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