May 31, 2022 • 32 mins
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 (00:04):
Welcome to tex Stuff, a production from I Heart Radio.
This season of Smart Talks with IBM is all about
new creators, the developers, data scientists, c t o s
and other visionaries creatively applying technology in business to drive change.
They use their knowledge and creativity to develop better ways
(00:26):
of working, no matter the industry. Join hosts from your
favorite Pushkin Industries podcasts as they use their expertise to
deepen these conversations, and of course Malcolm Gladwell will guide
you through the season as your host and provide his
thoughts and analysis along the way. Look out for new
episodes of Smart Talks with IBM on the I Heart
Radio app, Apple Podcasts, or wherever you get your podcasts,
(00:49):
and learn more at IBM dot com slash smart talks. Hello, Hello,
welcome to a new season of Smart Talks with IBM,
a podcast from pushed In Industries, I Heart Radio and IBM.
I'm Malcolm Gladwell. This season we're talking to new creators,
the developers, data scientists, c t o s and other
(01:12):
visionaries who are creatively applying technology in business to drive change.
Channeling their knowledge and expertise, 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.
(01:33):
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 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,
(01:58):
especially if you're part of an unders serve community, a
process that, as you'll hear, is not only confusing and complex,
but often deeply unfair. 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,
(02:21):
using AI to help people learn how to qualify for
a loan, and relying on IBM technology to store consumers
most sensitive information safely 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
(02:42):
a decade, first at The Wall Street Journal, then at MPR.
Now let's get into the interview. Let's start this like
a brom car How did you meet each other and
decided to start a company together. Steven was supposed to
(03:05):
come to a bachelor party in Miami and didn't show up,
and it broke my heart. There's more to the story
than just simply that one of my old employees introduced us.
I've just left Marcato. They've been acquired for one point
for a billion, and I am, you know, living the
Miami lifestyle. You know, I have a condo on the
water and all the nice things to go with. A
(03:25):
guy named Michael Ramsey had asked me, you know what,
I helped him do mortgage lead generation and I was like,
you know, sure, I'm not doing anything else? Why not?
And I meet Steve that he was in North Carolina.
I left a pretty fruitful career in banking. I was
an underwriter. Underwriting loans mean it's basically deciding who should
(03:46):
get a loan and at what interest rate? Right, Absolutely
due diligence, right, which is understanding whether or not this
particular individual has the worldwithal to afford the mortgage. Also
the credit risk individual presents. But there was this disconnect
in that process where you have hidden action taking place
(04:09):
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 this is usually both sides of
the negotia. Everybody's hiding stuff from everybody else. There's a
(04:30):
absolutely and it's like sort of inadvertent as well too,
right in that process, and 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 both sides,
and that winds up leading to bad outcomes. It winds
(04:52):
up leading to long delays that are frustrating or scary
for the for the borrower, yes, who are 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 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
(05:14):
gonna get that same level of support over months because
you know, buying houses and 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
I reach back out to Stephen around August of two
thousand seventeen and said, hey, you know, we need to
(05:35):
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,
you're sort of coming from the banking side, and Brian
you're sort of coming from the tech side. Absolutely, what
(05:58):
exactly is the problem that you've got us are trying
to solve when you start this company, and its simple
assessence is data democratization, the ability to take complex information
and simplify so that someone that isn't an expert like
Stephen can understand what's going on, and in this case specifically,
what is the data that you're trying to democratize underwriting data,
(06:20):
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.
But do I know why not? Really? I mean, you
get a letter of an adverse letter, but it's still
very broad Engineeric, it doesn't really tell you what to
(06:42):
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? And
that's really what sets this platform apart and all. So
why it's important how we're sort of reframing of this
(07:04):
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 how it fits with with what you're trying
to do. Yeah, I mean, the home ownership gap, at
(07:25):
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, whether you're part of the l p G,
t Q plus community, it shows that there's a higher
(07:46):
level of declines for these communities than there are for
for or white males. So you know, in 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 eggable for all. So, if I
understand you correctly, you're saying, basically, in the current system,
white men have an easier time getting a mortgage than
(08:08):
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 said, yeah, that that is what the data shows.
It's not just my perspect that's what the data shows.
Is so, and so, how are you trying to help
fix that problem by turning everybody into corn? By turning
(08:30):
everybody into corn? I 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
(08:52):
these biases 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 remove or
(09:14):
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,
genre specifically blockchain. There are three big tech ideas behind
home lending pal at least three of that we're going
(09:35):
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
(09:58):
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
(10:19):
thesis is that with the leverage of a mutable ledger
such as blockchain, you're able to still collect the information
that is necessary for the Home Mortgage Disclosure Act or
HUMMED 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
(10:40):
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 talking about artificial intelligence and a virtual assistant. Most
people think of that it's just a one way street.
You know, we are trying to build this human like
(11:01):
interaction where it is able to not only understand, but
to respond, and then to leverage those responses and create
a road map 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
are going to be using it up front, and a
(11:24):
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 ready millennials today that are qualified to
buy alone, even though they're not trying. They just don't know.
We're trying to bring greater trust and transparency to this process. Yeah,
(11:44):
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. We were child is constantly
with why not a simpler solution? Right? But in reality
(12:05):
the problem is much more complicated than the simplicity these
forces wanted to bring it 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
the old, broken processes doesn't allow for systemic change. You know,
you have to try to find ways to not only
(12:27):
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 ticularly Stephen mentioned
and wanted us to do is simpler. Yeah. So one
of the ideas you guys have is that transparency can
(12:50):
help reduce bias. So in what we are, you're 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 it is just the natural
features of the blockchain. Right. It's transparent because all participants
(13:13):
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 is taken, it's
(13:36):
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 feel very confident that
(13:58):
we'll be able to reduce it said nificantly 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 light bulb, the high idea that you
could do this. The moment that made me realize that
(14:19):
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 the overall originations that
happened in the US. When they kind of stepped in
and we're like, hey, you know, we're gonna lead your
your development before your Series A, We're going to try
(14:40):
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 Quo Mutual or
CMfg Adventures their discovery fund, and they are the one
of the largest collections of credit unions 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
(15:00):
find solutions like ours, I think that was the 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
(15:24):
when when you're fully you know, fully up and running.
How's it gonna work? Yes, So you will spend about
five minutes going through our onboarding process where you're connecting
your online bank accounts, you're authorizing and soft FCO. Cool.
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
(15:46):
able to go to your showing a vantage score. So
that's kind of the first level of disconnect and so
we're solving for that first. So you go to that
process and then after you signed up, our virtual assistant
keV begins doing his he's analyzing your profile. Uh. He's
really a geared towards helping you understand really three or
(16:06):
four critical elements. You know, one 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 the home? Three the best loan product for you,
and then for the lenders within our ecosystem, they present
(16:28):
the best chance of success with 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. He's probably
one of the most helpful, friendly people that you've ever met,
(16:49):
and it didn't matter who you were, So we really
wanted to encompass his personality into the solution itself. But yes, keV,
it becomes a friend 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 ber profile and begins to create a
path that you can follow to become a homeowner. We
(17:13):
have 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 alan, and what loan makes the most sense
(17:34):
for you, and how long the whole process is likely
to take. You can ask keV questions and it will
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 beyond just providing a frequently
asked questions link. Brian says he thinks a lot of
(17:55):
people might be 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
(18:16):
try to use this virtual assistant just to make them
feel comfortable getting into the process of what homeownership could
look like. And then from there 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
(18:37):
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.
Is the dream that sort of AI can help people
who have been excluded become more included. Yes, most white
(18:58):
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 that right.
(19:20):
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 accurate support that
(19:45):
historically hasn't been available to these marginalized groups. Stephen, you
mentioned your own families, your and Brian's famili's experience with
getting home loans with the banking system. Do you guys
mind us talking about that specifically? What have been in
your family's experiences with getting loan? Yeah? Yeah, I mean
(20:06):
back in the subprime mortgage crisis, and you know, my
mom nearly lost her dream home that I bought for
those primarily because we were in an arm even though
we should have been an av A loan because she
has a military veteran and an armed an adjustable rate
loans that was way worse than the mortgage. It was
way worse. I mean, you know, it started out better
just because you pay less, but once that interest rate flips,
it becomes way worse if you're not prepared for it.
(20:28):
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 rate lining, etcetera.
Red Lining is basically the history of lenders not making
(20:49):
loans to people in predominantly black neighborhoods. Essential exactly, we're
picking in which areas they will lend to specific groups. Yes,
and those areas were predominantly white historically hamper dom wa
why Yes. So you have those elements. You have the
economic elements where there's this concept of its just being
unattainable for us. You have the psychological elements of being
(21:11):
misunderstood thinking that the only way I can 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 monitor income borrowers
these days, just because with rates increasing, with the supply
shortages that we have, you know, homeownership is really going
(21:32):
to become a lot more difficult for 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 IBM um our need for data protection and security?
(21:55):
So you're talking about digitizing documents, 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 palace that we ran without having to worry about
(22:17):
people's information getting stolen in essence, 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
(22:39):
a vendor on boarding process that requires 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 Pal story protecting data in the cloud. Think
about the problem this one is solving. Brian and Stephen
(23:00):
have this little startup. They need to collect supersensitive data
from people. Everything you have to show 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
(23:23):
would never do on their own. From a technical perspective,
you have different compliance checks that 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,
(23:44):
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 either have internal
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 can into me? How do you convince
customers that you're going to keep their data safe absolutely. Um.
(24:06):
Part of it is doing stuff like this where we're
acknowledging and making the consumers aware 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
(24:27):
lot to help Home Lending Pal protect this sensitive data.
Part of it is being able to show IBM s
logo on our website. You'll you'll 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
(24:49):
into why people will be willing to give us their data.
But a lot of that is very contingental, just people
seeing the IBM logo and saying that, hey, you know,
we can if we don't trust Home Lending Path, we
definitely trust IBM with this expect 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
(25:11):
you need? We 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
of internally and externally in terms of getting things change
in this industry, especially when talk about systemic change, and
sometimes that requires you to make very big ask, you know,
(25:33):
swing for the 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're trying to
do it this way, but maybe you want to use
our internal product and do it with this instead, And
(25:55):
so that makes it 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 can get recommendations right now.
(26:17):
We're fully licensed Colorado, 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 ought
to be presented with. Have you heard back? I mean,
(26:39):
I know that this is 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 any of your customers told you about how it's
helped them. Surprisingly, a lot of our customers will reach
out to us and give us use cases we've had
local TV interviews, what they've interviewed them. Without those success
(26:59):
stores will have customers that will reach out to us
what challenges that they're having and hoping that we can
help them through those, even if we have to manually
connect the 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
(27:20):
in at all stages of 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 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
(27:40):
I really think that is the beauty about what we're
building is that the people 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. We were able
(28:00):
to get her approved for a little bit over six
D fifty thousand, which was about fifty 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 know,
to do better than your mom, right, that's the whole
reason why I built the system, so you know, So
that one really sticks closest to me is because, uh,
(28:21):
we've asked some users that have gone through the entire
process and have helped 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. Now, as Steven mentioned, we're getting into this
much more interactive, uh, conversational dialogue where consumers are not
(28:42):
only showing kind of what they want to buy, but
also getting into kind of what their feelings are, what
is what are their sentiments that they're looking for in
a potential relationship with they lender. Uh. 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
(29:03):
the next couple of months, where we'll go out and
raise hopefully eight thiggers or more to really flush out
the features 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 on a million minorities, become homeowners by
(29:24):
utilizing our product. You know, we operate in an industry
that's very lucrative 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 agguiable for everyone.
You know, I think if we were to be acquired
or to to do an initial public offering in five
(29:47):
years and we're not doing that, then for me it
would not 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 absolutely. Malcolm glave all here to end today's show,
(30:11):
I want 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.
(30:32):
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
(30:55):
the scenes. We all just want a good mortgage with
fair terms. And because Brian and Stephen made creative use
of IBM 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 with IBM,
(31:19):
as AI becomes more widespread, how do we ensure that
it is built and deployed responsibly, We talked with Pedra
Bonadira's trustworthy AI practice leader within IBM Consulting. Smart Talks
with IBM is produced by Molly Sosha, Alexandra Garraton, Royston
(31:40):
Preserve and Edith Rousselo with Jacob Goldstein. We're edited by
Jan Guerra. Our engineers are Jason Gambrel, Sarah Brogare and
Ben Holliday. Theme song by Gramoscope. Special thanks to Colly
Migliari and Kelly Kathy Callaghan and the eight Bar and
IBM teams, as well as the Pushkin marketing team. Smart
(32:03):
Talks with IBM is a production of Pushkin Industries and
I Heart Media. To find more Pushkin podcasts, listen on
the i Heart Radio app, Apple Podcasts, or wherever you
listen to podcasts. I'm Malcolm Gladwell. This is a paid
advertisement from IBM.
Welcome to tex Stuff, a production from I Heart Radio.
This season of Smart Talks with IBM is all about
new creators, the developers, data scientists, c t o s
and other visionaries creatively applying technology in business to drive change.
They use their knowledge and creativity to develop better ways
(00:26):
of working, no matter the industry. Join hosts from your
favorite Pushkin Industries podcasts as they use their expertise to
deepen these conversations, and of course Malcolm Gladwell will guide
you through the season as your host and provide his
thoughts and analysis along the way. Look out for new
episodes of Smart Talks with IBM on the I Heart
Radio app, Apple Podcasts, or wherever you get your podcasts,
(00:49):
and learn more at IBM dot com slash smart talks. Hello, Hello,
welcome to a new season of Smart Talks with IBM,
a podcast from pushed In Industries, I Heart Radio and IBM.
I'm Malcolm Gladwell. This season we're talking to new creators,
the developers, data scientists, c t o s and other
(01:12):
visionaries who are creatively applying technology in business to drive change.
Channeling their knowledge and expertise, 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.
(01:33):
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 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,
(01:58):
especially if you're part of an unders serve community, a
process that, as you'll hear, is not only confusing and complex,
but often deeply unfair. 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,
(02:21):
using AI to help people learn how to qualify for
a loan, and relying on IBM technology to store consumers
most sensitive information safely 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
(02:42):
a decade, first at The Wall Street Journal, then at MPR.
Now let's get into the interview. Let's start this like
a brom car How did you meet each other and
decided to start a company together. Steven was supposed to
(03:05):
come to a bachelor party in Miami and didn't show up,
and it broke my heart. There's more to the story
than just simply that one of my old employees introduced us.
I've just left Marcato. They've been acquired for one point
for a billion, and I am, you know, living the
Miami lifestyle. You know, I have a condo on the
water and all the nice things to go with. A
(03:25):
guy named Michael Ramsey had asked me, you know what,
I helped him do mortgage lead generation and I was like,
you know, sure, I'm not doing anything else? Why not?
And I meet Steve that he was in North Carolina.
I left a pretty fruitful career in banking. I was
an underwriter. Underwriting loans mean it's basically deciding who should
(03:46):
get a loan and at what interest rate? Right, Absolutely
due diligence, right, which is understanding whether or not this
particular individual has the worldwithal to afford the mortgage. Also
the credit risk individual presents. But there was this disconnect
in that process where you have hidden action taking place
(04:09):
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 this is usually both sides of
the negotia. Everybody's hiding stuff from everybody else. There's a
(04:30):
absolutely and it's like sort of inadvertent as well too,
right in that process, and 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 both sides,
and that winds up leading to bad outcomes. It winds
(04:52):
up leading to long delays that are frustrating or scary
for the for the borrower, yes, who are 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 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
(05:14):
gonna get that same level of support over months because
you know, buying houses and 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
I reach back out to Stephen around August of two
thousand seventeen and said, hey, you know, we need to
(05:35):
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,
you're sort of coming from the banking side, and Brian
you're sort of coming from the tech side. Absolutely, what
(05:58):
exactly is the problem that you've got us are trying
to solve when you start this company, and its simple
assessence is data democratization, the ability to take complex information
and simplify so that someone that isn't an expert like
Stephen can understand what's going on, and in this case specifically,
what is the data that you're trying to democratize underwriting data,
(06:20):
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.
But do I know why not? Really? I mean, you
get a letter of an adverse letter, but it's still
very broad Engineeric, it doesn't really tell you what to
(06:42):
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? And
that's really what sets this platform apart and all. So
why it's important how we're sort of reframing of this
(07:04):
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 how it fits with with what you're trying
to do. Yeah, I mean, the home ownership gap, at
(07:25):
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, whether you're part of the l p G,
t Q plus community, it shows that there's a higher
(07:46):
level of declines for these communities than there are for
for or white males. So you know, in 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 eggable for all. So, if I
understand you correctly, you're saying, basically, in the current system,
white men have an easier time getting a mortgage than
(08:08):
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 said, yeah, that that is what the data shows.
It's not just my perspect that's what the data shows.
Is so, and so, how are you trying to help
fix that problem by turning everybody into corn? By turning
(08:30):
everybody into corn? I 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
(08:52):
these biases 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 remove or
(09:14):
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,
genre specifically blockchain. There are three big tech ideas behind
home lending pal at least three of that we're going
(09:35):
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
(09:58):
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
(10:19):
thesis is that with the leverage of a mutable ledger
such as blockchain, you're able to still collect the information
that is necessary for the Home Mortgage Disclosure Act or
HUMMED 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
(10:40):
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 talking about artificial intelligence and a virtual assistant. Most
people think of that it's just a one way street.
You know, we are trying to build this human like
(11:01):
interaction where it is able to not only understand, but
to respond, and then to leverage those responses and create
a road map 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
are going to be using it up front, and a
(11:24):
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 ready millennials today that are qualified to
buy alone, even though they're not trying. They just don't know.
We're trying to bring greater trust and transparency to this process. Yeah,
(11:44):
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. We were child is constantly
with why not a simpler solution? Right? But in reality
(12:05):
the problem is much more complicated than the simplicity these
forces wanted to bring it 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
the old, broken processes doesn't allow for systemic change. You know,
you have to try to find ways to not only
(12:27):
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 ticularly Stephen mentioned
and wanted us to do is simpler. Yeah. So one
of the ideas you guys have is that transparency can
(12:50):
help reduce bias. So in what we are, you're 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 it is just the natural
features of the blockchain. Right. It's transparent because all participants
(13:13):
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 is taken, it's
(13:36):
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 feel very confident that
(13:58):
we'll be able to reduce it said nificantly 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 light bulb, the high idea that you
could do this. The moment that made me realize that
(14:19):
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 the overall originations that
happened in the US. When they kind of stepped in
and we're like, hey, you know, we're gonna lead your
your development before your Series A, We're going to try
(14:40):
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 Quo Mutual or
CMfg Adventures their discovery fund, and they are the one
of the largest collections of credit unions 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
(15:00):
find solutions like ours, I think that was the 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
(15:24):
when when you're fully you know, fully up and running.
How's it gonna work? Yes, So you will spend about
five minutes going through our onboarding process where you're connecting
your online bank accounts, you're authorizing and soft FCO. Cool.
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
(15:46):
able to go to your showing a vantage score. So
that's kind of the first level of disconnect and so
we're solving for that first. So you go to that
process and then after you signed up, our virtual assistant
keV begins doing his he's analyzing your profile. Uh. He's
really a geared towards helping you understand really three or
(16:06):
four critical elements. You know, one 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 the home? Three the best loan product for you,
and then for the lenders within our ecosystem, they present
(16:28):
the best chance of success with 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. He's probably
one of the most helpful, friendly people that you've ever met,
(16:49):
and it didn't matter who you were, So we really
wanted to encompass his personality into the solution itself. But yes, keV,
it becomes a friend 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 ber profile and begins to create a
path that you can follow to become a homeowner. We
(17:13):
have 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 alan, and what loan makes the most sense
(17:34):
for you, and how long the whole process is likely
to take. You can ask keV questions and it will
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 beyond just providing a frequently
asked questions link. Brian says he thinks a lot of
(17:55):
people might be 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
(18:16):
try to use this virtual assistant just to make them
feel comfortable getting into the process of what homeownership could
look like. And then from there 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
(18:37):
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.
Is the dream that sort of AI can help people
who have been excluded become more included. Yes, most white
(18:58):
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 that right.
(19:20):
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 accurate support that
(19:45):
historically hasn't been available to these marginalized groups. Stephen, you
mentioned your own families, your and Brian's famili's experience with
getting home loans with the banking system. Do you guys
mind us talking about that specifically? What have been in
your family's experiences with getting loan? Yeah? Yeah, I mean
(20:06):
back in the subprime mortgage crisis, and you know, my
mom nearly lost her dream home that I bought for
those primarily because we were in an arm even though
we should have been an av A loan because she
has a military veteran and an armed an adjustable rate
loans that was way worse than the mortgage. It was
way worse. I mean, you know, it started out better
just because you pay less, but once that interest rate flips,
it becomes way worse if you're not prepared for it.
(20:28):
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 rate lining, etcetera.
Red Lining is basically the history of lenders not making
(20:49):
loans to people in predominantly black neighborhoods. Essential exactly, we're
picking in which areas they will lend to specific groups. Yes,
and those areas were predominantly white historically hamper dom wa
why Yes. So you have those elements. You have the
economic elements where there's this concept of its just being
unattainable for us. You have the psychological elements of being
(21:11):
misunderstood thinking that the only way I can 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 monitor income borrowers
these days, just because with rates increasing, with the supply
shortages that we have, you know, homeownership is really going
(21:32):
to become a lot more difficult for 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 IBM um our need for data protection and security?
(21:55):
So you're talking about digitizing documents, 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 palace that we ran without having to worry about
(22:17):
people's information getting stolen in essence, 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
(22:39):
a vendor on boarding process that requires 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 Pal story protecting data in the cloud. Think
about the problem this one is solving. Brian and Stephen
(23:00):
have this little startup. They need to collect supersensitive data
from people. Everything you have to show 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
(23:23):
would never do on their own. From a technical perspective,
you have different compliance checks that 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,
(23:44):
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 either have internal
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 can into me? How do you convince
customers that you're going to keep their data safe absolutely. Um.
(24:06):
Part of it is doing stuff like this where we're
acknowledging and making the consumers aware 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
(24:27):
lot to help Home Lending Pal protect this sensitive data.
Part of it is being able to show IBM s
logo on our website. You'll you'll 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
(24:49):
into why people will be willing to give us their data.
But a lot of that is very contingental, just people
seeing the IBM logo and saying that, hey, you know,
we can if we don't trust Home Lending Path, we
definitely trust IBM with this expect 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
(25:11):
you need? We 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
of internally and externally in terms of getting things change
in this industry, especially when talk about systemic change, and
sometimes that requires you to make very big ask, you know,
(25:33):
swing for the 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're trying to
do it this way, but maybe you want to use
our internal product and do it with this instead, And
(25:55):
so that makes it 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 can get recommendations right now.
(26:17):
We're fully licensed Colorado, 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 ought
to be presented with. Have you heard back? I mean,
(26:39):
I know that this is 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 any of your customers told you about how it's
helped them. Surprisingly, a lot of our customers will reach
out to us and give us use cases we've had
local TV interviews, what they've interviewed them. Without those success
(26:59):
stores will have customers that will reach out to us
what challenges that they're having and hoping that we can
help them through those, even if we have to manually
connect the 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
(27:20):
in at all stages of 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 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
(27:40):
I really think that is the beauty about what we're
building is that the people 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. We were able
(28:00):
to get her approved for a little bit over six
D fifty thousand, which was about fifty 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 know,
to do better than your mom, right, that's the whole
reason why I built the system, so you know, So
that one really sticks closest to me is because, uh,
(28:21):
we've asked some users that have gone through the entire
process and have helped 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. Now, as Steven mentioned, we're getting into this
much more interactive, uh, conversational dialogue where consumers are not
(28:42):
only showing kind of what they want to buy, but
also getting into kind of what their feelings are, what
is what are their sentiments that they're looking for in
a potential relationship with they lender. Uh. 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
(29:03):
the next couple of months, where we'll go out and
raise hopefully eight thiggers or more to really flush out
the features 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 on a million minorities, become homeowners by
(29:24):
utilizing our product. You know, we operate in an industry
that's very lucrative 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 agguiable for everyone.
You know, I think if we were to be acquired
or to to do an initial public offering in five
(29:47):
years and we're not doing that, then for me it
would not 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 absolutely. Malcolm glave all here to end today's show,
(30:11):
I want 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.
(30:32):
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
(30:55):
the scenes. We all just want a good mortgage with
fair terms. And because Brian and Stephen made creative use
of IBM 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 with IBM,
(31:19):
as AI becomes more widespread, how do we ensure that
it is built and deployed responsibly, We talked with Pedra
Bonadira's trustworthy AI practice leader within IBM Consulting. Smart Talks
with IBM is produced by Molly Sosha, Alexandra Garraton, Royston
(31:40):
Preserve and Edith Rousselo with Jacob Goldstein. We're edited by
Jan Guerra. Our engineers are Jason Gambrel, Sarah Brogare and
Ben Holliday. Theme song by Gramoscope. Special thanks to Colly
Migliari and Kelly Kathy Callaghan and the eight Bar and
IBM teams, as well as the Pushkin marketing team. Smart
(32:03):
Talks with IBM is a production of Pushkin Industries and
I Heart Media. To find more Pushkin podcasts, listen on
the i Heart 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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