
13:42
Speaker 3: stepped in and were like, hey, you know, we're going
13:43
Speaker 3: to lead you your development before your Series A, We're
13:46
Speaker 3: going to try to help you there, I think that
13:48
Speaker 3: was the moment for me. And then we had shortly
13:51
Speaker 3: after that. Joining that round was a group called Queno
13:53
Speaker 3: Mutual or CMfg Ventures their discovery fund, and they are
13:56
Speaker 3: the one of the largest collections of credit unis in
13:59
Speaker 3: the industry. So, you know, typically you have an issue
14:01
Speaker 3: where you know, consumers feel like there's a problem that's
14:04
Speaker 3: not truly being solved. But to see that lenders were
14:06
Speaker 3: looking to try to find solutions like ours, I think
14:09
Speaker 3: that was the IHA moment for me. They said, hey,
14:11
Speaker 3: you know this could be feasible for us.
14:13
Speaker 2: That the people who actually have to work with you
14:17
Speaker 2: want to help you. Like, that's exactly great, but just
14:21
Speaker 2: tell me how will it work? Like walk me through.
14:25
Speaker 2: I'm an ordinary person. I want to get a loan.
14:28
Speaker 2: I come to home lending POW. What happens when when
14:31
Speaker 2: you're fully you know, fully up and running. How's it
14:34
Speaker 2: going to work?
14:35
Speaker 3: Yeah, so you will spend about five minutes going through
14:38
Speaker 3: our onboarding process where you're connecting your online bank accounts,
14:41
Speaker 3: you're authorizing a soft fyco pool.
14:44
Speaker 2: There's a credit report, basically a credit report.
14:46
Speaker 3: Yeah, you're here. Most people don't realize. So, so lenders
14:49
Speaker 3: are utilizing your FICO scores and most of the places
14:52
Speaker 3: online that you're able to go to or showing vantage scores.
14:54
Speaker 3: So that's kind of the first level of disconnect and
14:57
Speaker 3: so we're solving for that first. So you go to
15:00
Speaker 3: that process and then after you signed up, our virtual
15:04
Speaker 3: assistant keV begins doing his work. He's analyzing your profile.
15:09
Speaker 3: He's really geared towards helping you understand really three or
15:13
Speaker 3: four critical elements. You know, One you're likelihood for success
15:17
Speaker 3: or approval to some financial modeling and forecast and give
15:21
Speaker 3: you a better understanding of when you should begin the
15:24
Speaker 3: process to apply for a home So how long will
15:26
Speaker 3: it take you to become a homeowner or to close
15:29
Speaker 3: on a home. Three, the best loan product for you
15:32
Speaker 3: and then for the lenders within our ecosystem that present
15:35
Speaker 3: the best chance of success with them as well.
15:37
Speaker 2: So you mentioned a virtual advisor keV virtual meaning it
15:44
Speaker 2: is not a guy named keV right.
15:47
Speaker 3: It's named after one of my good friends from college
15:50
Speaker 3: that passed from a rare form of germ sale cancer.
15:53
Speaker 3: He is probably one of the most helpful, friendly people
15:55
Speaker 3: that you've ever met, and it didn't matter who you were,
15:58
Speaker 3: so we really wanted to encompass his personality into the
16:01
Speaker 3: solution itself. But yes, keV it becomes a friend of pal,
16:07
Speaker 3: so even if you're not ready to buy, he just
16:09
Speaker 3: doesn't pass you off and say, hey, I'm not going
16:12
Speaker 3: to help. It really analyzes your profile and begins to
16:15
Speaker 3: create a path that you can follow to become a homeowner.
16:20
Speaker 1: We have arrived at big tech idea number two. keV
16:24
Speaker 1: the Virtual Assistant is built using powerful artificial intelligence tools.
16:30
Speaker 1: The AI takes the potential home buyer's information and runs
16:34
Speaker 1: it through algorithms that tell you things like how likely
16:38
Speaker 1: you are to get a loan, and what loan makes
16:40
Speaker 1: the most sense for you, and how long the whole
16:42
Speaker 1: process is likely to take. You can ask keV questions
16:46
Speaker 1: and it'll give you answers. But keV is more than
16:49
Speaker 1: your average responder chatbot. It speaks conversationally it knows who
16:55
Speaker 1: you are, understands your needs, and helps beyond just providing
16:59
Speaker 1: a frequently asked questions link. Brian says he thinks a
17:02
Speaker 1: lot of people might be more comfortable talking with an
17:05
Speaker 1: AI powered virtual assistant than with a human loan officer
17:09
Speaker 1: at a bank.
17:10
Speaker 3: I think it really solves a cultural problem. There are
17:13
Speaker 3: cultural barriers that prevent different segments from becoming homeowners or
17:17
Speaker 3: at least impact their buying decisions in terms of how
17:20
Speaker 3: they explore home ownership. So the first part is to
17:23
Speaker 3: try to use as virtual assistant just to make them
17:25
Speaker 3: feel comfortable getting into the process of what home ownership
17:29
Speaker 3: could look like. And then from there it is about
17:31
Speaker 3: preparing them, getting them better qualified so that once they
17:35
Speaker 3: are ready to say, hey, I want to come a homeowner,
17:37
Speaker 3: I found the house that I love, allowing that transaction
17:41
Speaker 3: that process to be a lot smoother and easier through
17:44
Speaker 3: the use of blockchain.
17:45
Speaker 2: Basically, so when you say cultural, I mean, does that
17:48
Speaker 2: include in part race and ethnicity, people who have traditionally
17:53
Speaker 2: been excluded from the banking sector, from housing. I mean
17:58
Speaker 2: is the dream that sort of AI I can help
18:00
Speaker 2: people who've been excluded become more included.
18:04
Speaker 4: Yes, most white people have resources.
18:07
Speaker 5: They have other friends and family who have gone through
18:10
Speaker 5: this process successfully multiple times as opposed to just the
18:14
Speaker 5: one time. Within our communities, it's difficult just to find
18:17
Speaker 5: the one person that you can discuss this process with,
18:21
Speaker 5: and most of the time that one person has gone
18:23
Speaker 5: through a negative experience in that right, Brian's parents, I
18:27
Speaker 5: have experienced difficulty in the since my parents have experienced
18:31
Speaker 5: difficulty in this process too. Isn't until you get to
18:34
Speaker 5: our generation where you have family members that have gone
18:38
Speaker 5: through this process multiple times and have been successful. So
18:42
Speaker 5: when we speak to keV being culturally relevant, it's because
18:47
Speaker 5: keV is there to provide you accurate support that historically
18:52
Speaker 5: hasn't been available to these marginalized groups.
18:56
Speaker 2: Stephen, you mentioned your own families, your and Brian's families
19:00
Speaker 2: experience with getting whole loans with the banking system. Do
19:04
Speaker 2: you guys mind just talking about that specifically? What have
19:06
Speaker 2: been your family's experiences with getting loans?
19:10
Speaker 3: Yeah? Please? Yeah? Yeah, I mean back in the subprime
19:14
Speaker 3: mortgage crisis, and you know, my mom nearly lost her
19:17
Speaker 3: dream home that I thought for. It was primarily because
19:19
Speaker 3: we were in an arm even though we should have
19:21
Speaker 3: been in a VA loan because she was a military veteran.
19:23
Speaker 2: An un arm an adjustable rate loans. Somebody sold a
19:27
Speaker 2: loan that was way worse than the mortgage, So it's
19:29
Speaker 2: way worse.
19:29
Speaker 3: I mean, you know, it started out better just because
19:31
Speaker 3: you pay less, but once that interest rate flips, it
19:33
Speaker 3: becomes way worse if you're not prepared for it. And
19:36
Speaker 3: I think you know, again, when we talk about these
19:39
Speaker 3: cultural factors, there's really five that you deal with. There's
19:41
Speaker 3: there's cultural itself. So things like the subprime mortgage crisis,
19:45
Speaker 3: where African Americans are hurt the most coming out of that,
19:48
Speaker 3: you have redlining, reverse redlining, et cetera.
19:52
Speaker 2: Red Lining is basically the history of lenders not making
19:56
Speaker 2: loans to people in predominantly black neighborhoods.
19:58
Speaker 5: Essentially exactly, we're picking which areas they will lend to
20:02
Speaker 5: specific groups.
20:03
Speaker 2: Yes, and those areas were predominantly white historically.
20:06
Speaker 3: And predominantly white yes. So you have those elements. You
20:10
Speaker 3: have the economic elements where there's this concept of it's
20:13
Speaker 3: just being unattainable for us. You have the psychological elements
20:17
Speaker 3: of being misunderstood thinking that the only way I can
20:20
Speaker 3: buy a home is having twenty percent down to put
20:22
Speaker 3: down towards the down payment, and that's just not true.
20:26
Speaker 3: So our ultimate objective is just really to make that
20:29
Speaker 3: more attainable for everyone, and it's really for all load
20:31
Speaker 3: of modern income borrowers these days, just because with rates increasing,
20:35
Speaker 3: with the supply shortages that we have, you know, home
20:38
Speaker 3: ownership is really going to become a lot more difficult
20:40
Speaker 3: for a lot of people, regardless of their age, sex,
20:43
Speaker 3: and race.
20:45
Speaker 2: So you have this industry that suffers from a lack
20:47
Speaker 2: of transparency, from historical bias in terms of race and gender.
20:51
Speaker 2: You start this technology driven company to try and fix
20:54
Speaker 2: those things. As you're building the company, how do you
20:56
Speaker 2: come to work with IBM.
21:00
Speaker 3: Our need for data protection and security? So you're talking
21:03
Speaker 3: about digitizing documents, digitizing information to allow greater access to underserved,
21:10
Speaker 3: underrepresented groups. And IBM had their Hyperprotect Accelerator which was
21:15
Speaker 3: entirely focused on that, taking small startups like ours and
21:19
Speaker 3: allowing them to basically run the pilots that we ran
21:23
Speaker 3: without having to worry about people's information getting stolen in essence,
21:27
Speaker 3: and then Steve and I were just very aggressive in
21:29
Speaker 3: terms of just reaching out to different vps, different executives
21:33
Speaker 3: at IBM kind of saying, you know, here's what we
21:35
Speaker 3: want to do, here's what we need, will you help us,
21:39
Speaker 3: And being in an industry that is so regulated, it
21:42
Speaker 3: helped us really get through that door, just because you know,
21:45
Speaker 3: every bank has a vendor on boarding process that requires
21:49
Speaker 3: a very high level of data security to even work
21:53
Speaker 3: with them.
21:53
Speaker 1: In an essence, here's the third big tech idea in
21:58
Speaker 1: the Home Lending Pulse, protecting data in the cloud. Think
22:04
Speaker 1: about the problem this one is solving. Brian and Stephen
22:07
Speaker 1: have this little startup. They need to collect super sensitive
22:10
Speaker 1: data from people. Everything you have to show the bank
22:13
Speaker 1: when you want to get a mortgage, This data has
22:16
Speaker 1: to be secure. IBM's hyper Protect Accelerator enables small businesses
22:21
Speaker 1: to store sensitive data in the cloud and keep that
22:25
Speaker 1: data secure. Brian says, it lets home Lending Pal do
22:29
Speaker 1: something they would never do on their own.
22:32
Speaker 3: From a technical perspective, you have different compliance checks that
22:36
Speaker 3: you have to meet to work with banking institutions or
22:38
Speaker 3: financial institutions. So it allows a small startup like home
22:42
Speaker 3: Lending Pal to still be able to meet those checks
22:45
Speaker 3: and balances. To bring an innovative solution to the table
22:49
Speaker 3: for a financial institution, where more than likely as a startup,
22:52
Speaker 3: you're not going to have the ability to do that
22:54
Speaker 3: on your own, just because it is so expensive to
22:56
Speaker 3: either have internal servers or to try to do it
22:59
Speaker 3: on your own as well.
23:00
Speaker 2: So people have to trust you to use home Landing
23:03
Speaker 2: pow right, Like, I'm giving you everything, how do you
23:06
Speaker 2: convince me? How do you convince customers that you're going
23:09
Speaker 2: to keep their data safe?
23:11
Speaker 3: Absolutely? Part of it is doing stuff like this where
23:15
Speaker 3: we're acknowledging and making the consumers aware of our relationship
23:18
Speaker 3: with the IBM and how IBM is handling our storage
23:21
Speaker 3: of the data and the sensitive data itself. Technically, the
23:24
Speaker 3: IBM description of it is their confidential computing services or
23:28
Speaker 3: cloud services, and it's basically saying that even though the
23:31
Speaker 3: information is stored in the cloud, IBM is going to
23:34
Speaker 3: do a lot to help Home Lending Pal protect this
23:36
Speaker 3: sensitive data. Part of it is being able to show
23:39
Speaker 3: IBM's logo on our website. You'll be surprised how much
23:42
Speaker 3: logo recognition helps people understand that this is a legit business,
23:46
Speaker 3: a legit company, if you will. And then there's also
23:49
Speaker 3: stuff like you know, people seeing the address of the business,
23:53
Speaker 3: contact information for the business. Like, all this stuff factors
23:56
Speaker 3: into why people would be willing to give us their data.
23:59
Speaker 3: But a lot of that is very contingent on just
24:00
Speaker 3: people seeing the IBM logo and saying that, hey, you
24:03
Speaker 3: know we can if we don't trust home Lending Pod,
24:05
Speaker 3: we definitely trust IBM with this aspect of the business.
24:08
Speaker 2: So what is the sort of story of working with
24:11
Speaker 2: IBM on this? I mean, did you just figure out
24:14
Speaker 2: that they had the thing you need? Or did they
24:16
Speaker 2: sort of work with you to build the thing you need?
24:20
Speaker 3: We told them what we wanted. I think there's a
24:24
Speaker 3: certain special relationship that we have with IBM. As I mentioned,
24:28
Speaker 3: you know, Steve and I are very aggressive of internally
24:32
Speaker 3: and externally in terms of getting things changed in this industry,
24:34
Speaker 3: especially when we talk about systemic change, and sometimes that
24:38
Speaker 3: requires you to make very big ask, you know, swing
24:41
Speaker 3: for the fences and see what happens. And as we
24:44
Speaker 3: found out more, as we hired better talent, as we
24:47
Speaker 3: understood more of what we were trying to do, it
24:50
Speaker 3: made it a lot easier for us to really share
24:51
Speaker 3: this vision with IBM. And then now they're able to
24:55
Speaker 3: recommend products to say, we see you're trying to do
24:58
Speaker 3: it this way, but maybe you want to use our
25:00
Speaker 3: internal product and do it with this instead, And so
25:02
Speaker 3: that makes it a lot easier for us to try
25:03
Speaker 3: to bring artificial intelligence and blockchain to an industry that
25:07
Speaker 3: hasn't historically accepted new technology that well.
25:11
Speaker 2: So where are you in your journey as a company.
25:14
Speaker 2: I know you're still sort of working on it. What
25:17
Speaker 2: can customers do now with your product?
25:21
Speaker 5: They can get recommendations right now, we're fully licensed Colorado,
25:27
Speaker 5: Florida and in North Carolina, So right now, customers from
25:31
Speaker 5: those dates can expect to be connected with a lender
25:34
Speaker 5: with full guidance as to what exactly they're getting into
25:39
Speaker 5: and what pricing expectations they ought to be presented with.
25:44
Speaker 2: Have you heard back? I mean, I know that this
25:47
Speaker 2: is kind of a weird question, given that the whole
25:49
Speaker 2: point is that people can be anonymized, But are you
25:52
Speaker 2: able to talk to your customers? Haven of your customers
25:56
Speaker 2: told you about how it's helped them.
25:58
Speaker 3: Surprisingly, a lot of our customers will reach out to
26:02
Speaker 3: us and give us the use cases. We've had local
26:04
Speaker 3: TV interviews where they've interviewed them about those success stories.
26:07
Speaker 3: We'll have customers that reach out to us with challenges
26:10
Speaker 3: that they're having and hoping that we can help them
26:11
Speaker 3: through those, even if we have to manually connect a
26:14
Speaker 3: borrower to a lender and a state that we don't operate,
26:17
Speaker 3: and we're more than happy to do that. In exchange
26:20
Speaker 3: for that, they're basically helping us build out this new process.
26:23
Speaker 3: And so that's kind of the beauty of the system
26:25
Speaker 3: is that you customers are coming in at all stages
26:28
Speaker 3: of the buying cycle. You have some that are still renting,
26:32
Speaker 3: at that daydreaming phase where they're really trying to understand,
26:35
Speaker 3: you know, is home ownership a feasible option for me?
26:37
Speaker 3: And then you have some that are, you know, trying
26:39
Speaker 3: to test out new features like optimal character recognition software
26:42
Speaker 3: where they're able to upload documents and see how those
26:44
Speaker 3: documents transfer to lenders. So I really think that is
26:48
Speaker 3: the beauty about what we're building, is that the people
26:51
Speaker 3: have helped us build it so far.
26:53
Speaker 2: Are there any particular stories you've heard from customers that
26:56
Speaker 2: have stayed with you.
27:00
Speaker 3: I think the one that's most relevant to me, that
27:02
Speaker 3: sticks closest to my heart is my mom. You know,
27:04
Speaker 3: she was looking to try to buy another house, and
27:06
Speaker 3: we were able to get her approved for a little
27:09
Speaker 3: bit over six hundred and fifty thousand, which was about
27:11
Speaker 3: fifty thousand more than what she had heard from anyone
27:13
Speaker 3: else in the area. So, you know, we've really been excited.
27:16
Speaker 3: At least I had really been excited about that one.
27:19
Speaker 2: That's great. You're not going to do better than your mom.
27:21
Speaker 3: Right your mom me. That was the whole reason I
27:24
Speaker 3: built the systems, you know, So that one really sticks
27:26
Speaker 3: closest to me is because we've had some users that
27:29
Speaker 3: have gone through the entire process and have helped us
27:32
Speaker 3: go from our initial phase and we've really been launching
27:35
Speaker 3: in phases where at first it was more show just
27:38
Speaker 3: showing the affordability amount, like you know, what was the
27:40
Speaker 3: amount of home that you could afford? And now, as
27:42
Speaker 3: Steven mentioned, we're getting into this much more interactive conversational
27:47
Speaker 3: dialogue where consumers are not only showing kind of what
27:50
Speaker 3: they want to buy, but also getting into kind of
27:52
Speaker 3: what their feelings are, what is what are their sentiments
27:55
Speaker 3: that they're looking for in a potential relationship with they lender.
27:58
Speaker 3: So we're really excited and consumers come in and they
28:01
Speaker 3: test new features and they say, hey, this is working, great,
28:04
Speaker 3: this isn't working you know what about this? And we
28:07
Speaker 3: think that's really going to lead into our series A
28:09
Speaker 3: raise here in the next couple of months, where we'll
28:11
Speaker 3: go out and raise hopefully eight figures are more to
28:13
Speaker 3: really flush out the features that consumers have said they
28:16
Speaker 3: wanted the most. Is really what we're most excited about.
28:20
Speaker 2: What's your dream for home lending pal If you think whatever,
28:23
Speaker 2: I don't know, five years in the future, ten years
28:25
Speaker 2: in the future, where are you.
28:27
Speaker 3: I want to see at least a million people, hopefully
28:29
Speaker 3: a million minorities become homeowners by utilizing our product. You know,
28:33
Speaker 3: we operate in an industry that's very lucrative for a
28:36
Speaker 3: lot of people. Having supportive IBM will hopefully help us
28:39
Speaker 3: from a business perspective. But I don't want us to
28:42
Speaker 3: lose sight of our social impact goals and the things
28:44
Speaker 3: that were really set out before, which was to make
28:47
Speaker 3: the process more agguitable for everyone. You know, I think
28:49
Speaker 3: if we were to be acquired or to do an
28:52
Speaker 3: initial public offering in five years and we're not doing that,
28:55
Speaker 3: then for me, it would not be as sweet as
28:57
Speaker 3: if it were. To ensure that we're actually doing stuff
29:00
Speaker 3: to close the gap for people. Thank you guys so
29:03
Speaker 3: much for your time. I really it was great to
29:05
Speaker 3: talk with you.
29:07
Speaker 4: Pleasure Well, thank you absolutely.
29:15
Speaker 1: Malcolm Glavell. Here to end today's show, I want to
29:18
Speaker 1: talk about someone who we didn't hear from in the interview,
29:22
Speaker 1: but who we heard about, Brian's mom, because her story
29:27
Speaker 1: really is the story of home Lending Pal. Remember how
29:30
Speaker 1: Brian told us that back in the odds, his mom
29:33
Speaker 1: got that crappy mortgage, the one that left her paying
29:36
Speaker 1: higher interest rates than she should have been paying. That
29:39
Speaker 1: happened to a lot of people, particularly people of color.
29:43
Speaker 1: It was that story and others like it that really
29:46
Speaker 1: inspired Brian to team up with Stephen to build Home
29:50
Speaker 1: Lending Pal. They wanted to fix a home lending system
29:54
Speaker 1: that had been opaque and unfair basically forever. Most people
29:59
Speaker 1: applying for mortgage aren't thinking about the technology that's behind
30:02
Speaker 1: the scenes. We all just want a good mortgage with
30:05
Speaker 1: fair terms. And because Brian and Stephen made creative use
30:10
Speaker 1: of IBM technology using AI, blockchain, and cloud to rethink
30:16
Speaker 1: the home loan process, that is now possible for all
30:20
Speaker 1: of us. On the next episode of Smart Talks with IBM.
30:26
Speaker 1: As AI becomes more widespread, how do we ensure that
30:30
Speaker 1: it is built and deployed responsibly? We talk with Phedro Bonadius,
30:35
Speaker 1: trustworthy AI practice leader within IBM Consulting. Smart Talks with
30:42
Speaker 1: IBM is produced by Molly Sosia, Alexandra Garatin, Royston Preserve
30:48
Speaker 1: and Edith Russolo with Jacob Goldstein. We're edited by Jen Guera.
30:53
Speaker 1: Our engineers are Jason Gambrel, Sarah Buguier and Ben Tolliday.
30:58
Speaker 1: Theme song by Gramoscope. Special thanks to Carli Migliori and
31:02
Speaker 1: e Kelly, Kathy Callahan and the eight Bar and I
31:06
Speaker 1: b M teams, as well as the Pushkin marketing team.
31:10
Speaker 1: Smart Talks with I b M is a production of
31:12
Speaker 1: Pushkin Industries and iHeartMedia. To find more Pushkin podcasts, listen
31:17
Speaker 1: on the iHeartRadio app, Apple Podcasts, or wherever you listen
31:22
Speaker 1: to podcasts. I'm Malcolm Gladwell. This is a paid advertisement
31:27
Speaker 1: from ib M.