0:01

Hello, Hello, Welcome to a new season

0:03

of Smart Talks with IBM, a

0:05

podcast from Pushkin Industries, iHeartRadio

0:09

and IBM. I'm Malcolm

0:11

Glabwell. This season,

0:13

we're talking to new creators,

0:15

the developers, data scientists,

0:18

CTOs, and other visionaries

0:20

who are creatively applying technology

0:23

in business to drive change. Channeling

0:25

their knowledge and expertise, they're developing

0:28

more creative and effective solutions

0:31

no matter the industry. Our

0:33

guests today are Brian Young and

0:35

Stephen Better, co founders

0:38

of home lending Pal. Home

0:40

lending Pal is a member of the IBM

0:43

hyper Protect Accelerator, an

0:45

investment readiness and technical

0:47

mentorship program that supports

0:49

impact focused startups leveraging

0:52

highly sensitive data.

0:54

Their story is a perfect place

0:57

to start our season. They

0:59

recognize used a profound problem

1:02

the horrible process of getting a

1:04

home loan, especially if you're part

1:06

of an underserved community, a process

1:08

that, as you'll hear, is not only

1:10

confusing and complex, but often

1:13

deeply unfair. So Brian

1:15

and Stephen teamed up to use technology

1:18

to attack that problem in a bunch

1:20

of creative ways. You'll hear

1:22

how they're tapping into blockchain to make

1:25

the home loan process more transparent

1:27

and fair, using AI

1:29

to help people learn how to qualify for

1:31

a loan, and relying on IBM technology

1:34

to store consumers most sensitive

1:37

information safely in the

1:39

cloud. Brian and Stephen

1:41

talked with Jacob Goldstein, host of

1:43

the pushkin podcast What's Your Problem.

1:46

Jacob has covered technology and business

1:49

for over a decade, first at

1:51

The Wall Street Journal, then at

1:53

MPR. Now let's

1:55

get into the interview. Let's

2:05

start this like a rom com.

2:07

How did you meet each other and decide

2:09

to start a company together. Stephen

2:11

was supposed to come to a bachelor party in Miami

2:13

and didn't show up, and it broke my heart.

2:16

There's more to the story than just simply

2:18

that one of my old employees

2:20

introduced us. I've just left

2:22

Marcato. They've been acquired for one point

2:25

four billion, and I am, you know, living

2:27

the Miami lifestyle. You know, I have a

2:29

condo on the water and all the nice things

2:31

that go with it. A guy named Michael Ramsey

2:34

had asked me, you know, would I

2:36

help him do mortgage lead generation? And I

2:38

was like, you know, I'm sure I'm not doing anything else. Why not?

2:41

And I meet Steve that he

2:44

was in North Carolina. I left a

2:46

pretty fruitful career in banking.

2:48

I was an underwriter underwriting

2:51

loans. Mean it's basically deciding who should get

2:53

a loan and at what interest rate? Right, absolutely

2:55

due diligence, right, which is understanding

2:58

whether or not this particular individual

3:01

has the wherewithal to afford

3:04

the mortgage, also the credit

3:06

risks that individual presents.

3:09

But there was this disconnect

3:12

in that process where

3:14

you have hidden action taking place

3:16

on one side of the transaction, while

3:18

you have another side of the transaction that that tends

3:20

to hide information. And

3:23

just to be clear, it's the borrower

3:25

who hides information and the bank that

3:27

hides the action the lender in

3:30

most cases, but it's usually

3:33

both sides of the negotia. Everybody's

3:35

hiding stuff from everybody else's

3:37

absolutely, and it's like sort of

3:39

inadvertent as well too. Right in

3:41

that process, certain things fall through the cracks.

3:43

And you know, falling through the cracks means

3:46

weeks without notification from

3:48

a bar's perspective as to whether

3:50

or not you know this deal is moving

3:53

forward. Okay, So the problem is a lack

3:55

of information on both sides, and

3:57

that winds up leading to bad outcomes. It winds

3:59

up leading to long delays that are

4:02

frustrating or scary for the borrower.

4:05

Yes, a lot of consumers

4:07

just don't have anywhere to go. If

4:10

you go online, everything is too broad engineering,

4:12

especially if you know you're not ready to buy at that moment.

4:15

If you talk to a lender or relator, if you're not ready

4:17

to buy at that moment, there they'll help you, but

4:19

it's not the same level of help. But you're not going to get

4:21

that same level of support over months,

4:23

because you know, buying house isn't like buying a piece

4:25

of candy online. And so we really

4:27

looked at, Okay, well, how can we give people this

4:30

safe environment to go explore and

4:33

understand when homeownership could look like for them

4:35

based on their personal information. And that's kind of when

4:37

I reach back out to Stephen around August

4:39

of twenty seventeen and said, Hey, you

4:42

know, we need to do this together. You understand

4:44

the back inside from a lender underwriter's

4:47

perspective, and I understand the plight

4:49

of the consumers, and if we come together, this could

4:51

be something that could be really unique. A

4:53

capitalist solution to a social challenge is

4:55

probably the best way to put it. So Stephen,

4:57

you're sort of coming from the banking side,

5:00

and Brian you're sort of coming from the tech

5:02

side. Absolutely, what

5:05

exactly is the problem that you guys are trying

5:07

to solve when you start this company, and

5:09

its simplest essence is data

5:11

democratization, the ability to

5:13

take complex information and

5:15

simplify so that someone that isn't an expert

5:18

like Stephen can understand what's going

5:20

on. And in this case specifically,

5:22

what is the data that you're trying to democratize?

5:25

Underwriting data, so the decision

5:28

or the data that is utilized to determine

5:30

whether or not you are approved or declined

5:32

for a home loan. So right now,

5:35

if I go apply for a loan, they

5:38

approve me or they decline me. But

5:41

do I know why not?

5:43

Really? I mean, you get a letter of an adverse

5:45

letter, but it's still very broad Engineeric, it

5:47

doesn't always tell you what to focus on next,

5:50

but you do have a very high level sense

5:52

of why your decline. Yeah,

5:54

there's no true guidance from

5:56

that point of rejection, right

5:59

there's no fundamental understanding

6:01

as to what could I have done better?

6:03

And that's really what sets this platform

6:06

apart, and also why it's important

6:08

how we're sort of reframing

6:11

of this data workflow. I want to get

6:13

into the details of that, but just as we sort

6:15

of understand the problem a little

6:17

bit more, I mean, one piece of it that we haven't

6:19

talked about is race

6:22

and the homeownership gap. Can you guys

6:24

talk a little bit about that and how it fits with what

6:27

you're trying to do. Yeah,

6:30

I mean, the homeownership

6:32

gap, at least for African Americans is larger

6:34

now than it was fifty years ago

6:37

and segregation was legal, which

6:39

is quite saddening. But it's not just

6:41

African Americans. And when you look at declines,

6:44

whether you are a woman, whether

6:46

you are a minority, whether you're a

6:48

part of the LBTTQ plus community,

6:50

it shows that there's a higher level

6:53

of declines for these communities than there are

6:55

for older white males. So

6:57

you know, in our perspective, there has to be a

7:00

lot that needs to be done in terms of resetting

7:02

reconfiguring the system to make it more fair

7:04

and equitable for all. So, if

7:07

I understand you correctly, you're saying,

7:09

basically, in the current system,

7:12

white men have an easier time getting

7:14

a mortgage than anybody else. Well

7:17

you said it. I'll just agree with it. I

7:19

think you said it. I think if I understood you're

7:21

correctly you said, yeah, that is

7:24

what the data shows us. And it's not just my perspective,

7:26

that's what the data shows us. So and so,

7:29

how are you trying to help fix

7:31

that problem by turning

7:34

everybody into corn? By

7:36

turning everybody into corn? I like it,

7:38

Yeah, what do you mean by that? Through the power

7:40

of math, right, cryptography specifically,

7:43

we are able to make everyone look the same and

7:46

the underwriter just simply

7:48

understands the fundamental attributes

7:51

that ought to drive that approval disapproval

7:53

decision. Right, in order to help

7:55

us and also to help our government

7:57

understand where these biases

8:00

are coming from. Our

8:02

lenders are required to ask you what

8:04

your raise, what your sex, even your

8:06

age? Right, Like, all of this comes with this

8:09

application scenario. But

8:11

does this information inadvertingly create

8:14

the bias? Can we make

8:16

everyone look the same and start

8:19

to remove or better

8:21

identify where these issues

8:23

are sort of coming from? So

8:25

you're trying to use technology to blind

8:29

all the decision makers in the home loan process

8:31

to race ethnicity,

8:34

specifically blockchain. There

8:36

are three big tech ideas behind

8:39

home lending pal at least three

8:42

that we're going to talk about today on the show, and

8:44

blockchain is big tech idea number

8:46

one. You may have heard of blockchain

8:49

because it's the key idea behind cryptocurrency,

8:53

but the idea of blockchain is bigger

8:55

than just digital money and much

8:57

more than just a new way to store information

9:00

on the Internet. Blockchain is

9:02

a shared, immutable ledger

9:04

that facilitates the process of

9:07

recording transactions and tracking

9:09

assets in a business network.

9:12

Brian and Stephen want to use blockchain

9:14

to gather up the information on race and

9:16

gender that's required by law without

9:19

showing it to the lenders making the decisions

9:22

about who gets alan. Our

9:24

argument, or our thesis

9:26

is that with the leverage of a

9:29

mutable ledger such as blockchain, you're able to

9:31

still collect the information that is necessary

9:33

for the Home Mortgage Disclosure Act

9:35

or HUMDA as Stephen was referring to. But

9:38

then with a smart contract, you don't have to

9:40

release that information, so after the decision,

9:42

the approval of decline is made for the consumer.

9:45

So you have this big idea for what you want to do as

9:47

a business, which you want to do socially, but

9:50

how do you make creative use of technology

9:53

to do the thing you want to do, to make it real.

9:56

You know, we're trying to build something that hasn't been done in

9:58

the mortgage industry. Sho we talk

10:00

about artificial intelligence and a virtual assistant,

10:02

most people think of that it's just a one

10:05

way street. You know, we are trying to build

10:07

this human like interaction where it

10:09

is able to not only understand,

10:11

but to respond, and then to leverage

10:13

those responses and create a

10:16

roadmap towards allowing you

10:18

to achieve your goals, which is probably

10:20

one of the most creative things that I've ever done

10:22

personally. But it also requires

10:25

you to be willing to accept constructive criticism

10:27

from the people that are going to be using it up front,

10:30

and a lot of what we're doing is

10:32

really trying to find creative ways just to get them involved

10:34

in that conversation to say that, hey, you

10:36

know, we are trying to build this to help you. Right

10:39

now, there's about twenty one million mortgage reading

10:41

millennials to day that are qualified

10:43

to buy a loan, even though they're not trying.

10:46

They just don't know. We're trying to bring greater

10:48

trust and transparency to this process.

10:50

Yeah, I guess, from my perspective, beyond

10:53

just simply understanding the technology

10:56

and what it's able to do, I

10:58

think it takes the will to

11:00

go ahead and take on that complexity

11:03

to try something new. We

11:06

were challenged constantly with

11:08

why not a simpler solution, right,

11:11

But in reality, the problem is much more

11:13

complicated than the simplicity

11:16

these forces wanted to bring into

11:18

the table. You have to have vision,

11:21

you have to have a desire to want to make

11:23

fundamental change. Yeah,

11:26

new tech built on old, broken processes

11:28

doesn't allow for systemic change. You know, we

11:30

have to try to find ways

11:32

to not only just to make it

11:34

easier for people to connect to lenders, but at

11:37

the core of what we were trying to build, we really wanted

11:39

to address the systemic issues in the

11:41

home buying process, and that required us to

11:43

try something different basically, and I think

11:45

that's the most creative thing you can do in an industry

11:47

that typically a Stephen mentioned wanted

11:50

us to do its simpler. Yeah. So

11:52

one of the ideas you guys have is that transparency

11:56

can help reduce bias.

11:59

So in what way are you using technology

12:02

to bring more transparency to

12:05

the home buying process? When

12:07

we speak of transparency when we speak

12:10

of trust, what we're really

12:12

talking about is just the natural

12:14

features of the blockchain. Right.

12:17

It's transparent because all participants

12:20

within this framework of access to this

12:23

decentralized ledger. So

12:25

we are all seeing how

12:28

these pieces are sort of moving. Right, we're

12:30

playing poker with our cards

12:32

facing up when we're speaking

12:35

to trust, right, we're speaking

12:37

of the mutability of this

12:39

information, knowing that if an action

12:41

is taken, it's there on the ledger

12:43

and we can't just simply remove it.

12:46

So these features

12:49

lead to this forceful

12:51

curing of certain biases

12:55

that tend to form within certain

12:57

systems. We're not saying

12:59

that we're going to move all biased,

13:02

but what we're saying is that we feel very

13:04

confident that we'll be able to reduce it

13:06

significantly without regulatory

13:10

reinforcement by the simple

13:12

nature of this technology stack that

13:14

we're developing. So,

13:16

was there some moment when you guys had

13:19

the light bulb, the a

13:21

high idea that you could do this. The

13:23

moment that made me realize

13:26

that this was stable was when our first group

13:28

of lenders invested. It was a group called the Mortgage

13:30

Collaborative. They are a collection

13:32

of about three hundred and twenty

13:34

five lenders. I believe across the country they

13:37

represent about twenty five percent the overall originations

13:39

that happened in the US. When they

13:41

kind of stepped in and we're like, hey, you know, we're going

13:43

to lead her your development before

13:46

your Series A, we're going to try to help you there, I think that

13:48

was the moment for me. And then we had shortly

13:51

after that joining that round was a group called Quino

13:53

Mutual or CMfg Ventures their Discovery

13:55

Fund, and they are the one of the largest

13:57

collections of credit unions in the industry. So, you

14:00

know, typically you have an issue where you

14:02

know, consumers feel like there's a problem that's

14:04

not truly being solved. But to see that lenders

14:06

we're looking to try to find solutions like ours.

14:08

I think that was the AHA moment for me and said,

14:10

hey, you know this could be feasible for

14:13

us, that the people who will actually have to work

14:15

with you want to help you. Like,

14:18

that's exactly great,

14:21

but just tell me how

14:23

will it work, Like walk me through. I'm an

14:26

ordinary person. I want to get a loan. I

14:28

come to home lending Pal. What happens

14:31

when you're fully you know, fully up

14:33

and running. How's it going to work? Yeah,

14:36

So you will spend about five minutes going

14:38

through our onboarding process where you're connecting

14:40

your online bank accounts, you're authorizing

14:42

a soft FCO pool. There's a credit

14:45

report, basically a credit report. Yeah, you're here.

14:47

Most people don't realize. So, so lenders are utilizing

14:49

your FIICO scores and most of

14:51

the places online that you're able to go to or showing

14:53

advantage scores. So that's kind of the first level

14:56

of disconnect and so we're solving for that first.

14:59

So you go to that process and then after

15:02

you signed up, our virtual assistant

15:05

keV begins doing his work.

15:07

He's analyzing your profile. He's

15:09

really geared towards helping you understand

15:12

really three or four critical elements.

15:14

You know, One you're likelihood for

15:16

success or approval to

15:19

some financial modeling and forecast and give you

15:21

a better understanding of when you should begin the process

15:24

to apply for a home So how long will it take

15:26

you to become a homeowner or to close

15:29

them a home. Three, the best loan

15:31

product for you and then for the

15:33

lenders within our ecosystem that present the best chance

15:36

of success with them as well. So you

15:39

mentioned a virtual advisor keV

15:42

virtual meaning it's not a guy named

15:45

keV right. It's

15:48

named after one of my good friends from college

15:50

that passed from a rare form of germ sale

15:52

cancer. He's probably one of the most helpful,

15:54

friendly people that you've ever met, and it didn't matter who

15:57

you were, so we really wanted to encompass

15:59

his personality into the solution

16:02

itself. But yes, keV,

16:05

it becomes a friend of pal, you know, so

16:08

even if you're not ready to buy, he just doesn't

16:10

pass you off and say, hey, I'm not going to help.

16:12

It really analyzes your profile

16:14

and begins to create a path that

16:17

you can follow to become a homeowner.

16:20

We have arrived at big tech idea number

16:22

two. keV the Virtual

16:25

Assistant is built using powerful

16:27

artificial intelligence tools. The

16:30

AI takes the potential homebuyers

16:33

information and runs it through algorithms

16:35

that tell you things like how likely

16:37

you are to get a loan, and what loan makes

16:40

the most sense for you, and how long

16:42

the whole process is likely to take. You

16:45

can ask keV questions and it'll

16:47

give you answers. But keV is more

16:49

than your average responder chatbot.

16:52

It speaks conversationally. It

16:54

knows who you are, understands your needs,

16:57

and helps beyond just providing a frequently

16:59

ask questions link. Brian

17:01

says he thinks a lot of people might be more comfortable

17:04

talking with an aipowered virtual assistant

17:07

than with a human loan officer at a bank.

17:10

I think it really solves a cultural problem.

17:12

There are cultural barriers that prevent

17:15

different segments from becoming homeowners or

17:17

at least impact their buying decisions

17:20

in terms of how they explore homeownership. So the

17:22

first part is to try to use

17:24

this virtual assistant just to make them feel comfortable

17:27

getting into the process of what homeownership

17:29

could look like. And then from there it

17:31

is about preparing them, getting them better

17:33

qualify so that once they are ready to

17:35

say, hey, I want to come home owner,

17:37

I found the house that I love, allowing

17:40

that transaction, that process

17:42

to be a lot smoother and easier through

17:44

the use of blockchain. Basically, so when

17:46

you say cultural, I mean does that include

17:49

in part race and

17:51

ethnicity, people who have traditionally

17:53

been excluded from the banking

17:56

sector from housing. Is

17:58

the dream that sort of a I can

18:00

help people who've been excluded

18:02

become more included. Yes,

18:04

most white people have resources.

18:06

They have other friends and family

18:09

who have gone through this process successfully

18:12

multiple times as opposed to just the one

18:14

time. Within our communities, it is difficult

18:16

just to find the one person that

18:18

you can discuss this process with, and

18:21

most of the time that one person has gone

18:23

through a negative experience in

18:26

that right. Brian's parents have experienced

18:28

difficulty in this instry. My parents have experienced

18:31

difficulty in this process too.

18:33

Isn't until you get to our generation where

18:35

you have family members that

18:37

have gone through this process multiple times

18:39

and have been successful. So

18:42

when we speak to keV being culturally

18:45

relevant, it's because keV

18:48

is there to provide you accurate

18:50

support that historically hasn't

18:53

been available to these marginalized

18:55

groups. Stephen, you mentioned your

18:57

own families, your and Brian's families

19:00

experience with getting home loans

19:02

with the banking system. Do you guys mind just talking

19:05

about that specifically? What have been your

19:07

family's experiences with getting

19:10

loan please? Yeah? Yeah,

19:12

I mean back in the subprime mortgage

19:15

crisis, and you know, my mom nearly lost her dream

19:17

home that I bought for those primarily

19:19

because we were in an arm even though we should have been an

19:21

AVA loan because she is a military veteran

19:23

and an arm an adjustable rate loans

19:25

sold Alan that was way

19:27

worse than the mortgages. It was way worse. I mean,

19:29

you know, it started out better just because you pay less,

19:32

but once that interest rate flips, it becomes way

19:34

worse if you're not prepared for it. And I think

19:36

you know, again, when when we talk about

19:38

these cultural factors, there's really five that

19:40

you deal with. There's there's cultural itself. So

19:43

things like the subprime mortgage crisis

19:45

where African Americans are hurt the most

19:47

coming out of that, you have redlining,

19:49

reverse redlining, etc. Redlining

19:52

is basically the history of lenders

19:55

not making loans to people in predominantly

19:58

black neighborhoods. Essential exactly, we're

20:00

picking which areas they will lend

20:02

to specific groups. Yes, and those areas

20:04

were predominantly white historically and predominantly

20:07

why yes. So you have those

20:09

elements. You have the economic elements where

20:12

there's this concept of as just being unattainable

20:14

for us. You have the psychological

20:17

elements of being misunderstood

20:19

thinking that the only way I can buy a home is having

20:21

twenty percent down to put down towards a down payment,

20:24

and that's just not true. So

20:26

our ultimate objective is just

20:28

really to make that more attainable for everyone,

20:30

and it's really for all load of modern income bar hours

20:33

these days, just because with rates increasing,

20:35

with the supply shortages that we have,

20:37

you know, homeownership is really going to become a

20:39

lot more difficult for a lot of

20:41

people, regardless of their age, sex, and race.

20:45

So you have this industry that suffers from a lack

20:47

of transparency, from historical bias

20:49

in terms of race and gender. You start

20:52

this technology driven company to try and fix

20:54

those things. As you're building the company, how

20:56

do you come to work with IBM

21:00

our need for data protection and security? So

21:02

you're talking about digitizing documents,

21:05

digitizing information to allow

21:08

greater access to underserved,

21:10

underrepresented groups. And IBM

21:12

had their hyper Protect Accelerator which

21:15

was entirely focused on that, taking small

21:17

startups like Hours and allowing

21:19

them to basically run the pilots

21:22

that we ran without having to worry

21:24

about people's information getting stolen in

21:26

essence, and then Steve and I were just

21:28

very aggressive in terms of just reaching out

21:31

to different vps, different

21:33

executives at IBM kind of saying, you know,

21:35

here's what we want to do, here's what we need. Will

21:37

you help us? And being in

21:39

an industry that is so regulated,

21:42

it helped us really get to that door, just because

21:44

you know, every bank has a vendor

21:47

on boarding process that requires

21:49

a very high level of data security

21:52

to even work with them. In an essence, here's

21:57

the third big tech idea in a Home

21:59

Lending Pulse story, protecting

22:01

data in the cloud. Think

22:04

about the problem this one is solving. Brian

22:06

and Stephen have this little startup. They

22:08

need to collect supersensitive data

22:10

from people. Everything you have to show

22:12

the bank when you want to get a mortgage. This

22:15

data has to be secure. IBM's

22:18

hyper Protect Accelerator enables

22:20

small businesses to store sensitive

22:22

data in the cloud and keep that

22:25

data secure. Brian says,

22:27

it lets Home Lending Pall do something

22:29

they would never do on their own. From

22:32

a technical perspective, you have different compliance

22:35

checks that you have to meet to work with banking

22:37

institutions or financial institutions. So

22:40

it allows a small startup

22:42

like Home Lending Pall to still be able

22:44

to meet those checks and balances. To bring

22:47

an innovative solution to the

22:49

table for a financial institution where

22:51

more than likely as a startup, you're not going to have the ability

22:54

to do that on your own, just because it is so expensive

22:56

to either have internal servers

22:58

or to try do it on your own as well. So people

23:01

have to trust you to use Homelanding

23:03

Piller, right, Like, I'm giving you everything, how

23:06

do you convince me? How do you convince customers

23:08

that you're going to keep their data safe? Absolutely?

23:13

Part of it is doing stuff like this where we're

23:15

acknowledging and making the consumers

23:17

aware of our relationship with IBM and how IBM

23:20

is handling our storage of the data

23:22

and the sensitive data itself. Technically,

23:24

the IBM description of it is their confidential

23:27

computing services or cloud services,

23:29

and it's basically saying that even though the information

23:31

is stored in the cloud, IBM

23:33

is going to do a lot to help home Landing pill protect

23:36

this sensitive data. Part of it is

23:38

being able to show IBM's logo on

23:40

our website. You'll be surprised how much logo

23:43

recognition helps people understand that this is a

23:45

legit business, a legit company,

23:48

if you will. And then there's also stuff

23:50

like you know, people seeing the address

23:52

of the business, contact information for the business.

23:54

Like all this stuff backers into why people

23:57

will be willing to give us their data. But

23:59

a lot of that's very contingent on just people seeing

24:01

the IBM logo and saying that, hey, you know, we

24:03

can if we don't trust Home Landing Path, we

24:05

definitely trust IBM with this aspect of the

24:07

business. So what is the sort of story

24:09

of working with IBM

24:11

on this? I mean, did you just figure out

24:14

that they had the thing you need or did they sort

24:16

of work with you to build the thing you need?

24:19

We told them what we wanted. I

24:23

think there's a certain special

24:26

relationship that we have with IBM. As I mention,

24:28

you know, Steve and I are very aggressive

24:31

of internally and externally in terms of getting

24:33

things change in this industry, especially when we talk

24:35

about systemic change, and sometimes

24:37

that requires you to make very

24:40

big asks, you know, swing for the fences and

24:42

see what happens. And as

24:44

we found out more, as we hired better

24:46

talent, as we understood more of what we were trying

24:48

to do, it made it a lot easier

24:50

for us to really share this vision with IBM.

24:53

And then now they're able to recommend

24:56

products to say we see you're trying to do it

24:58

this way, but maybe you want to use our internal

25:00

product, can do it with this instead, and so that

25:02

makes a lot easier for us to try to bring artificial

25:05

intelligence in blockchain to an industry that hasn't

25:07

historically accepted new technology

25:09

that well. So

25:12

where are you in your journey as a company.

25:14

I know you're still sort of working

25:16

on it. What can customers do

25:18

now with your product? They

25:21

could get recommendations right

25:23

now. We're

25:25

fully licensed in Colorado, Florida, and

25:27

in North Carolina,

25:30

So right now, customers from those days

25:32

can expect to be connected with a lender with

25:36

full guidance as to what

25:38

exactly they're getting into and what

25:40

pricing expectations they ought to be

25:43

presented with. Have you heard

25:45

back? I mean, I know that this is kind

25:47

of a weird question, given that the whole point

25:49

is that people can be anonymized,

25:52

But are you able to talk to your customers?

25:54

Have any of your customers told

25:56

you about how it's helped them? Surprisingly,

26:00

a lot of our customers will reach out to us

26:02

and give us use cases, as we've had local TV

26:04

interviews where they've interviewed them about those success

26:06

stories. Will have customers

26:08

that all reach out to us with challenges that they're having

26:10

and hoping that we can help them through those, even

26:13

if we have to manually connect a borrower

26:15

to a lender and a state that we don't operate,

26:17

and we're more than happy to do that. In

26:19

exchange for that, they're basically helping us build

26:21

out this new process. And so that's

26:24

kind of the beauty of the system is that, you know, customers

26:26

are coming in at all stages

26:28

of the buying cycle. You have some that are

26:31

still renting, at that daydreaming phase where

26:33

they're really trying to understand, you know, is home

26:35

ownership a feasible option for me? And

26:37

then you have some that are, you know, trying to

26:39

test out new features like optimal character

26:41

recognition software where they're able to upload documents

26:44

and see how those documents transferred to lenders.

26:46

So I really think that is the beauty

26:48

about what we're building, is that the people

26:51

have helped us build it so far. Are

26:53

there any particular stories you've heard

26:55

from customers that have stayed with you? So,

27:00

I think the one that's most relevant to me, that sticks

27:02

closest to my heart is my mom. You know, she was

27:04

looking to try to buy another house, and

27:06

we were able to get her approved for a

27:09

little bit over six hundred and fifty thousand, which was about

27:11

fifty thousand more than what she had heard

27:13

from anyone else in the area. So,

27:15

you know, we've really been excited. At least I'd really been excited

27:18

about that one. That's great. You're not going

27:20

to do better than your mom, right helped

27:22

your mom. That was the whole reason I built

27:24

the system, So you know, so that one really sticks

27:26

closest to me is because we've

27:28

had some users that have gone through the entire process

27:31

and have helped us go from our initial

27:33

phase and we've really been launching in phases

27:35

where at first it was more showed

27:37

just showing the affordability amount, like you

27:40

know, what was the amount of home that you could afford, And

27:42

now, as Steven mentioned, we're getting into this much more

27:44

interactive conversational

27:47

dialogue where consumers are not

27:49

only showing kind of what they want to buy, but

27:51

also getting into kind of what their feelings

27:53

are, what is what are their sentiments

27:55

that they're looking for in a potential relationship

27:57

with they lender. And so we're really excited

28:00

and consumers come in and they test new

28:02

features and they say, hey, this is working, great,

28:04

this isn't working you know what

28:06

about this? And we think that's really going to lead into

28:09

our series A raise here in the next couple of months, where

28:11

we'll go out and raise hopefully eight figures or more

28:13

to really flush out the features that consumers

28:15

have set they want it the most. Is really what we're

28:17

most excited about. What's

28:20

your dream for homelanding pal? If you think whatever,

28:23

I don't know, five years in the future, ten years in the

28:25

future, where are you. I want

28:27

to see at least a million people, hopefully a million

28:29

minorities become homeowners by utilizing

28:32

our product. You know, we operate

28:34

in an industry that's very lucrative

28:36

for a lot of people. Having supported

28:38

IBM will hopefully help us from a business perspective,

28:40

But I don't want us to lose sight of our

28:43

social impact goals and the things that we're really

28:45

set out before, which was to make the process

28:47

more eggiable for everyone. You know, I think if we

28:50

were to be acquired or to do

28:52

an initial public offering in five years and we're

28:54

not doing that, then for me it would not be as sweet

28:57

as if it were. To ensure that

28:59

we're actually doing stuff to close the gap for people.

29:03

Thank you guys so much for your time. I really it

29:05

was great to talk with you. Pleasure

29:07

Well, thank you absolutely. Malcolm

29:16

Glavell. Here to end today's show,

29:18

I want to talk about someone who we didn't

29:20

hear from in the interview, but who we heard

29:23

about, Brian's mom,

29:25

because her story really is the story

29:28

of home Lending Pal. Remember

29:30

how Brian told us that back in the adds,

29:32

his mom got that crappy mortgage, the

29:34

one that left her paying higher interest rates

29:37

than she should have been paying. That

29:39

happened to a lot of people, particularly

29:41

people of color. It was that

29:44

story and others like it that really

29:46

inspired Brian to team up with Stephen

29:49

to build Home Lending Pal. They

29:51

wanted to fix a home lending system

29:54

that had been opaque and unfair basically

29:56

forever. Most people

29:59

applying for mortgage aren't thinking

30:01

about the technology that's behind the scenes.

30:03

We all just want a good mortgage with fair

30:06

terms. And because Brian and

30:08

Stephen made creative use of IBM

30:11

technology using AI, blockchain,

30:14

and cloud to rethink the home

30:16

loan process, that is now

30:18

possible for all of us. On

30:23

the next episode of Smart Talks with

30:25

IBM. As AI becomes

30:27

more widespread, how do we ensure that

30:30

it is built and deployed responsibly?

30:33

We talk with Pedra Bonadira's trustworthy

30:36

AI practice leader within IBM

30:38

Consulting. Smart

30:42

Talks with IBM is produced by Molly

30:44

Sosha, Alexandra Garatin,

30:46

Royston Reserved, and Edith Rousselo

30:49

with Jacob Goldstein. We're edited

30:51

by Jen Guerra. Our engineers are

30:54

Jason Gambrel, Sarah Bregere,

30:56

and Ben Tolliday. Theme song

30:58

by Gramoscope. Special

31:01

thanks to Colli Migliori, Andy Kelly,

31:03

Kathy Callahan and the eight Bar and

31:05

IBM teams, as well as the Pushkin

31:08

marketing team. Smart Talks

31:10

with IBM is a production of Pushkin

31:13

Industries and iHeartMedia. To

31:15

find more Pushkin podcasts, listen

31:17

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31:20

Podcasts, or wherever you listen

31:22

to podcasts. I'm

31:24

Malcolm Gladwell. This is a paid advertisement

31:27

from IBM.