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FEDERAL DEPOSIT INSURANCE CORPORATION

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ADVISORY COMMITTEE ON ECONOMIC INCLUSION

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MEETING

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TUESDAY

OCTOBER 22, 2019

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The Advisory Committee convened at

8:45 a.m. in the Federal Deposit Insurance

Corporation Board Room, 550 17th Street, N.W.,

Room 6010, Washington, D.C., Jelena McWilliams,

Chairman, FDIC, presiding.

PRESENT:

JELENA MCWILLIAMS, Chairman, FDIC Board of 

Directors

ROBERT ANNIBALE, Citi

JANIE BARRERA, LiftFund

RAPHAEL BOSTIC, Federal Reserve Bank of Atlanta

JOSE CISNEROS, City and County of San Francisco

MARTIN EAKES, Self-Help Credit Union

DON GRAVES, KeyBank

MARTIN J. GRUENBERG, FDIC Board of Directors

WADE HENDERSON, The Leadership Conference on

Civil and Human Rights

MAURICE JONES, Local Initiatives Support

Corporation (LISC)

ANDREA LEVERE, Prosperity Now

MARGARET LIBBY, MyPath

ALDEN J. MCDONALD, JR., Liberty Bank and Trust

JONATHAN MINTZ, Cities for Financial Empowerment

Fund

PAM PATENAUDE, Habitat for Humanity

JOHN C. WEICHER, Hudson Institute

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ALSO PRESENT:

KANAV BHAGAT, Managing Director of Financial

 Market Research, JPMorgan Chase Institute

LARIECE BROWN, Quantitative Analytics Director,

 Freddie Mac

KELLY COCHRAN, Deputy Director, FinRegLab

KATY DAVIS, Managing Director, ideas42

KEITH ERNST, Associate Director, FDIC DCP

AMELIA ERWITT, Managing Director, Cities for

 Financial Empowerment Fund

LINDSAY FERGUSON, Director of Strategic

 Engagement for America Saves, Consumer

 Federation of America

RYAN GOODSTEIN, Senior Economist, FDIC DCP

JASON GROSS, CEO and Co-Founder, Petal

EMERSON HALL, Associate Director, FDIC DCP

JONATHAN MILLER, Deputy Director, FDIC DCP

DANIEL NESTEL, Senior Director Government

 Relations, FICO

MARK PEARCE, Director, FDIC DCP

LAUREN SAUNDERS, Associate Director, National

 Consumer Law Center

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 C-O-N-T-E-N-T-S

Opening Remarks. . . . . . . . . . . . . . . . . . 4

Update by Committee Members. . . . . . . . . . . . 9

Developments in Credit Scoring and 

Underwriting . . . . . . . . . . . . . . . . . . 109

Mortgage Markets Update. . . . . . . . . . . . . 147

Sustainable Bank Accounts. . . . . . . . . . . . 258

Closing Remarks. . . . . . . . . . . . . . . . . 328

Adjourn. . . . . . . . . . . . . . . . . . . . . 331

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1 P-R-O-C-E-E-D-I-N-G-S

2 8:50 a.m.

3 DIRECTOR GRUENBERG: Good morning,

4 everybody. Jelena got delayed this morning, so

5 she gave me the privilege of welcoming you all.

6 And a couple of things. We have a

7 number of new members to the committee that we

8 want to welcome, and I'm going to introduce them

9 in a moment, a number of members whose terms are

10 coming to an end. And I want to begin the

11 process of thanking them for their service on the

12 committee.

13 Let me just say at the outset that

14 this committee has now been around at the FDIC

15 for over ten years. It has started when Sheila

16 Bair was the chairman. It was continued while I

17 was the chairman. And now Jelena is continuing

18 it as chairman of the agency.

19 And it has proven to be an

20 extraordinarily valuable resource to the FDIC for

21 ideas and energy for addressing an issue that is

22 really core to the mission of the FDIC. We're

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1 fundamentally about providing security to people

2 for their savings, preserving the public's

3 confidence in the banking system.

4 And so the mission of expanding access

5 to the mainstream banking system is really a core

6 part of the mission of this agency. And this

7 committee has been really the driver of all their

8 thinking and work around that issue. So I wanted

9 to begin as a starting point for thanking you all

10 for your willingness to serve and to underscore

11 the value that from our standpoint you bring to

12 the FDIC.

13 If I may, let me now introduce the new

14 members of the committee who are joining. It's

15 an extraordinarily distinguished group. And in

16 alphabetical order, Raphael Bostic is the

17 president of the Federal Reserve Bank in Atlanta. 

18 Raphael has dedicated his career to expanding

19 economic and housing opportunities for

20 underserved families. He previously served as

21 Assistant Secretary for policy development and

22 research at HUD and held a Chair in Governance

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1 and Public Enterprise at the USC School of Public

2 Policy.

3 Don Graves holds the Bruce Murphy

4 chair on this committee. Don is the Director of

5 Corporate Responsibility at KeyBank. His

6 predecessor, of course, at KeyBank was Bruce

7 Murphy who was really a very valued member of

8 this committee, and I think we're extraordinarily

9 pleased that Don is able to continue the service

10 on this committee. He has extensive expertise in

11 small business and economic development in both

12 private and public sectors, previously served as

13 Deputy Assistant Treasury Secretary for Small

14 Business, Community Development and Housing

15 Policy.

16 Maurice Jones to my left is president

17 and CEO of the Local Initiatives Support

18 Corporation or as LISC is known. He comes to us

19 with a wealth of experience in federal and state

20 government, previously served as Deputy Secretary

21 of HUD and Secretary of Commerce for Virginia

22 among many other things.

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1 Margaret Libby is the founder and CEO

2 of MyPath which uses strategies centered on the

3 workplace to bring young, low income workers into

4 the banking system and to encourage savings which

5 are really two core objectives of this committee. 

6 So we're really pleased to welcome Margaret. And

7 she previously worked with the Youth Leadership

8 Institute and the California Reinvestment

9 Coalition.

10 Jonathan Mintz is not a stranger to

11 this committee. He is the founding president and

12 CEO of Cities for Financial Empowerment. He has

13 really led a national effort to expand access to

14 the banking system, to support and sustain local

15 Bank On coalitions, and has been a national

16 advocate for the expansion of safe transaction

17 accounts which is work that came out of this

18 committee I think is fair to say. And I would

19 note Jonathan was the longest serving

20 Commissioner of Consumer Affairs in the history

21 of New York City.

22 And finally, Pam Patenaude joins us

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1 after recently serving as Deputy Secretary of

2 HUD. We have two former HUD Deputy Secretaries

3 and currently serves on the Board of Habitat for

4 Humanity and previously was president of the J.

5 Ronald Terwilliger Foundation for Housing

6 America's Families.

7 So this is a pretty good group, I have

8 to say.

9 (Laughter.)

10 DIRECTOR GRUENBERG: And we do have a

11 number of members whose term on the committee is

12 coming to a close. I wanted to acknowledge them. 

13 Janie Barrera -- we really hate to say -- we like

14 to add. We hate to say goodbye. I have to say

15 that. Wade Henderson, Andrea Levere, John Ryan,

16 Phillip Swagel, and John Weicher. We thank you

17 for your service.

18 And I also want to acknowledge Ted

19 Beck, Kelvin Boston, Ester Fuchs, Pat McCoy,

20 Bruce Murphy, and Manuel Orozco whose terms ended

21 last year.

22 We can now actually move into the

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1 business of this meeting, and I'll turn it over

2 to Jonathan Miller. And we are going to try

3 something different this morning for the first

4 time which is to give you all the opportunity for

5 five minutes or so apiece to share your thoughts

6 and what we ought to be thinking about.

7 Jonathan?

8 MR. MILLER: Thank you very much, Mr.

9 Chairman. So I just want to -- before starting,

10 I want to just remind everybody. In your packet,

11 you will see a sheet advertising our 2020

12 National Interagency Community Reinvestment

13 Conference next March in Colorado. We want to

14 make sure people get it on their calendars as

15 quickly as -- as soon as possible.

16 It's a terrific group of stakeholders. 

17 It's sponsored by the three prudential

18 regulators, the FDIC, the Fed, and the OCC. And

19 it's a great meeting of folks for the purpose of

20 talking about national community reinvestment.

21 So I have -- I'm under strict orders

22 to keep you to five minutes. I have my iPhone

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1 ready to tell me when that comes up. So I'll

2 just start, and we'll just go around the table. 

3 Bob?

4 MEMBER ANNIBALE: Always the "A".

5 (Laughter.)

6 MEMBER ANNIBALE: My childhood

7 memories of being the first seat in the row of

8 every class. I hated it.

9 (Laughter.)

10 MEMBER ANNIBALE: Just -- well, thank

11 you first, again, to those leaving. It's been

12 wonderful working with you, and thank you for all

13 you've shared and we've learned. And for those

14 joining, I'm looking forward to getting to know

15 you better.

16 I think the additional expertise

17 coming on about housing is going to be very

18 helpful for some of us. So we'll look at that

19 intersection of particularly communities working

20 and the challenges of housing. Low income

21 communities and density of that often corresponds

22 to lack of access to many other services,

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1 including financial.

2 I do want to just use this for a

3 moment to say there's something which we're more

4 formally going to announce. But -- and of

5 course, I always do these things. When it's all

6 public, I forget that we are streamed across the

7 hall and elsewhere.

8 That we're doing some very good work

9 with the City of New York, and Jonathan has been

10 very helpful and others in that on something

11 called EmpoweredNYC. And we've talked for a long

12 time about really the lack of access of financial

13 capability and coaching advice for people, the

14 complex lives of people, particularly with

15 disabilities, and those households with someone

16 with disabilities. Anybody navigating through

17 the benefits system, at least in this country,

18 it's a landmine for anybody who has a disability.

19 And we started with New York with the

20 Mayor's office for people with disabilities, and

21 Jonathan's success on the Commission for Consumer

22 Affairs, and a group of very knowledgeable people

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1 to look at the absolute absence of support and

2 material. Also, the National Disabilities

3 Institute joined us for that which was very

4 helpful.

5 And as we look at that, we know that

6 something like 18 percent of people with

7 disabilities are unbanked. That's a big contrast

8 to what we see nationally and other levels which

9 have been improving. These levels actually

10 deteriorated further. And there are only 37

11 percent of people with disabilities who are

12 employed, 70 percent without disabilities are

13 employed. Half of those that are working have

14 $25,000 a year.

15 And so we built a program in New York

16 to really develop deeper coaching and training of

17 financial counselors around the needs on the

18 lives of people with disabilities. And we'll

19 announce in a few days. But that will expand

20 nationally to at least a group, a crop of about

21 five cities to start with, New York, Boston,

22 Chicago, LA, San Francisco and we believe more.

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1 And we're bringing together the

2 commissioners of people with disabilities from

3 those cities and to build out -- invest a couple

4 million dollars to really build out resources and

5 trained counselors, financial counselors for

6 those who are navigating the most complex lives.

7 So I really just wanted to share that. 

8 And did I use my five minutes?

9 MR. MILLER: Almost.

10 MEMBER ANNIBALE: There you go. I

11 have two minutes left. Nothing more though that

12 will come up later to just say I hope that an

13 example of something we've also learned. We

14 opened up our ATM network as we spoke, I think I

15 mentioned before, to adopt a pilot for about 25

16 community banks and credit unions that are in our

17 footprint.

18 And that's pretty -- almost a half

19 million clients of those banks to basically give

20 them access to all cities. The ATMs that we own,

21 it's like 2,300 ATMs in the same footprint with

22 no charge. Because the out of charge fees are

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1 continually going up, anybody who tries using

2 other bank's network which I disastrously did

3 here and realized it costs over four dollars to

4 do that.

5 The idea was to say, can small

6 community banks, credit unions access our ATMs

7 and their clients not pay an out of market fee or

8 out of network? And that's proved very

9 interesting, good.

10 Sixty percent use it for cash

11 withdrawals just out of curiosity. And the

12 average cash withdrawal is about $204. And these

13 are, like, sometimes the Lower East Side Credit

14 Union. They're small groups.

15 And the other 40 percent are really

16 just trying to check their balances, and they do

17 it frequently. But they used to be afraid

18 they'll get charged to use someone else's

19 machine. So we really think there's an

20 opportunity because those who run big networks,

21 this is pretty de minimis. I mean, adding a half

22 million people in a way because the usage is

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1 spread and timely.

2 But for anybody who works is using a

3 minority depository institution or a small credit

4 union. The cost for those institutions of

5 getting into a bigger network or to put in more

6 machines just doesn't make sense. So yeah, we

7 think it's a good experience. We wanted to share

8 it, and we're going to try to expand it.

9 MR. MILLER: Thank you. All right,

10 Janie, your five minutes.

11 MEMBER BARRERA: Thanks, Jonathan. 

12 Well, it is a bittersweet moment being the last

13 meeting. And I'm happy that we got this five

14 minutes to be able to share some of the good work

15 that CDFIs and non-profits are doing across our

16 country.

17 So where other people see risk, we see

18 investments. And so some of that we have seen at

19 LiftFund. We're celebrating 25 years this year. 

20 And what we do is that we work with folks that

21 are unbankable. We do micro and small business

22 lending.

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1 The average credit score, FICO score,

2 one of our customers is 590. And we have a 96

3 percent repayment rate. So today during the

4 agenda to cease talking about underwriting, and I

5 could relate to the stuff that was sent to us for

6 reading because we do need to look at other

7 things besides credit scores to be able to make a

8 determination.

9 Having a 96 percent repayment rate

10 with that high risk of that credit score says

11 that, and we've proven it. We've dispersed over

12 $300 million in the 25 years. Average loan size

13 is $25,000. So we do know that access to capital

14 is so important for these small businesses that

15 aren't able to go to banks and credit unions to

16 be able to get these funds.

17 So we continue to experiment and pilot

18 things. And so some of that is actually with

19 some credit unions. There's a credit union,

20 Suncoast Credit Union, in Florida that we're

21 partnering with to see how we can say yes to

22 their members that they have to say no to. And

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1 we're trying to figure out a way by them not

2 having to reapply for a loan.

3 Because right now in the banking

4 community, we're having -- a bank says no. And

5 then they have to reapply with us. And so what

6 we're trying to figure out and we hope by second

7 quarter of next year to accomplish this where in

8 their system -- in their regulatory system,

9 through NCUA and everyone else saying yes. Our

10 backroom operation at LiftFund is a part of that

11 regulatory box that is doable, so that the

12 application goes in to the credit union and then

13 it directly comes to LiftFund.

14 And so we're hoping -- I mean, that's

15 going to be a big change in our industry because

16 the pipeline of where do we find customers, how

17 do they find out about CDFIs and microlenders

18 like us. We'll be able to share that with a

19 bigger audience.

20 The other thing is that we are

21 creating through the NALCAB, National Association

22 of Community Asset Latino Builders, NALCAB, is

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1 the fact that we are creating a national Latino

2 fund for small businesses. And hopefully we'll

3 be able to go public with that in the next couple

4 of weeks. There's a major bank that has given us

5 a ten million dollar grant to start this.

6 And what that is, the concept is there

7 are a lot of small CDFIs across the country. But

8 they're not able to reach scale because they

9 don't have the cash to be able to provide these

10 loans. So we create a national pool where we can

11 have that ten million dollar equity grant that's

12 come in and leverage it then to borrow more into

13 that fund. Then we'll be able to serve more

14 people.

15 So it's going to be a fund that does

16 not belong to LiftFund. We are going to be the

17 partner in it. We're going to be doing the

18 underwriting. It'll belong to a for-profit LLC

19 within NALCAB. So it's a brand-new concept that

20 we are so excited about creating. And hopefully

21 you'll hear about that in the next couple of

22 weeks.

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1 The other thing about our work as well

2 is that technical assistance is so important. 

3 We've learned over the years that it's not access

4 to capital but it's also the TA that small

5 business people need.

6 We recently did a study, 77 percent of

7 the small businesses that work with LiftFund are

8 still in business three years later. And as you

9 know, startup businesses go belly up within three

10 years. And so to have those kind of statistics

11 is proof to us that we're able to keep that

12 legacy of that small business for the next

13 generation.

14 So maybe you've heard me say before. 

15 You can teach a person to fish. They eat for a

16 day. But what we're helping people do is buy the

17 pond where they're fishing.

18 (Laughter.)

19 MEMBER BARRERA: And we'll be able to

20 leave a legacy for the next generation. So

21 that's what we're attempting to do as well.

22 And then lastly I just wanted to share

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1 because I know our chairwoman, Jelena, is an

2 advocate of de novo banks. And so I've been part

3 for the last three years helping in a small way

4 to start Piermont Bank. So it's located in New

5 York City. It is a woman owned, minority owned

6 bank. And it's been the first de novo bank in

7 New York in ten years.

8 And so we're excited. We opened up

9 doors July 1 and are ready for business. So if

10 you want to make a deposit at Piermont Bank,

11 we're located on Bryant Park. And it's a woman

12 owned, minority owned bank. So we're also

13 looking for investors from bigger banks to make

14 deposits --

15 (Laughter.)

16 MEMBER BARRERA: -- into our bank. 

17 And we'd be happy to entertain any questions you

18 may have on that.

19 (Laughter.)

20 MR. MILLER: Thank you. Raphael?

21 MEMBER BOSTIC: Thank you. I wanted

22 to just first say good morning, everyone. And

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1 it's really a pleasure to be here. I thank the

2 FDIC for the invitation to be a part of this

3 committee.

4 I'm thinking about challenges that

5 we're facing. It's been very interesting. Our

6 bank has just finished a strategic planning

7 process where two issues that we highlighted were

8 the challenge of economic mobility and resilience

9 and issues around innovation in financial

10 products.

11 And we settled on those things for a

12 couple of reasons. One is that the issue of

13 financial wellness, financial stability is

14 something that is critical for just families

15 living and for people making decisions that are

16 going to work and have longer term horizons.

17 So I've encountered issues around

18 financial literacy problems, financial

19 sophistication issues. And we've really been

20 grappling with the idea that the seeds of these

21 are sown very early in people's lives that too

22 often people who are children, minorities, and

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1 women are not getting positive reinforcement. 

2 This is something that they should be experts in

3 or could be experts in.

4 And so we're actually working to try

5 to provide some training and some curriculum for

6 fifth graders and eighth graders to try to change

7 that. And hopefully that will improve collective

8 financial sophistication and also get more people

9 in the pipeline so that our field looks more

10 diverse as we move forward.

11 I would also say that the fintech

12 space is something that is really active. And

13 we've been doing a lot of work to just try to

14 remind people or encourage innovators to think

15 about the ability of their innovations to change

16 people's access and increase people's access to

17 financial services and to the capital that can be

18 helpful.

19 I grew up in New Jersey. So the

20 Southeast is not a place that I knew very well. 

21 And I spent a lot of time in the last two years

22 just traveling around and seeing places. And

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1 there are a couple of themes that have emerged. 

2 More generally, one is affordable housing is a

3 problem pretty much everywhere. The source of

4 that problem changes. So in some instances, it's

5 income. In some instances, there's not enough

6 housing. But it's something that we really must

7 face.

8 Second thing is access to capital

9 which is consistent in communities that are not

10 connected to workforce and to employment

11 networks. And so in that regard, we've been

12 thinking a lot about CRA reform, about how to

13 promote and engage with targeted and creative

14 investments and deployment of capital for small

15 businesses, for community-based amenities.

16 And really we've started to create

17 some relationships with foundations and others

18 just to point out to where there are

19 opportunities. Because in the South, we're

20 underinvested in these things. And we're on the

21 ground, so we are seeing things a bit

22 differently.

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1 And then I would also say that I've

2 been really struck by the difference in the

3 challenges in urban places and rural places. And

4 I would hope that we spend some time just being

5 mindful to the special challenges that rural

6 places have. It's kind of hit me pretty hard. 

7 You look at my map. Most of it is rural. Most

8 of it is struggling.

9 And so issues around this capital

10 access, workforce development, the strength of

11 institutions. And particular issues around

12 community banks, like smaller banks that are

13 often the pillars of these communities, they're

14 feeling particularly stressed. And without them,

15 I think many of these communities are going to

16 really have a hard time finding a future.

17 And then the last thing I would say is

18 we've been -- I've been for a long time thinking

19 about the cost of capital for lower income

20 families. And really thinking hard of how I can

21 get the banks in my district to be creative about

22 providing products that are real competitors for

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1 check cashing operations where it's not charity

2 but it's also still cheaper than what they have

3 today.

4 And we're going to keep working on

5 that. There are some things that I think are

6 quite promising. So thank you. It's really good

7 to be here.

8 MEMBER CISNEROS: Thanks, Jonathan. 

9 Good morning, everyone. Jose Cisneros, San

10 Francisco Treasurer. I'm happy to report that

11 our Office of Financial Empowerment in San

12 Francisco continues to deliver a number of

13 important programs from the longest running Bank

14 On, Bank On San Francisco program, that continues

15 to connect unbanked folks to safe accounts in our

16 city to a number of other programs.

17 Getting youth -- summer interns and

18 youth into bank accounts by the time they get

19 their first paycheck, financial coaching which

20 we're providing to folks to help improve their

21 credit scores and improve their financial

22 situation. A first of its kind children's

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1 savings account program which we do in

2 partnership with CitiBank and our local public

3 school district.

4 And even a new program we're

5 developing with our San Francisco International

6 Airport private employers to try and develop a

7 workers fund to help low wage workers weather an

8 unexpected financial need.

9 But a couple of things I wanted to

10 bring to this committee that's come out of a lot

11 of that work are two important things that we've

12 seen and would love to have help addressing. One

13 is I just think an interesting thing that we need

14 to keep in mind and it's something we've seen

15 through our children's savings account program.

16 We opened our children's savings

17 account program as I mentioned in partnership

18 with CitiBank. And we've opened to date nearly

19 40,000 accounts that folks access by walking into

20 a local bank branch in our city to make deposits. 

21 Quite a different opportunity of access compared

22 to the typical 529 college savings accounts which

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1 are more remote and challenging to access.

2 What we found in our program which has

3 been very successful at getting low income

4 families to save is that fully half of the first

5 deposits that families make into their child

6 savings account, they make it by walking into a

7 bank branch and walking up to a teller window

8 oftentimes with cash. And fully one-third of all

9 the deposits made by all of the families, even

10 the repeat deposits, are still made by walking

11 into a bank branch.

12 I think the lesson we all need to talk

13 away from this is the incredibly important

14 reality that physical brick and mortar bank

15 branches and cash deposits deliver for low income

16 folks who are trying to safely and economically

17 access our banking system. We're going to look

18 to continue that and see how we can expand on

19 that going forward to give them even more

20 opportunities to saving cash.

21 The other thing we're noticing that I

22 also wanted to bring to the committee is through

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1 our Bank On program, while we've significantly

2 reduced the population of unbanked folks in San

3 Francisco, we still find that there is a very

4 challenged population who are blocked from

5 opening up a bank account by negative banking

6 records, by the histories that the banks discover

7 in either check systems or early warning systems.

8 And we have a number of concerns about

9 this because, number one, these systems are very

10 difficult to access by the consumer. They're not

11 at all transparent. People don't know what it

12 says. When you compare it to the access that our

13 country has delivered around credit scores and

14 credit reports, here, it's quite the opposite.

15 There are two primary flags in that

16 system that restrict people from opening up

17 accounts. One is account abuse. The other is

18 suspected fraud. It's not at all clear that

19 these flags are accurate when they're showing up

20 in someone's record. And it becomes very

21 difficult for the consumer to, number one, find

22 out that they're there or, number two, correct

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1 them if they're not correct.

2 We feel that it would be vitally

3 important to find out how we can work with this

4 and improve the access to these systems. And

5 then the other side of the coin is the financial

6 institutions are all over the board in terms of

7 how they utilize these records.

8 So we see very little consistency,

9 very little information about how the financial

10 institutions will rely or not rely on these

11 scores. And so the transparency is lacking there

12 as well. So I bring that to the committee, and

13 hope it's something we can talk about in the

14 future. Thanks.

15 MR. MILLER: Thanks. Very

16 interesting. Martin?

17 MEMBER EAKES: Good morning. My name

18 is Martin Eakes. I work with Self-Help Credit

19 Union and the Center for Responsible Lending. I

20 particularly want to thank you for inviting --

21 credit unions and banks don't always get along. 

22 So I appreciate being admitted into the bank

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1 bastion here.

2 My mother used to say that it's okay

3 to be opinionated so long as you're willing to

4 change your mind. So I forewarn you. She'd

5 given me permission.

6 Self-Help started 35-plus years ago. 

7 Our belief was that if we could understand wealth

8 in families, we could make progress in the South

9 on issues of race. And that without

10 understanding wealth issues, we couldn't

11 understand race in the South.

12 So we discovered early on that one13 tenth of the wealth that white families hold were

14 held by black and Latino families. And our

15 solution for that was trying to figure out how do

16 we create and bring families of color to the

17 national average of homeownership.

18 And we started making loans early on. 

19 I made $100 million of loans to mostly black

20 single mothers, and my banker friends said you

21 can't possibly do this without losing your shirt. 

22 My conclusion being pugnacious back then unlike

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1 now was that, you guys know a lot more about

2 banking than I'll ever know.

3 DIRECTOR GRUENBERG: You've really

4 softened over the years, I have to say.

5 (Laughter.)

6 MEMBER EAKES: I know more about black

7 mothers than you'll ever know because I grew up

8 with them. So we started making home loans. And

9 in that first ten years, $100 million in loans,

10 we had not a single penny of loss over the first

11 decade to families who had been unable to receive

12 a home loan from any other source.

13 We've continued to believe that and

14 despite a lot of the debate about what is the

15 role of homeownership, let me just give you a

16 couple of numbers that have confirmed. We've now

17 done between five and $8 billion of home loans

18 and small business loans, particularly focused on

19 communities of color.

20 The wealth gap has actually gotten

21 worse for us. So my focus right now is I want to

22 keep this idea of wealth and families and

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1 homeownership front and center. Even though when

2 we discovered that the wealth gap was ten to one,

3 30 years ago, I thought that was the most

4 ignominious fact in a great economy, that it was

5 just something that we couldn't tolerate.

6 It's actually gotten worse. In the

7 last 20 years, the actual in real dollars gap

8 between white and black families has gone from

9 $100,000 to $150,000, the gap in real dollars

10 after inflation.

11 So we're moving in the wrong

12 direction, but we also know what the answers are. 

13 When we did 50,000 different home loans through

14 partner banks, we found that from 2005 to 2012,

15 which is a very weird period. Prices were at

16 their peak. In 2012, they had really reached a

17 trough.

18 The families that had become

19 homeowners during this program that we studied

20 over time increased their family wealth by

21 40,000, from zero to 40,000 over that time

22 period, which again is a really hostile

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1 measurement period.

2 And the families that had identical

3 characteristics in every other way but remained a

4 renter at the end of that 2012 had $200 of net

5 wealth. So homeownership is still a really

6 wonderful path into the middle class.

7 Most families do not have cash

8 reserves, both white, black, and Latino and

9 others. But for families of color, 50 percent

10 median has zero to $200 of cash wealth in the

11 household which means you can't sustain very much

12 of a financial crisis and you can't have a down

13 payment to make a home loan. So if we don't

14 figure out how to enable homeownership with down

15 payment assistance, we will never close this

16 wealth gap.

17 The cash reserves are critical and

18 brings us to several of the areas of the FDIC

19 financial inclusion plan, the five different

20 components. Small dollar, I will say to you

21 small dollar is often the fig leaf for bad action

22 that payday lending, direct deposit loans. And

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1 thank you to the FDIC for continuing its rule and

2 leadership and preventing payday lending directly

3 by bank depositories in the form of the, quote,

4 direct deposit advance, and then overdraft fees.

5 All three of these are essentially a

6 trapping mechanism. Payday loans, direct deposit

7 advance, and overdraft fees trap people and take

8 cash reserves out of the household. And my focus

9 is on bringing the black and Latino homeownership

10 rate to the national average. If we allow abuse

11 in this area, we'll never have down payment. 

12 We'll never ever get there.

13 A final thing is that the FDIC has

14 been a leader through lots of different

15 chairmanships in not permitting bank charters to

16 be rented to companies that really are partnering

17 with them solely to use the preemptive effect of

18 a federal chart or a federal insurance to do bad

19 things.

20 Fintech, I've heard this. If I had a

21 nickel for every time I heard, we just need more

22 innovation in the financial services network. 

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1 Mostly, I heard that early on subprime mortgages. 

2 I would be a rich man today just from hearing

3 that. And all I'm saying is that sometimes

4 innovation is really wonderful and we have to be

5 open to it. Oftentimes, quote, innovation is

6 simply a refuge for scoundrels.

7 (Laughter.)

8 MEMBER EAKES: So we have to actually

9 dig into the details and make sure that as this

10 committee we stay focused. I would say that I

11 really appreciate being invited with this

12 esteemed group. I know many of the people on

13 this panel are amazing. The folks in the

14 audience are equally esteemed. And the

15 professional staff at the FDIC are the best, bar

16 none.

17 MR. MILLER: If you'd like to keep

18 going.

19 MEMBER EAKES: So thank you very much.

20 (Laughter.)

21 MR. MILLER: Thank you, Martin.

22 MEMBER EAKES: That was a good one. 

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1 Thank you. That's all.

2 MR. MILLER: Don?

3 MEMBER GRAVES: Well, thank you all. 

4 My thanks to the Chairman and to Marty for having

5 me. Don Graves from KeyBank. And while I am not

6 a black -- I'm a banker and not a black mother, I

7 did have a black mother. So hopefully I know a

8 little bit about what --

9 MEMBER EAKES: You are not who I was

10 talking about.

11 (Laughter.)

12 MEMBER GRAVES: Marty and I are

13 friends. I'll just say start with this to begin. 

14 KeyBank's mission, our stated mission, it's on

15 our website. It's on all of our -- everything

16 that we put out is to help our clients and our

17 communities thrive.

18 And what we mean by that is that we're

19 all about trying to support people's hopes and

20 their dreams by providing them opportunities, and

21 this is the important part, to live a life of

22 dignity. And for too many people in this country

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1 right now, they aren't living lives of dignity

2 because they may have hopes and dreams that have

3 not yet been quashed. But they don't have the

4 opportunity that leads them to that place of

5 living a life of dignity.

6 I think our commitment in this regard

7 shows through what we received last year which is

8 our ninth straight outstanding CRA rating. We

9 had an outstanding CRA rating every exam since

10 the inception of the Community Reinvestment Act. 

11 So I don't know how many there are. I don't

12 think that there are more than on one handful

13 that have received that every single time.

14 What that actually -- how we do that,

15 some of you have heard this from Bruce Murphy in

16 times that he's been here before, the types of

17 products that we put out like our KeyBank Plus

18 Check Cashing product which is meant to directly

19 compete with the check cashers, a very, very low

20 fee, very easy to obtain, go to any of our

21 branches.

22 Our KeyBank Hassle-Free Checking

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1 Account which is a Bank On certified account. 

2 Our KeyBank Secured Card, our Key Cashback credit

3 card which is brand new, very, very accessible,

4 two percent cash back into the pockets of any

5 client as well as any card holder who's not a

6 client. You can also get one-half percent cash

7 back, no caps, no limits, no rotating categories,

8 no annual fee.

9 And the reason I say all of that is

10 because I think it's important that banks like

11 Key and Citi and others that are pushing the

12 envelope continue to offer these types of

13 products.

14 We have a handful of brand-new things

15 that we're trying thanks to many of the friends

16 here at the table. Our E-Z Up program which is

17 an innovative new tool that allows our clients to

18 automatically save money for the payment of

19 debts. For every debit card purchase or

20 transaction, a dollar is transferred into a

21 savings account. And that savings account is

22 then able to -- you're automatically able to then

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1 send money through the E-Z Up program to pay down

2 the debt of your choice.

3 So what we found is that clients could

4 save more than $300 each year using E-Z Up which

5 is a meaningful amount for low and very low

6 income clients. You could pay off a 30-year

7 mortgage, for instance, a year and a half faster

8 by using this. You could eliminate three car

9 payments on a five-year auto loan by doing that. 

10 Or you could pay off a student loan one year

11 faster.

12 To Martin's point, zero dollar

13 overdraft protection option for our accounts

14 because we know that oversight happens. Even if

15 you're well intentioned, we know that things

16 happened to individuals over the course of a

17 month.

18 So we're offering a new no fee

19 overdraft protection for clients' checking

20 accounts linked to a KeyBank savings account. If

21 an overdraft occurs, if you're signed up for this

22 service, the available money in your savings

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1 account goes to cover that overdraft so that you

2 don't end up having the overdraft fees and

3 there's no shortfalls.

4 All of that said, we know that there

5 continue to be major issues in communities. 

6 Oftentimes, it's around financial literacy. And

7 it's less an issue of the -- it's more of a

8 nuanced issue around financial literacy.

9 Most people -- not everyone, but most

10 people understand the basics of how checking

11 accounts work and how the systems work. But it's

12 when you get into the more -- the deeper parts of

13 the financial services industry, the more complex

14 parts of the financial industry that people have

15 real challenges.

16 So we're particularly challenged in

17 terms of outreach around financial literacy I

18 think as a bank and as an industry to make sure

19 that people are getting that deeper level of

20 engagement, deeper level of understanding so that

21 it's not just, should I use a checking account,

22 or should I go to -- should I open a checking

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1 account or should I go to a check casher.

2 It's, well, if I do go to a bank, I

3 don't know what I'm supposed to be doing. They

4 offer 25 different types of accounts. I'm

5 confused. I'd rather just go to the check casher

6 that I know.

7 It's also, as you know, expensive to

8 be poor. So applying a traditional fee structure

9 to accounts for the underbanked instills a deep

10 distrust. So once again, you end up sending

11 folks to the types of accounts that are not

12 particularly friendly to low and moderate income

13 individuals.

14 And then I'll touch on just quickly a

15 couple other issues before I finish. As my

16 friend, Raphael, mentioned, the rural issue is a

17 huge challenge for large institutions in

18 particular. And the Community Reinvestment Act

19 doesn't necessarily help us to achieve our goals

20 in reaching rural poor.

21 And then finally, issues around how we

22 provide credit and apply for it. Right now, the

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1 challenge is access versus pricing and risk. And

2 for institutions like ours, trying to get that

3 balance correct is a problem. I suggest that we

4 consider a range of new options, perhaps a

5 public-private approach like an SBA guarantee.

6 Thank you.

7 MR. MILLER: Thank you. Wade?

8 MEMBER HENDERSON: Thanks, Jonathan. 

9 Good morning, ladies and gentlemen. I'm Wade

10 Henderson. I represent the Leadership Conference

11 on Civil and Human Rights.

12 The Leadership Conference is the

13 nation's leading civil and human rights coalition

14 with over 200 national organizations working to

15 build an America as good as its ideals. This is

16 my last meeting as a member of this esteemed

17 committee, and I want to thank Chairman

18 McWilliams for continuing the important work of

19 this body.

20 I'm also grateful to Chairman

21 Gruenberg and Chairman Bair for beginning this

22 journey 12 years ago and for the foresight you

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1 showed even before the onset of the financial

2 crisis and recognizing that the financial

3 industry would need to take a better, more

4 collaborative and cooperative approach in facing

5 the challenges in the financial industry and our

6 economy at large.

7 Now together the work of this

8 committee has been critical in making sense of

9 the constant changes we've witnessed in the past

10 12 years, from the foreclosure crisis to Dodd11 Frank and through the growth of fintech and new

12 banking services.

13 I really appreciate the willingness of

14 the FDIC to genuinely hear the voices of the

15 civil and human rights community and through the

16 dialogue and the understanding and the

17 relationships that you've engendered. You've

18 also made those of us in the Leadership

19 Conference community better advocates for the

20 people we serve.

21 Now on a personal note, I'm especially

22 grateful for the support you've all shown for

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1 bringing communities of color into the banking

2 system and not just as consumers but also as

3 participants. I'm referring specifically to the

4 recent policy changes to promote Fair Chance

5 Hiring Section 19 policy reforms to make it

6 easier for banks to bring in employees who are

7 rebuilding their lives.

8 Now as our country continues to become

9 more diverse, it's important that the banking

10 industry look more like the communities they

11 serve. And along with the First Step Act, the

12 Section 19 reforms are an essential part of that.

13 Now as I step down, we welcome new

14 voices to the committee. I want to encourage you

15 all to keep up the incredibly good work you've

16 done. I believe that more can be done to advance

17 Fair Chance Hiring and that the FDIC and this

18 committee can help lead the way there.

19 Now second, and Chairman McWilliams

20 who is not here this morning but I wanted to

21 acknowledge it. It was great to recently meet

22 with her and my colleagues from the National

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1 Community Reinvestment Coalition and other groups

2 to talk about it.

3 The FDIC has a critical role to play

4 in making sure that any reforms to the Community

5 Reinvestment Act, and one of our most important

6 but often misunderstood civil rights laws, truly

7 lives up to its name and works to bring more

8 people into the financial mainstream.

9 Now while we need the law to evolve,

10 we also need to be armed with the right data

11 before we commit to changes because the

12 consequences are too great for impacting the

13 racial wealth gap. And we really have only one

14 chance to get it right.

15 Now third, as this committee continues

16 to look at the evolution of banking and the

17 growth of fintech, I want to issue a cautionary

18 note about one particular growing threat to the

19 consumer protection laws.

20 I want to salute Martin Eakes for his

21 comment that there is an ongoing debate over what

22 we call a rent-a-bank practice where some lenders

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1 are taking advantage of relationships with

2 federally regulated banks to do an end run around

3 state usury laws.

4 As states continue to fight the

5 scourge of payday lending and every state that

6 puts payday lending up to a vote has found

7 overwhelming opposition. We would do a

8 tremendous disservice to low income families if

9 we allowed payday-like products to gain new

10 footholds at the federal level.

11 Now fourth, we need the banking

12 industry and its regulators to do their part in

13 addressing the troubling racial gap in

14 homeownership. Homeownership is the primary

15 means that most families build wealth, and

16 housing is the biggest single expenditure that

17 families face every month. And yet black

18 homeownership today is as low as it was on the

19 day that President Johnson signed the Fair

20 Housing Act into law.

21 There are a lot of factors at work

22 here, including the lingering damage of the

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1 foreclosure crisis and the role that local rules

2 play in reducing affordable housing supply. But

3 we need to make sure to do no harm in our housing

4 finance system.

5 And we need to provide a level playing

6 field for lenders to serve all communities and to

7 ensure that credit is as inclusive and as

8 sustainable as it can be. And I hope that

9 continues as a priority for this committee.

10 And finally, as this will certainly be

11 a priority for me moving forward, the biggest

12 financial threat that low income communities of

13 color and our economy faces moving forward is

14 student loan debt.

15 Now while most student lending is no

16 longer done within the banking system, I couldn't

17 agree more with Chairman Maxine Waters that we're

18 going to need the best efforts of the banks to

19 help respond to it because this is your consumer

20 base and this is the economy that you're working

21 to rebuild.

22 And I really want to salute the work

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1 of the Center for Responsible Lending. Full

2 disclosure, I'm a founding board member.

3 (Laughter.)

4 MEMBER HENDERSON: The organization

5 has done an extraordinary job in focusing on the

6 challenges of students attending historically

7 black colleges and universities. And I hope that

8 in the work this committee does, there's also

9 emphasis on that.

10 And so while I say goodbye to this

11 committee, the Leadership Conference and our

12 partners are certainly not saying goodbye to the

13 work that you're all doing. And we're looking

14 forward to continuing these conversations moving

15 forward.

16 It's been an extraordinary honor for

17 me to serve with this committee for the past 12

18 years, and I appreciate your indulgence this

19 morning. Thank you.

20 DIRECTOR GRUENBERG: Thank you, Wade.

21 MR. MILLER: Maurice?

22 MEMBER JONES: Well, good morning. 

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1 And I too want to say thank you for letting me

2 join and learn from this group. I'm going to try

3 -- I'm going to say ditto to everything --

4 (Laughter.)

5 MEMBER JONES: -- that's been said

6 before. That's four minutes of my --

7 (Laughter.)

8 MEMBER JONES: And then add another

9 minute. I think with respect to the key -- I

10 think I'll use the word opportunities instead of

11 challenges facing the communities that we serve,

12 I'd articulate two or three.

13 One is helping members of the

14 community become prepared enough to take

15 advantage of this incredible jobs market that we

16 have in most places. And for the members of the

17 communities we serve, we found that you have to

18 do more than just workforce development to make

19 it happen.

20 And so a bundle of things for us that

21 include financial literacy coaching. If you look

22 in most of the communities we serve, most folks

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1 have either a GED or a high school degree at

2 least. But when you test those members, their

3 literacy and numeracy skill sets are somewhere

4 between the sixth and the eighth grade.

5 And what we have to figure out is how

6 you can quickly at least get them to the tenth

7 grade in those areas so that we can help them at

8 a minimum to get on a pathway to a credential

9 that leads to mental skills type job that pays

10 livable wages and beyond and you can take off. 

11 And that's doable.

12 It's doable largely we found by

13 working with community-based organizations that

14 can actually have access to these folks that have

15 been providing services and so have some street

16 credibility and trust and can actually help them

17 design a pathway. And so we spent a lot of time

18 investing in neighborhood-based or community19 based organizations that can do that kind of

20 work.

21 The second component of that

22 investment is case management around those things

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1 that everybody needs to take care of in order to

2 be a productive and sustainable worker, housing,

3 transportation, child care, accessing the earned

4 income tax credit.

5 And then the third component is

6 actually the workforce piece. It's actually

7 helping people achieve the soft and the hard

8 skills that are relevant to the markets that they

9 live in.

10 So if you're in Jacksonville, the port

11 is still hiring. We're trying to get people

12 employed at the port. If you're in Boston,

13 biotechnology is still hiring folks for jobs all

14 along the economic spectrum. We can help people

15 get prepared to walk into those jobs, to compete

16 effectively for those jobs.

17 So a huge opportunity for the

18 communities that we serve is investing in folks

19 at the local level to help them compete for jobs

20 that pay livable wages. There's an incredible

21 amount of work to be done on that. We as a

22 country are missing an opportunity when we just

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1 focus on the formal school system to do that.

2 This is a team sport that we can make

3 a difference in if we own it. And so we want to

4 get more partners involved in that.

5 A second piece for us does relate to

6 this wealth gap, but I want to come at it --

7 housing is definitely a huge piece of it, but

8 helping people own their businesses or start

9 businesses is another. And investing in small

10 businesses, we're finding as much demand for that

11 in the work that we do as the housing component.

12 And so we're trying to find more ways

13 to take more risk in investing, in particular, in

14 women-owned and minority-owned businesses. And

15 the work to be done there is tremendous. I mean,

16 just to give a -- we work a lot in Boston. And

17 this wealth gap in Boston is as great as I've

18 ever seen anywhere.

19 The Fed did a study a couple years ago

20 which showed that the net worth of a white family

21 in Boston was $247,000. The net worth of a black

22 family in Boston is $8. $8, I mean, it's not even

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1 -- it's not a race. It's not even close.

2 And what we're finding is housing is

3 clearly a component of that. But we also need to

4 find more ways to launch and to create more

5 entrepreneurs in the black community, period.

6 And so one of the things that we have

7 found as a route, if you will, is working with

8 anchor institutions to leverage their procurement

9 power to build businesses and to share and

10 mitigate risks jointly.

11 So an example, we have a partnership

12 with Northeastern whereby Northeastern is using

13 their procurement power to build businesses in

14 Roxbury and Dorchester and Mattapan. We're

15 investing in those businesses, both grants,

16 technical assistance, and loans.

17 They're providing us loan-loss reserve

18 so we can take more risk. And if we can get

19 these companies to the point where they got the

20 capacity to compete with confidence, they're

21 going to spend 15 percent of their spend there.

22 The last thing and this is probably an

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1 odd thing to say in the FDIC world. But the work

2 of reconciling in communities or helping

3 communities to reconcile so that we can work

4 together across race, so that we can work

5 together across class, so that we can work

6 together across geographies. Whether it's North

7 and South Dallas, whether it's Mattapan and

8 Northern Boston, or whether it's San Francisco

9 and the South Bay.

10 That work is tough and it's work that

11 we're finding that if we're really going to make

12 a difference in people's lives, we have to

13 include that as well in our portfolio of tools. 

14 And there's no -- it's more art than it is

15 science and has nothing to do with capital. It's

16 more about how you build a team across folks who

17 for a long time and continue today to live in

18 almost separate worlds in our country.

19 And so that piece too requires an all

20 hands on approach. Thank you.

21 MR. MILLER: Thank you. Andrea?

22 MEMBER LEVERE: So let me begin by

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1 thanking Marty and my fellow committee members

2 for an extraordinary not quite 12 but 6 years.

3 And I want to really give a shout out

4 to the partnership we've had with the staff of

5 the FDIC, particularly the research staff whose

6 data we've been able to incorporate in the

7 Prosperity Now Scorecard and has been a basis for

8 really transformational conversations and the

9 ability of Prosperity Now to do two metrics this

10 year, not just looking at economic performance

11 but also ranking all the states by racial

12 disparities at the same time to see how

13 dramatically -- having just come from speaking at

14 the Boston Federal Reserve Bank on Friday that

15 contrast in that state is quite profound.

16 And I'm Andrea Levere. I'm now

17 President Emerita of Prosperity Now which is very

18 exciting for many reasons. Emerita, yeah. They

19 decided that there is a gender to Emeritus, the

20 board. So that's why they gave me Emerita.

21 So I want to talk about a couple of

22 things that are part of particularly the

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1 innovation not for scoundrels over the last

2 several years as we've lost all the federal

3 programs to help match the savings of low income

4 people. And how do we look at expanding savings

5 and expanding savings in accounts that are safe?

6 And so the framework we've used is

7 savings for now, soon, and later. And we've

8 looked at that framework as a way of really

9 looking at where on the continuum people are,

10 really starting where people are in the

11 beginning. The framework we've used in our human

12 insights research is nothing about us without us.

13 And that really has informed how we've

14 done product design and development and how we've

15 done, I think, two fundamental things, identify

16 the platforms that are the platforms that people

17 use in their daily lives that can be leveraged on

18 behalf of getting people banked and saving. And

19 then being very innovative in cross sector

20 collaborations to leverage the strengths. In

21 many ways, your closing comment, how do we work

22 across many, many silos that used to exist?

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1 So I just want to highlight four of

2 them, several of which have been already

3 identified around this table. Several years ago,

4 we took over the network of community tax prep

5 sites when there was just 200 of them. As of

6 this last tax season, we had 4,000 members of the

7 Taxpayer Opportunity Network who were all using

8 tax time to help people get banked, get

9 registered to vote, and to save.

10 And the innovation and the advocacy

11 network helped us, for the first time in 40

12 years, to authorize VITA and not only prevent it

13 from being cut in half but to virtually double

14 the amount of funding that we have for free tax

15 prep, which I still count as a pathetic amount of

16 money we provide to leverage billions for low

17 income households.

18 One of our key innovations that we've

19 worked on with Kathryn Edin is the Refund to

20 Rainy Day Savings Act which helps people when

21 they get their refund save 20 percent of it for

22 just six months which then prevents them from

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1 doing the downward spiral into the payday lender,

2 keeps them cash flow positive, and helps them

3 move out of that tax year into a very different

4 situation that next year. We're working on that

5 with the District of Columbia, and we also have

6 federal legislation around this.

7 We've also then worked with private

8 sector partners on other forms of emergency

9 savings. We have been working with Treasury to

10 try to do something called the sidecar savings so

11 that when people were taking hardship withdrawals

12 out of their retirement savings, getting

13 penalized for that, and then not being able to

14 put it back to then link that to a Roth IRA so

15 they could build an emergency savings account

16 that was linked to their retirement. We are now

17 piloting that with Prudential with a goal of

18 really scaling that out.

19 Then back to our work which we've done

20 kind of really deep research on the role of

21 housing and the racial wealth divide. We have

22 been piloting with a number of CDFIs mortgage

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1 reserve accounts is one of the things we see

2 happening is low income families often use all

3 their cash, all their savings as Martin said. 

4 And they have very little emergency savings left

5 over.

6 So why can't we automate the creation

7 of an emergency savings account as they're paying

8 back their mortgage and build that? So we're

9 testing that with several of the leading CDFIs to

10 figure out what is the best way to structure it. 

11 And particularly for different communities, what

12 would it look like differently?

13 The third real fourth cross sector

14 collaboration has been our work on health-wealth. 

15 If we want to go where the money is, it is the

16 healthcare institutions as many of you have

17 spoken that have a huge incentive to invest in

18 addressing the upstream determinants of health.

19 And in my other committee on the Fed,

20 we raised this issue at our last meeting to

21 really look at the health-wealth connection and

22 look to see how we build that connection across

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1 communities and build that partner in helping to

2 build short and long-term savings.

3 And then I will finish really speaking

4 about the work with children savings which Jose

5 and I have been tireless advocates for. We

6 reached a whole new level in our Campaign for

7 Every Kid's Future this year with over 700,000

8 accounts with children.

9 And when I was in Boston on Friday,

10 the treasurer's office announced that on January

11 1st, the Baby Steps Program where every child

12 born in Massachusetts is going to start with an

13 account as well.

14 And looking at how this field has

15 grown over time and as we talked about, how do we

16 integrate financial capability and financial

17 knowledge? Rafael, you were talking about that

18 for our hardest age group, middle school kids. 

19 How do we have the actual experience of having an

20 account which then changes all the motivations

21 that they have to go forward? We're working on

22 this with baby bonds legislation as a way of

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1 looking at this to really again help to address

2 the racial wealth gap from the very beginning.

3 The key issues that we think about is

4 how do we engage across all communities starting

5 with the data and then coming up with solutions

6 that they really see.

7 So Jonathan, I know you're going to

8 miss my low key, non-enthusiastic reactions when

9 we have great presentations and informative. And

10 let me just say, Marty, that the partnership and

11 the work you have done here at the FDIC and the

12 advocacy and the convening has been an absolutely

13 critical role in helping to expand the financial

14 well-being of this country.

15 And one final point, two of the women

16 are going off. That will leave only two women on

17 this committee. So please think of that as you

18 add new ones.

19 MR. MILLER: Thank you for all those

20 wise words. Margaret?

21 MEMBER LIBBY: Okay. So good morning,

22 everyone. My name is Margaret Libby. I'm the

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1 founder and CEO of an organization called MyPath

2 based in San Francisco but working nationally. 

3 Excuse me.

4 And I want to thank the FDIC for

5 inviting me to be part of this committee. I'm

6 excited to bring, I'm going to say, a youth

7 perspective. I'm, of course, not a young person

8 myself anymore. But we do work on behalf of

9 youth and young adults and really focus on

10 financial capability.

11 And the work that we've done has

12 really been about how to bring financial

13 capability into youth employment and youth

14 workforce settings to really make sure that when

15 somebody is 14 to 24 and they're having that

16 first employment experience that access to

17 banking and supports around saving and credit

18 building are in place so that that income, that

19 first income stream isn't just about income but

20 is really about wealth building and lasting

21 economic mobility.

22 And so I'll share. A lot of times

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1 when we talk about the work, people don't

2 necessarily have a sense of what the barriers are

3 for low income youth of color in particular. 

4 That's really who we focus on. People don't

5 often understand the barriers that young people

6 face and trying to access banking.

7 And so I'll just share a quick story

8 which is there was a young man who was one of our

9 participants who told a story that when he first

10 earned his paycheck, he was 16. He brought it to

11 the local Safeway to buy a sandwich. And the

12 cashier said, wait a second. This is a check. I

13 can't process this.

14 But actually, okay, you know, he was,

15 of course, embarrassed. And she said, don't

16 worry, sweetheart. Go across the street. 

17 There's a check casher there. You can get the

18 cash and come back and buy the sandwich. Don't

19 worry about it. You're good.

20 And so I think when you think about

21 that kind of experience and that community kind

22 of and those messages that a lot of times like

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1 the program wasn't able to provide the supports

2 and the adults in the community are using check

3 cashers, it's challenging for a young person to

4 get on a different financial path.

5 And so we know that half of young

6 people in San Francisco when we first started the

7 program were using check cashers for those

8 paychecks. And so as we innovated this approach

9 not as scoundrels but as innovators who were

10 working directly with young people to really

11 figure out how do we build a model that will

12 resonate with them, that will be relevant, that

13 they can engage with and really start saving.

14 We figured out how to do that. We

15 were seeing young people saving 30 percent of

16 those incomes even though they were also in a

17 position to need to really support their family,

18 economic circumstances and lives. We partnered

19 with Jose and the city to scale that up and then

20 later with Jonathan.

21 And so there's a number of cities now

22 across the country using this approach to really

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1 bring the banking services into the programs. 

2 And I think one of the challenges -- or I should

3 say into the employment programs. One of the

4 challenges that we continue to face is these

5 barriers that youth face in getting into

6 accounts. And so one of the things that we have

7 been recommending is to have -- especially for

8 15, 16, and 17 year olds who can't open an

9 account on their own without a parent, is to make

10 non-custodial accounts available.

11 And not just the -- we love the work

12 that the FDIC has done around the safe accounts

13 and the non-custodial savings accounts which has

14 been a really enormous step forward. And I think

15 what we would love to see is some focus around

16 how do we do the same thing with transactional

17 accounts or checking accounts, debit cards so

18 that young people have access to that when

19 they're under 18 and can own that account on

20 their own.

21 Because of a lot of young people, if

22 their parents are -- well, if they're foster

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1 children, if their parents are undocumented or in

2 check systems, they're truly excluded from

3 opening an account. And if they're earning that

4 income, we know that there's such tremendous

5 potential if they can be connected to that non6 custodial account.

7 And I think the other key barrier is

8 ID requirements. A lot of people who are minors

9 don't have the government issued photo ID but do

10 have a school ID. And so some financial

11 institutions have been willing to accept that and

12 change their CIP. But we'd love to see some more

13 ways to really support those kinds of efforts.

14 Because I think with some of the Bank

15 On accounts or really with the Bank On 2.0

16 standards, the accounts that are pretty airtight

17 from a risk perspective. So we'd love to see how

18 we can do some more around those transactional

19 accounts.

20 I think the other point that I wanted

21 to make is that we know from research that when

22 we do these early interventions that there's this

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1 generative effect, not just for that young

2 person's life when they set and meet a savings

3 goal. We know the second goal is generally a

4 larger monetary amount and also longer term.

5 So our data has shown that it's really

6 changing their time horizons, increasing their

7 confidence in themselves, not just in their

8 financial choices and their financial lives but

9 in their career thinking and just their own sense

10 of their own potential.

11 But we also know from Pew Research

12 from a few years back that when you're raised in

13 a saving household, you're much, much more likely

14 to be economically mobile. So that we know if we

15 can provide these supports from young people, we

16 know they use them and they do great things for

17 themselves. But it also puts them in a position

18 where they're likely raising children who will

19 also be economically mobile. So I think from

20 that data perspective, it's an exciting approach.

21 How am I doing, Jonathan?

22 MR. MILLER: You're out of time.

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1 MEMBER LIBBY: I'm out of time. Okay. 

2 So I won't talk about the coaching that young

3 people need when they're 18 to 24. Many

4 financial diverse decisions that having a little

5 bit more of some of the coaching, Bob, that you

6 talked about is so essential to give them those

7 supports as they make those decisions. I will

8 not mention that.

9 (Laughter.)

10 MEMBER LIBBY: Okay. Thank you very

11 much.

12 MR. MILLER: Thank you so much. It

13 didn't take her long to learn, did it? Alden?

14 MEMBER MCDONALD: Thank you very much. 

15 My name is Alden McDonald. I'm president and CEO

16 of Liberty Bank out of New Orleans, African

17 American owned bank. We operate a community bank

18 that operates within the rules and regulations of

19 the banking industry.

20 What we bring to the table is

21 information from another aspect of banking in the

22 community. And before I go any further, I want

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1 to thank again the FDIC for its understanding of

2 the work that the minority banks play in the

3 communities of color.

4 As more things in the country begin to

5 reach out into the communities, we still see a

6 tremendous need. And the FDIC has been supplying

7 the banking community with the proper

8 information, the proper statistics, and the

9 encouragement to do more.

10 And so I want to congratulate again

11 the leadership of the FDIC and the staff for

12 providing this information. It's much needed

13 throughout the community.

14 I want to thank the members who are

15 departing our commission here and for the

16 information you have provided all of us. And I'd

17 like to encourage the newcomers to feel very free

18 to express yourself because this task force over

19 the years, we've been very honest about the

20 issues that we face in the community. And that

21 is what has caused the FDIC to create the good

22 work for the banking community. So I'd like to

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1 encourage the new members to really feel free to

2 put it out there.

3 When Jonathan asked for us to share

4 what we see in the community, it reminded me that

5 Congress chartered the first bank in 1870 to

6 assist free slaves to enter into the economy that

7 we live in today. Those challenges still exist

8 today.

9 I've been around for 47 years as a

10 CEO. So I've been through many ups and downs in

11 the economy, and I've been able to see

12 improvement and I've been able to see the economy

13 go back in a negative way. And we are very proud

14 in our institution of the challenges that exist

15 and to try to formulate methods of erasing the

16 challenges that continue to exist in the

17 community.

18 I broke down my comments basically

19 into three different areas of information to give

20 to this body. We see more deterioration in the

21 income gap. The income gap is very real. Some

22 members made reference to the different research

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1 pieces that proves that out.

2 So in the community in the income gap,

3 we're trying to put things together to help that

4 income gap close going forward. And in the past,

5 we've referred to financial literacy and going

6 forward. And in our shop, we're trying to change

7 the financial literacy phrase to increasing your

8 wealth.

9 And even in our employ -- we're in

10 eight different states by the way and ten

11 different cities -- we see this income gap in all

12 of the areas we serve. We see the second part of

13 the affordable housing crisis we call it. It's

14 an affordable housing crisis that we've been

15 noticing now for about 10 to 15 years and it's

16 getting worse.

17 In the affordable housing crisis, the

18 way we see it in the communities coming from a

19 number of different directions. Number one, the

20 income gap, that's real. But the second piece is

21 increased cost of housing through insurance

22 coverage and real estate taxes.

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1 In the communities we serve, we've

2 seen in some communities real estate taxes triple

3 within the last 12 to 13 years and insurance

4 costs triple. We finance a lot of homeowners

5 that can't qualify for the secondary market. So

6 we have a portfolio of about 100 million of

7 people that we have financed over the years who

8 couldn't qualify for the secondary market for

9 whatever reason.

10 And we're beginning to see more and

11 more of the escrow requirement outstripping the

12 P&I and causing higher delinquencies. And when

13 you take a look at the income gap, what we've

14 seen is more and more of the consumers using

15 credit card debt to cover that gap which is

16 putting them deeper and deeper into financial

17 trouble.

18 We've been looking at this, and we're

19 trying to find different ways of helping to

20 overcome this, not only from a safety and

21 soundness position. And I want to probably say

22 that we foreclose on less than three homes a

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1 year. And we have credit scores down to the 550

2 range, and we do 100 percent financing. And so

3 we've been able to do all of these things in

4 support of not only wealth building but

5 homeownership.

6 In New Orleans today, we have

7 convinced some of our leadership to look at how

8 we could adjust the real estate tax issue because

9 the real estate tax piece continues to go up as

10 property values go up.

11 And so we're looking at it. And

12 within the next six months, we think the city is

13 going to incorporate some changes where we can

14 freeze the real estate taxes for certain

15 communities as well as certain income groups. So

16 it's a policy change.

17 And when you take a look at some of

18 the other issues on affordable housing, I think

19 we continue to talk today that homeownership

20 assistance for 80 percent family income -- median

21 income is where the assistance is.

22 We're finding that the 80 percent rule

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1 can't afford the house anymore. So how do we

2 begin moving that up to 100, 110 percent of

3 median income? Because that's where some

4 assistance is needed. Otherwise, we're going to

5 go backwards.

6 We began to see the deterioration in

7 those neighborhoods and the lack of increase in

8 value. In those particular neighborhoods, we

9 have a lot closures of shopping centers, shopping

10 malls which continues to decline the value in

11 these neighborhoods.

12 I make that association simply because

13 I think going forward we need to concentrate on

14 some policy changes, centered around wealth,

15 centered around real estate, centered around

16 homeownership. And some of the policy changes

17 are very, very important for us to make sure that

18 we stop the blight that's taking place now in the

19 urban communities.

20 Two additional things I'd like to just

21 put on the table. The cost of construction

22 continues to increase, and I know that's the

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1 marketplace. But we have to find some policy to

2 offset some of that cost.

3 I was recently in St. Louis trying to

4 assist a neighborhood of coming back and the

5 developers are still talking about housing costs

6 in those neighborhoods above $150 a foot. So

7 when you begin looking at that, begin looking at

8 individuals in those communities, their income

9 levels, that's not going to work. So we need an

10 overhaul of the policy changes.

11 The other piece that we're doing for

12 wealth building and we started with our staff of

13 teaching individuals what to do with their

14 retirement dollars and how to invest. A lot of

15 people have 401(k)s and they don't know what to

16 do with it.

17 So we started some adult classes

18 within our community in helping individuals

19 navigate through how to invest their retirement

20 dollars so that they can at least have some type

21 of life after work.

22 And the other thing that we're looking

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1 at -- and I don't know whether we're going to be

2 successful at it. But we're looking at a summer

3 camp for youth to teach them about the

4 marketplace, teach them about investments, teach

5 them about how to use their income dollars when

6 they become wage earners. And we'll keep you

7 posted on that.

8 Thank you.

9 MR. MILLER: Thank you very much. 

10 Jonathan?

11 MEMBER MINTZ: Thank you. I'm so

12 happy to be here. I'm really honored and there's

13 few partners that rival the FDIC. And thank you

14 to Jonathan. I'm the other Jonathan in this

15 room.

16 I appreciate the opportunity to sort

17 of use this group and the audience to think

18 around an issue that we've been struggling with

19 at the CFE Fund.

20 As you know, the issue, not to hide

21 the ball, is really trying to bridge the divide

22 between tech advances and, in particular, the

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1 rise of branchless banking and the need for

2 something Jose mentioned earlier which is the

3 ability to make cash deposits and to be able to

4 cash checks. And it is that divide that I want

5 to talk about for a few minutes as we wrestle

6 with it.

7 The CFE Fund runs the Bank On

8 initiative. We're working with about 80

9 coalitions around the country supporting their

10 work. We certify accounts that meet the national

11 standard which was, as Marty mentioned,

12 absolutely inspired by, built upon, advised by

13 the Safe Accounts Pilot here at the FDIC.

14 And we've been learning some

15 interesting things in our efforts and in our

16 partners' efforts to connect people ideally

17 through municipal programming into these

18 certified accounts.

19 We partnered with the Federal Reserve

20 Bank of St. Louis that worked with four of our

21 financial institution partners, our seed partner

22 at Chase, Wells Fargo, Bank of American, and U.S.

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1 Bank to collect information about how those

2 certified accounts were doing and how people were

3 doing in those certified accounts.

4 We are about to come out with data for

5 2018. But at the moment, I'm allowed to talk

6 about data from 2017. And the data was really

7 instructive and exciting. There were about three

8 million accounts just in those four institutions

9 that were opened in that year, 600,000 alone had

10 been opened in that one year in just those four

11 institutions.

12 And what we found was that 72 percent

13 of the people that were opening these accounts

14 were new to that financial institution. So I do

15 think that we are largely talking about the

16 population that we are together trying to serve

17 and to connect into or back into the system.

18 The conundrum that we have is that

19 while about two-thirds of those people were

20 interacting with those accounts in a tech

21 friendly way. They were employing various

22 technological capabilities of the financial

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1 institutions. The truth is only a third of them

2 have direct deposit and 87 percent of them were

3 making regular cash deposits into those accounts

4 with a monthly average of just under $300.

5 So the simple truth is that even as

6 people are successfully reentering the system

7 thanks to safe accounts offered by many of the

8 folks at this table, as the market begins to

9 increasingly incorporate branchless banking and

10 branchless bank providers. And many of them are

11 coming to the CFE Fund seeking certification for

12 their accounts as being Bank On compliant.

13 The truth is we're trying to figure

14 out as we take a look at our standards which

15 refreshes every two years. The best piece of

16 advice I ever got from the FDIC is really to

17 figure out, well, if we are going to be

18 embracing, we -- and Bank On, we as a community

19 if we are going to be embracing the role that

20 branchless providers can play with good accounts,

21 then what are our expectations? Or from a Bank

22 On perspective, what are our standards in regard

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1 to that part of the banking equation which is not

2 all online.

3 And so that's, simply put, the issue

4 that we're wrestling with. And over the course

5 of the day and breaks and afterward, we would

6 really appreciate any people's thought on that

7 subject. Thank you.

8 MR. MILLER: Thank you very much. 

9 Pam?

10 MEMBER PATENAUDE: Good morning. And

11 I'd like to also add my thanks to Chairman

12 McWilliams for inviting me to serve on this

13 advisory board. And to those of you that will be

14 departing, I'm very sad because I've heard some

15 really fascinating ideas. And my brain is just

16 spinning on what to share with you.

17 So I'm going to focus just on two

18 recent experiences and ongoing work with the

19 community in Puerto Rico. I'm going to touch on

20 the easy one first. But I wanted to share my

21 experience with Habitat for Humanity.

22 I recently joined the board, but I've

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1 been working with Habitat for more than two

2 decades from my work at HUD. But this was my

3 first hands-on experience where I worked

4 alongside 21 homeowners for a week at the Carter

5 Build.

6 And some of these families applied

7 four, five, six times before they were eligible

8 to be selected as Habitat homeowners. And some

9 of the challenges were the language barriers. 

10 The family that we worked with on the house that

11 I worked on was from the Congo. And if we had

12 not had the translator there, we never would've

13 learned the story.

14 So I just wanted to touch on that. 

15 And then second, Puerto Rico, I'm very passionate

16 about Puerto Rico. As most people know when they

17 google me, that's the first thing that comes up. 

18 And the challenges are tremendous.

19 But because of my appointment to this

20 advisory board, I started thinking about the

21 opportunities and the connections. And this was

22 my conversation with Liz. Is Liz in the room? 

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1 Okay. I have not met her in person.

2 Thousands of families are unbanked if

3 that's the proper terminology to use. In Puerto

4 Rico, there's an informal economy. More than

5 half of the housing is informal. Title issues

6 are tremendous challenges. Not the first time

7 that disaster survivors have been faced with

8 title issues. We encountered that in Louisiana. 

9 But in Puerto Rico, it's a much higher

10 percentage.

11 So these families are not currently

12 connected to the regulated banking system. And I

13 have a thought about how that could happen,

14 although I can also think of some potential

15 criticism of this. But somewhere around $90

16 billion of federal funding will flow into Puerto

17 Rico. Only a fraction of that has through the

18 FEMA fund has actually reached Puerto Rico. Only

19 one percent of the $20 billion of CDBG-DR has

20 been dispersed in Puerto Rico.

21 So through some of these programs,

22 whether it'd be small business loans or

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1 reimbursement for rehabs on homes, this money is

2 going to change hands from the federal government

3 through Puerto Rico and then to the disaster

4 survivor. And if direct deposit was required,

5 that would be a nexus to the banking system.

6 I brought this up with one of my

7 colleagues that I'm working with right now, my

8 consulting business. That would be

9 discriminatory. So then I worried about even

10 bringing it up until I could really think this

11 through. So I think this committee could be very

12 helpful in helping our fellow citizens in Puerto

13 Rico access the financial mainstream, if you

14 will.

15 So thank you again for this

16 opportunity. I look forward to working with you,

17 not just on Puerto Rico.

18 And just one final comment. When I

19 did talk to this colleague of mine, he mentioned

20 that in Florida Walmart was cashing the checks

21 for the disaster survivors. And I didn't know

22 Walmart was in the banking system. So they're

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1 leaving with cash in their hands then. And that

2 certainly is not a safe way to save.

3 Thank you.

4 MR. MILLER: Thank you very much, Pam. 

5 John?

6 MEMBER WEICHER: Thank you, Jonathan,

7 and thank you, Marty, for appointing me to this

8 committee half a dozen years ago. I have enjoyed

9 the meetings and I have enjoyed my colleagues. I

10 have learned a lot, and I am very grateful.

11 I'm with a different kind of

12 organization than the organizations that all of

13 you are with. We are a think tank. We don't run

14 the kinds of activities which you have been

15 describing, although we have from time to time

16 gotten involved, particularly with state

17 governments.

18 And I'm particularly pleased with a

19 project we had which created a new property tax

20 reversion system for the State of Michigan to

21 replace the system that had been enacted right

22 after the depression of 1893 so that farmers

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1 would be able to keep their property if there was

2 a bad year of crops. They wouldn't lose their

3 farm.

4 That system is one major reason why

5 Detroit is famous -- has been famous for

6 abandoned housing. And on a lesser extent, so

7 had most of the other towns and bigger towns in

8 the state of Michigan.

9 But we are a think tank, and we cover

10 the world. And you may have missed our session

11 last week on policy challenges in the Arctic and

12 around the Arctic which attracted a respectable

13 crowd of people who know something about the

14 Arctic, not including me.

15 Wade mentioned that the committee was

16 started 12 years ago. And as an economist, I

17 think of 12 years ago in a different context. 

18 Twelve years ago -- 12 years ago and two months

19 is the start of the Great Recession. And it has

20 been the worst recession. It was the worst

21 recession that we've had in this country since

22 the end of World War II. And it has been the

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1 slowest recovery from any economic downturn that

2 we've had since the 1930s.

3 And listening to my colleagues, you

4 have been working in an environment where the

5 wind is dead against you and has been against you

6 for as long as this organization has existed and

7 certainly as long as -- for a long part of the

8 time that you all have been working in this area.

9 And several of my colleagues have

10 talked about wealth in America. And in the first

11 couple of years after the onset of the Great

12 Recession, American households lost a quarter of

13 their wealth which is an impressive achievement

14 in its way.

15 We had $88 trillion in wealth among us

16 in 2007 and $66 trillion among us in wealth in

17 the year 2010. And it hurt everybody. It hurt

18 the rich and it hurt the poor.

19 But if you were in the riches 20

20 percent of the population of the United States,

21 you lost about 11 percent of your wealth on

22 average. And if you were in the poorest 20

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1 percent of the population of the United States,

2 you lost half of your wealth on average in three

3 years.

4 Not that there was a lot to begin

5 with, but there was a lot less and in a quite

6 short period of time. Things have been improving

7 since then, but it's been a slower process than

8 we've experienced in the aftermath of other

9 economic downturns.

10 And also looking at the changes that

11 have occurred for different groups in the

12 population, white households and other households

13 which are primarily Asian Americans in the data

14 are not too far below where they were on average

15 ten years ago. Black and Hispanic households are

16 farther away from where they were ten years ago

17 on average.

18 It hasn't been a good time for a lot

19 of people, and I kept thinking about as I was

20 listening to what you all have been doing over

21 this years of this weak economy. I think in the

22 housing world -- in my background in the housing

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1 world, we have seen a decline in homeownership

2 which has only started to turn up about three

3 years ago and has a ways to go before it catches

4 up. And I know we're going to hear about some of

5 that this afternoon.

6 It's been -- it hasn't been a great

7 market. It hasn't been a great time for those of

8 you who are involved in housing. And I'm pretty

9 sure it hasn't been a great time for those who

10 are making loans to people or mortgages and

11 rehabilitation, renovation, whatever it might be.

12 It seems likely the work of this

13 committee over the next few years and the work of

14 your organizations will be with a little more of

15 a headwind -- a little more of a tailwind. I'm

16 not good at weather. It'll be a fairer climate

17 for you, but it will still have a ways to go.

18 So I want to wish you all well. My

19 colleagues who have been on the board, I have

20 enjoyed meeting with you, talking with you,

21 learning from you. And to Jonathan and to the

22 staff, I have enjoyed the presentations.

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1 I cannot finish without mentioning my

2 suggestion to the FDIC is to find a principle of

3 selection for speakers that is not alphabetical.

4 (Laughter.)

5 MEMBER WEICHER: I have sat in this

6 chair --

7 (Laughter.)

8 MEMBER WEICHER: -- and this

9 microphone, which I think works even though it's

10 not attached to anything, for many meetings

11 wondering if I was going to have anything left to

12 say after all my colleagues have spoken. And I

13 want to thank you for leaving me room for things

14 to say over these last few years.

15 And thank you, Marty.

16 MR. MILLER: Thank you very much. I

17 think we're scheduled for a 15-minute break. And

18 that was terrific hearing from everybody. Thank

19 you very much. See you in 15 minutes.

20 (Whereupon, the above-entitled matter

21 went off the record at 10:27 a.m. and resumed at

22 10:45 a.m.)

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1 MR. MILLER: So for our first panel

2 today, the moderator is going to be Keith Ernst. 

3 He's Associate Director for Consumer Research in

4 a Division of Depositor and Consumer Protection. 

5 I'll hand it over to Keith.

6 MR. ERNST: Great. Thank you,

7 Jonathan and good morning, members of the

8 committee. And I thought about how to place the

9 panel in some context. And I thought back to

10 results from our most recent survey.

11 In that, the FDIC reported that one in

12 five households didn't have access to mainstream

13 credit in the last 12 months. As a result, they

14 likely lacked a credit score which if you think

15 about it, it really means if they have a need for

16 credit when challenges arise, they're going to

17 have a hard go of it.

18 And this is true, even though a large

19 majority of these households have a bank account. 

20 Many other consumers have a thin credit file,

21 applies only a limited basis for evaluating their

22 creditworthiness.

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1 Well, not all these consumers will

2 want or necessarily have the ability to repay

3 credit. These observations illustrate the

4 benefit that can flow from advancements in

5 underwriting technologies.

6 This morning, we're fortunate to be

7 joined by four panelists who have considerable

8 experience and expertise to help us consider

9 related developments.

10 First, we'll hear from Jason Gross,

11 CEO and co-founder of Petal, a credit card

12 company that works to deploy innovative

13 underwriting and to extend credit to a broad set

14 of consumers.

15 Next, we'll hear from Kelly Cochran,

16 Deputy Director of FinRegLab who will share

17 results of research exploring how cash flow based

18 underwriting may complement traditional credit

19 measures.

20 Following Kelly, Daniel Nestel from

21 FICO will discuss the company's efforts to use

22 information beyond traditional credit bureau data

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1 to better score consumer's credit risk.

2 Finally, we'll conclude opening

3 remarks with Lauren Saunders from the National

4 Consumer Law Center who will point out some

5 potential concerns that attend these

6 developments.

7 Now in addition to questions that

8 Lauren might raise, I expect that members of the

9 committee will have questions of your own. I'd

10 ask you to please hold your questions until after

11 the presentation. And knowing the committee, if

12 I don't, we will not get to --

13 (Laughter.)

14 MR. ERNST: I think it's going to be

15 a rich conversation. I know this is an area of a

16 lot of interest, if we think about what are the

17 opportunities to expand access and use

18 small-dollar credit within the financial

19 mainstream. Developments like the technologies

20 we'll hear about today are a potential concern

21 and a promising avenue. And so without further

22 ado, I'm going to pass the floor over to Jason

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1 Gross.

2 MR. GROSS: Thanks very much, Keith. 

3 And before I begin, I would like to thank

4 Chairman McWilliams and the larger FDIC for

5 hosting us today. I think it's a really

6 important topic that we're discussing obviously,

7 but with some really exciting new developments

8 that are occurring and have occurred over the

9 last couple of years.

10 I want to maybe begin -- and I have no

11 slides for my presentation. But I want to begin

12 by putting my organization in context so you have

13 some understanding of what we've done, the data

14 that we've seen, some context for our learning,

15 et cetera.

16 We started this organization known as

17 Petal about four years ago and began talking

18 about issues related to credit access quite some

19 time before that. We are a purpose driven

20 organization in that we started the firm setting

21 out solve some particular problems that we saw in

22 the marketplaces.

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1 And in particular, we were very much

2 sort of inspired by the work that we had seen

3 from the public and private sectors six, seven

4 years ago around measuring access to credit and

5 starting to understand folks that we've referred

6 to as credit invisible, thin file, the size of

7 that market and the problems that result for

8 consumers and for families.

9 This was a problem that was -- it was

10 personal to our founding team. We had a founding

11 member of the team, it's a first generation

12 immigrant to the United States. And despite his

13 income, his relationship with a U.S. financial

14 institution, he was unable to qualify for

15 mainstream credit. So he had sort of a personal

16 experience with the system.

17 But of course, as the CFPB and others

18 have pointed out, there's a tremendous number of

19 Americans that lack a full credit file such that

20 you can generate an accurate credit score, 45

21 million Americans with no score, tens of millions

22 of additional thin file consumers.

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1 And demographically, these consumers

2 are not a cross section of the U.S. population. 

3 They are disproportionately young. They are also

4 disproportionately minorities, blacks and

5 Hispanics in particular, low and moderate income

6 consumers and first and second generation

7 immigrants.

8 We looked at this problem and had kind

9 of a similar insight into what Keith mentioned in

10 the overview that while these consumers may be

11 invisible from a traditional credit score

12 perspective, they're certainly not invisible in

13 the larger marketplace.

14 And in fact, the vast majority of them

15 have plenty of other financial data, the type of

16 which we use every single day in the underwriting

17 of other sorts of financial products that today

18 is not being used in the decision to extend

19 small-dollar credit like a credit card, for

20 instance.

21 And not only does that data exist but

22 it is becoming digital, standardized, and

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1 accessible through the control of the consumer to

2 use, to verify their identity, and to confirm

3 their creditworthiness and their ability to repay

4 a loan.

5 And we set over the following four

6 years to build technology and consumer financial

7 products that could leverage all of that

8 additional information. And I hesitate to call

9 it alternative information because really what

10 I'm referring to are digital bank statements, the

11 direct measurement of a consumer's ability to

12 pay.

13 The verification of income, liquid

14 assets, other monthly expenses, these are things

15 we've been using to underwrite loans arguably for

16 hundreds of years, right? But we built

17 technology to access the data and to underwrite

18 it in an automated rather than a manual process

19 which, of course, allows you to extend it in a

20 cost effective manner to small-dollar lending.

21 Today we offer a credit card product

22 that's specifically designed for these consumers

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1 that are new to credit. We're able to leverage

2 the rest of their financial picture, the rest of

3 their financial data to underwrite them for the

4 product, including if they have no traditional

5 credit information at all. We can generate our

6 own proprietary score.

7 About 70 percent of our overall user

8 base is thin file or credit invisible today. And

9 we are able to extend to them credit at lower

10 cost than they would get anywhere else in the

11 marketplace because of the additional data that

12 we can use to price that risk.

13 And I think over the course of the

14 panel, I'd be happy to speak to our learnings so

15 far in operating this product in the market. And

16 we've been very fortunate to collaborate with

17 some of the other panelists as well on these

18 issues over the last couple of years.

19 MS. COCHRAN: Hello. My name is Kelly

20 Cochran. I'm the Deputy Director of FinRegLab

21 which is a new non-profit research organization

22 that started in 2018. We're focused specifically

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1 on technology and data and financial services to

2 look at ways to drive financial inclusion and

3 encourage safe and responsible products.

4 Our first project -- major research

5 project has focused on use of cash flow data. 

6 And I joined the organization earlier this year. 

7 So I'm really happy to present our results.

8 We're in the process of publishing a

9 number of papers about our project. The

10 empirical results which are mostly what I'm

11 focusing on today came out this summer. A second

12 report looked at small business lending in

13 particular and how this data is being used in

14 that market. The third report will look more at

15 the consumer market and our policy issues

16 overall. It'll be forthcoming this fall.

17 So one thing that we feel is really

18 important to start with is just what is cash flow

19 data because it means different things and people

20 use it in different ways. What we primarily

21 looked at were records from consumer and deposit

22 and card accounts. But it can also include bills

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1 and receipts. So not all of it is electronic. 

2 And some of the participants in our study did use

3 paper sources as well as electronic.

4 And then in the small business world,

5 there are additional sources, things like

6 accounting software feeds, payment processor

7 records, e-commerce records, and other sources

8 that can give at least a partial view of cash

9 flow. Although they may not show the business'

10 entire set of operation.

11 As Jason has already said, one of the

12 things -- in some sense, this data has been used

13 for generations for underwriting. What's really

14 different here is that people are collecting more

15 of it electronically and using algorithms and

16 highly automated processes to do very

17 sophisticated, consistent analyses to derive more

18 insight from this kind of information.

19 When we set out to do our project, we

20 had gathered data from six companies on all known

21 banks, really a range of different products and

22 services. All of them are providing some form of

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1 unsecured credit, but it really varies.

2 Two were small business -- focused on

3 the small business market, four on consumer. And

4 we had two CDFIs, a number of fintechs. So a

5 really broad range of companies that are serving

6 different markets. Their product structures are

7 different, so really a range. It was all

8 unsecured, relatively short term. Up to about

9 four years is the longest, a long length. But

10 really a variety and a rich source.

11 We ended up having to study the

12 results -- the loan level results for each

13 company separately. We couldn't combine the data

14 sources because it was just too diverse to make

15 that practical. And when we did that, we working

16 with Charles River Associates and set out to

17 basically analyze the three research questions

18 that are on the slide.

19 The first was just, like, a general

20 predictiveness to find out how predicted is this

21 data. And where we could and had the data

22 available, we also benchmarked it against more

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1 traditional metrics, traditional credit scores

2 and attributes. And in some places, we were able

3 to combine the two sources and see what that

4 further did to the predictiveness.

5 The second question was looking at

6 inclusion. All of the companies that we worked

7 with have a particular focus on serving

8 underserved markets. We wanted to get some sense

9 of whether they were, in fact, able to reach

10 applicants that might have trouble accessing

11 credit under more traditional metrics and data

12 sources.

13 And the last question was to begin to

14 look at the fair lending effects to see if there

15 were differences in the results.

16 We had several different findings

17 which I'll just kind of march through. But I

18 want to save some time for the graphic on the

19 next page. In terms of the predictiveness, the

20 overall results were quite compelling where we

21 had loan level data and were able to analyze. We

22 found that the cash flow data was in fact

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1 predictive.

2 Different companies were using

3 different types of variables. It really ranged. 

4 But in general, they were looking at things like

5 inflows, outflows from the account, what's the

6 balance in the account, are there negative

7 balance incidents, those sorts of things. Really

8 getting a sense of how the applicant is managing

9 their finances on an ongoing basis.

10 And what we found was that it was, in

11 fact, quite predictive. The cash flow metrics

12 generally performed as well as the traditional

13 metrics that we had available for comparison. 

14 And in some cases actually performed better. And

15 then the other really interesting thing is that

16 when we were able to combine the two sources, we

17 often found an additional source of lift.

18 And so what seems to be happening here

19 is that cash flow data is actually telling these

20 companies something slightly different than what

21 the traditional scores are telling them which

22 kind of makes sense if you think about the nature

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1 of the data because it's showing not just the

2 expense side but also the income side and some

3 sense of overall reserves over time.

4 And so it's complementary and it is

5 actually providing lift even for consumers or

6 small business applicants who do have traditional

7 scores. It is adding value even in that

8 situation.

9 In terms of inclusion and fair

10 lending, our data was a little bit more limited

11 in these two areas. But we were able to study

12 what we could. What we generally found was that

13 there was evidence that the various study

14 participants were able to reach different

15 applicants who might have been underserved.

16 In some places, we were able to look

17 at traditional score ranges. Some places, we had

18 income information and looked at geography in

19 various different metrics. There was not a

20 consistent metric that we could across every

21 single participant. But the results were

22 encouraging.

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1 And then on the fair lending side, we

2 weren't able to do a full fair lending analysis

3 or the type that a company would do for itself in

4 a compliance exam. But what we could do was use

5 a proxy analysis to determine likely demographic

6 groups, both in terms of race and gender. And

7 then compare the results for each of those groups

8 separately to see what the predictiveness was.

9 What we found was that the data was

10 general predictive for all of the groups. There

11 were not large variations between the groups. 

12 And so what seems to be happening here that it's

13 not proxying for race or demographic status. 

14 What it is doing is providing independent

15 predictive value.

16 That's certainly encouraging and it's

17 consistent with -- it's a similar test to what

18 the Federal Reserve Board did when they were

19 studying traditional credit scores several years

20 ago in a report to Congress. But again, it's not

21 the full picture and there are many things we

22 don't know.

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1 We do know that there are some

2 differences, for instance, in transaction account

3 between different demographic groups that certain

4 groups, African Americans and Hispanics, are more

5 likely not to have transaction accounts as well. 

6 So this is not likely to make all demographic

7 differences disappear. But it is an encouraging

8 sign that it may be useful information in this

9 space.

10 This is the graphic that I wanted to

11 explain. It's a little bit complicated, so stick

12 with me.

13 (Laughter.)

14 MS. COCHRAN: Where we were able to,

15 we differentiated for each of the participants. 

16 We differentiated the output of the borrowers

17 based first on their traditional metrics. So

18 that's along the -- in each of the rows is a

19 different cluster of borrowers with similar risk

20 profiles using traditional metrics.

21 And they we differentiated them by

22 cash flow metrics, and there's a couple of things

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1 that stand out. What this is doing is showing

2 default rates and how close they are to the

3 median for this particular participant.

4 When most people read this, I think

5 the first thing they notice is that there's a lot

6 of red in the top corner and a lot of green in

7 the bottom corner. Now those are places where

8 the two metrics are predicting the same thing,

9 and the default rates are kind of what you

10 expect.

11 But what's really interesting is if

12 you go row by row across, what you see is it

13 starts red and goes towards the green. And

14 that's the differentiation that we're talking

15 about because in each row, these borrowers are

16 ones that are ranked as being about as risky

17 using traditional metrics. But when you look at

18 the cash flow metrics, they start to separate

19 out. You can differentiate risk.

20 So that's -- it's a visual

21 illustration of the effect that I was talking. I

22 should say in the gray rows, if there were less

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1 than five observations, we had it gray. So

2 that's why there's a different color there.

3 There aren't necessarily equal numbers

4 of borrowers in every single one of these cells. 

5 So it does vary a bit. But one of the things, we

6 weren't able to study this. So we don't know for

7 sure based on empirical analysis.

8 But one of things that's maybe shown

9 particularly along the right margin where there's

10 a lot of green, these may be thin file consumers

11 or borrowers. Or they may be people who have had

12 marred credit who have actually recovered so that

13 their finances are more stable. But their

14 traditional scores may still be relatively low. 

15 So we weren't able to study that for sure. But

16 that's certainly one hypothesis for what may be

17 causing some of this spread.

18 So just really briefly to talk about

19 our next couple of reports. We are seeing an

20 evolving market, particularly in our second

21 report.

22 As we detailed, there do seem to be a

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1 number of different types of institutions

2 including both large and small banks as well as

3 community development organizations and even some

4 companies who are really actually not primarily

5 financial services companies who are getting into

6 this space and using cash flow data in some way

7 or another to make loans to borrowers.

8 And in particular, they seem to be

9 focusing on the smaller side of the small

10 business market. There's a lot of demand, and

11 it's often very difficult to serve that market in

12 a cost effective way. So that's an encouraging

13 sign.

14 In the consumer side, our sense is

15 that the adoption is a little slower on the bank

16 side. But we are seeing it continue to grow. 

17 And people are certainly using this kind of data

18 for verification, even if they're not actually

19 feeding it into their underwriting processes.

20 And going forward, as I said, we'll

21 also be looking at some of the policy

22 considerations. This slide just outlined some of

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1 them. And I know Lauren is going to talk more,

2 so maybe we can save that for the overall

3 discussion.

4 Thank you.

5 MR. ERNST: Thank you, Kelly. Daniel?

6 MR. NESTEL: I'm Daniel Nestel. I

7 lead government relations for FICO. I'm based

8 here in the Washington, D.C. area.

9 Just as a way of background, I suspect

10 many of you are familiar with FICO because of our

11 work in credit scoring. But you may not be as

12 familiar with some of our work in other areas.

13 Back in the 1990s, we pioneered a

14 fraud detection solution in the payment card

15 space. Today we protect 2.6 billion credit

16 cards, debit cards globally across 9,000 banks. 

17 We have just announced that we are developing a

18 platform to address the new landscape of

19 real-time payments and the convergence of fraud

20 and financial crimes.

21 We have solutions -- leading solutions

22 in the cybersecurity space, the financial crime

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1 space. And we use analytics and AI and ML and

2 all of the buzzwords to help businesses make

3 quicker, more efficient, more informed decisions.

4 Everything from airlines that use our

5 analytics for logistics. We allocate gate

6 allocations for their planes that are arriving at

7 airports to professional sports leagues that use

8 our optimization solutions to determine the best

9 schedule for their games. But today we're here.

10 CHAIRMAN MCWILLIAMS: So I just have

11 to interrupt. So you are to blame for all of my

12 gate changes?

13 (Laughter.)

14 MR. NESTEL: No, Chairman McWilliams,

15 we are actually to ensure that your gates are

16 closer to where you have to be. And I hope the

17 airlines that are using us are --

18 (Simultaneous speaking.)

19 CHAIRMAN MCWILLIAMS: Well, I'm not a

20 good runner. So if you can, like, shrink the

21 distance when you move me to another gate, I

22 would greatly appreciate it.

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1 MR. NESTEL: We're here -- I'm here to

2 talk a little bit about what we're doing in the

3 financial inclusion space, but specifically some

4 of the credit scoring innovations that we've been

5 working on to help drive those initiatives.

6 So 2019 is a big year for FICO because

7 we're celebrating an anniversary, 30 years of the

8 FICO score. Back in 1989, we introduced the FICO

9 score that many people refer to as the classic

10 FICO score. This is the score that leverages the

11 credit bureau data from each of the credit

12 bureaus to drive our score.

13 And it's also -- 2019 is also a

14 milestone. We are now working in 30 countries

15 across the globe on credit scoring innovation and

16 financial inclusion, doing some really

17 interesting work which I'll just refer to in a

18 slide or two forward.

19 When we were developing -- during the

20 30 years that we've been working on the FICO

21 score, it's not surprising that we've come out

22 with updated versions. Our latest version is

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1 FICO Score 9. It is our most predictive FICO

2 score to date.

3 When we were developing that score, we

4 did a fair amount of voice of customer. And

5 around that same time, we had some lenders come

6 to us and say, FICO, we understand you score

7 about 200 million consumers today.

8 But we also understand that there are

9 about 53 million -- we've heard 45 million FICO

10 depending on the data set. We arrived at about

11 53 million that the classic FICO score is not

12 scoring today. Half of those, roughly half, 25

13 million are credit invisibles. They have

14 nothing, no files at the credit bureaus. And

15 then the other 28 million or so, we are not

16 scoring them because we have insufficient data to

17 return an analytically sound score.

18 And the lenders really encouraged us

19 to do some additional research. They did not

20 want us to lower our standards and simply just to

21 score more folks. What they did want us to do

22 was to look at alternative data sources to see if

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1 we could bring in new insights on the credit risk

2 profiles of some of these 53 million folks.

3 And this is when we started to really

4 delve deeply into data sets that resided outside

5 of the credit bureau files. That's how FICO has

6 kind of looked at alternative data, data that

7 resides outside of the credit bureau files.

8 So we look at alternative data in

9 categories. And this here shows the kind of

10 hierarchy of data and credit risk predictions

11 that we look at across the globe.

12 Not surprisingly at the top of the

13 pyramid is financial account data. You can see

14 the credit bureau trade lines as well as -- and I

15 want to highlight this because it's been a theme

16 -- demand deposit accounts which we're going to

17 talk about in just a few minutes.

18 In the third box there is

19 non-financial data. In many geographic locations

20 around the world, we are leveraging some of this

21 data. Where credit bureau data just doesn't

22 exist or is very sparse, we can use greater

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1 insights.

2 These insights that we're gaining from

3 this data, we may not be using it here. But it

4 helps inform our work. I want to concentrate,

5 though, on the second box because this is pivotal

6 in the kind of story that I was referencing about

7 our initial research efforts.

8 Telco utility data, there's some

9 confusion around this. FICO scores since 1989

10 have considered telco and utility data when it is

11 available in the credit bureau files. The fact

12 is there's not much available today in the credit

13 bureau files. Anywhere between two and a half

14 and five percent of consumer files actually have

15 telco and utility data.

16 Rent is somewhat of a new phenomenon

17 in the credit bureau files. But only about 1.8

18 million out of 80 million renters actually have

19 any trance of rental data in their files.

20 So when we started to look at data and

21 explore using alternative data to build a new

22 score, we ended up in 2016 introducing a score

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1 that is used in the unsecured lending space. And

2 it's really used as a second chance score, and it

3 works like this.

4 It leverages a telco database that

5 resides outside of the credit bureau files

6 focused on cell phone data, landline data, cable

7 payments, as well as another data set that has

8 some public records and property records that

9 also do not reside in the credit bureau file.

10 When a consumer would apply for a

11 credit card and become a no hit, a FICO score is

12 not returned. The card issuers now have access

13 to a score which can look at these other data

14 sets and hopefully score these individuals and

15 get them on an on-ramp to credit.

16 Because once they get a traditional

17 credit score, a traditional credit tool, they can

18 then as they make on time payments, those

19 payments are reported into the credit bureaus. 

20 And then they get a -- within a few months are

21 eligible to receive a FICO score which can

22 provide them with more access.

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1 But we're only looking at -- in this

2 case, we were just looking at the unscorable

3 population. And in terms of kind of looking

4 where we could expand access to credit and

5 financial inclusion, we wanted to look more

6 expansively.

7 And as you can see here in the two

8 addressable markets, the unscorable and those

9 that are not receiving a FICO score today, we

10 also looked at those that were below 680, a FICO

11 score of 680. It's a larger population and one

12 that we thought had a lot of promise for our next

13 initiative which we announced at the end of 2018.

14 So I must say a fair amount of

15 fanfare. We got a front page Wall Street Journal

16 article, and the phones started ringing from

17 financial institutions who were very interested

18 in participating in what would be a pilot project

19 to start.

20 The score is called UltraFICO. And

21 this leverage is demand deposit account

22 information, think checking, savings, money

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1 market accounts. But what's unique about this,

2 it's consumer contributed data. So this score is

3 going to be driven with the consumer provides

4 permission to access their bank account

5 information.

6 The score looks like a FICO score. It

7 has a score range from 300 to 850, so very easy

8 to understand for lenders. It's a line to the

9 same score -- odds to score relationship. So a

10 700 performs like a 700 in a FICO score.

11 And as you can see, what we're looking

12 at, if you take the checking account information

13 that's provided by a consumer, we're not looking

14 about what they purchase. We're looking at how

15 they maintain that account over time, the recency

16 and frequency of the banking transactions. Are

17 they maintaining positive balances, and are they

18 incurring negative balances?

19 As you can see in the slide, we have

20 two partners. Experian is our credit bureau

21 partner. They're not only providing the credit

22 bureau that drives the FICO score which is kind

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1 of the base here. They're also providing the

2 distribution and the FCRA support.

3 We have a not so young fintech,

4 Finicity. They're been in the data aggregation

5 business for two decades. It's interesting to

6 talk about a fintech that's been doing work for

7 two decades, but they have. And they're the ones

8 that are facilitating the access to the DDA

9 account information.

10 Sometimes it's easy to just look at a

11 graphic on how this works. This is just one use

12 case. The consumer would apply for credit. They

13 would not meet the underwriting criteria. The

14 consumer would be presented with the option of

15 leveraging their DDA account information through

16 UltraFICO.

17 If they gave permission, that

18 information would be obtained through an API that

19 they would credential through a Finicity

20 facilitated portal online. They would get the

21 information, bring it back to Experian who would

22 calculate the UltraFICO score and hopefully the

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1 minimum criteria would be met.

2 Let's talk about just -- let me

3 conclude by talking a little bit about

4 preliminary results and then leaving you with a

5 roadmap as to where we're headed.

6 This graph talks about first the

7 applicant population. So why are we looking at

8 the applicant population? We can produce a score

9 that can find many people who have very strong

10 credit risk profiles.

11 But if they're not likely to be an

12 applicant, for instance, they're 81 years old. 

13 They paid off their house, their car, their car

14 loan, they're not seeking credit. We have

15 insights to be able to kind of focus on those who

16 are likely to be applicants.

17 So we looked at the applicant

18 population. We also know that this score is --

19 because it's consumer contributed, we're going to

20 see some interesting behaviors. Those who may

21 not be managing their DDA accounts like they

22 would hope, we'll likely not give permission. We

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1 know there will be some selection bias here.

2 So what we try to do for both lenders

3 and consumers is hone in on where the real

4 benefits of this score will be seen. So you'll

5 notice that the two criteria we have here is no

6 negative balances within the last three months

7 and a positive balance of at least $400 average

8 over the past three months.

9 So when you look at this pool, the

10 total applicant pool with these two criteria, we

11 saw about 70 percent of this population will

12 experience a score increase.

13 When we look at the thin young credit

14 files is where we really see the sweet spot for

15 the score. Not only do we see about 78 percent

16 of the population experiencing a score increase,

17 but we see about a 40 percent increase of 20

18 points or more for this population. So about 40

19 percent, excuse me, of this population will see

20 an increase of 20 points or more.

21 When we look at those with FICO scores

22 of less than 680, about 77 percent of this

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1 population will experience a score increase. And

2 about one in ten will see an increase of 20

3 points or more.

4 So in conclusion, I'll just leave you

5 with a little idea of where we're at. We

6 announced that we were interesting in having

7 financial institutions engage in a pilot project. 

8 We had many people raise their hand.

9 We had three quick early adopters. 

10 While I can't release their names, I can tell you

11 kind of generally what they look like in terms of

12 the entities. We have a very large credit union. 

13 We have a very large fintech lender, and then we

14 have a credit card issuer that focuses on young

15 buyers.

16 We hope that that those pilot projects

17 will be completed by the end of the year, maybe

18 very early 2020. Around that same time frame, we

19 hope to make the score generally available for

20 financial institutions. We'll have much more to

21 talk about then and really look forward to

22 keeping all of you informed as we kind of do our

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1 work, not only here in the U.S. but globally.

2 Thanks very much.

3 MR. ERNST: Thank you, Daniel. 

4 Lauren?

5 MS. SAUNDERS: Thank you for having

6 me. I'm Lauren Saunders, the Associate Director

7 of the National Consumer Law Center. I don't

8 have a fancy graphic to put up there, but his is

9 our 50th anniversary this year. I hope we'll see

10 some of you at our celebration in November.

11 At the National Consumer Law Center,

12 we focus on low income and vulnerable consumers

13 and working to make sure that they are protected

14 in the financial marketplace, have access to safe

15 and affordable products, and hopefully are

16 protected against ones that are destructive of

17 their financial health.

18 We think that cash flow underwriting

19 is a promising form of underwriting. Some of the

20 key pillars of responsible lending are a fair,

21 affordable rate and making sure that the person

22 can actually afford to repay the loan.

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1 And cash flow underwriting clearly

2 goes to that question. It is basically can be

3 used as a form of residual income underwriting. 

4 The basic question of, what income do you have

5 coming in, what expenses do you have coming out,

6 and is there money left over? Which is, of

7 course, not a question directly addressed by

8 credit scores or credit reports. Certainly,

9 there's going to be overlap.

10 But as you've heard, there are

11 certainly populations for whom the credit scores

12 aren't going to work well. And being able to

13 look at that core issue, can you afford to take

14 on more debt and to repay a loan for populations

15 for which FICO doesn't work we think is

16 potentially promising.

17 In particular for people -- for

18 obviously the thin and no-score people that we've

19 heard about gets at that catch-22, you need

20 credit to get credit. I think the jury is still

21 out on that one. It sounds like the FinRegLab's

22 study didn't get too much at that question,

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1 focused more on those with a little bit of

2 thicker scores.

3 And for those who have scores but

4 maybe had a problem in the past, they have gotten

5 over. Obviously the financial crisis impacted a

6 lot of people. But hopefully some, at least, are

7 recovering.

8 For those who are relatively new to

9 credit and they have high utilization rates on

10 the few credit lines that they have and they end

11 up with a more negative score. Or just have had

12 trouble getting their feet under them as they

13 start to use credit but are good credit risks

14 today.

15 So we think it definitely has a role

16 in a responsible marketplace. And we are very

17 interested in the studies and the work going on

18 about how to use it.

19 I'd also add that I think it is a

20 better way of accessing utility data than

21 mandating that utility companies report their

22 information to the credit reports. Both being

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1 something voluntary and we don't have to get into

2 the problems and risking people's access to

3 government utility programs in the winter or

4 issues of accuracy and whether the utilities are

5 really set up to be furnishers. I think this is

6 a way of accessing that data for those who want

7 to permission it without running to the larger

8 problems of mandatory reporting.

9 That's it. I do have a number of

10 questions about the use of cash flow

11 underwriting. Obviously it is generally accessed

12 through a data aggregator and access to the bank

13 account. And we don't really know how every

14 company is using the data.

15 The FinRegLab's report talks about how

16 they deliberately chose a number of different

17 lenders with different models and who have

18 different proprietary ways that they use the data

19 that you didn't get into and I would, of course,

20 love to know a lot more about.

21 But I think that some of the lenders

22 are very different from Petal. For example,

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1 there was a payday lender, a balloon payment,

2 single payment loans at triple digit rates. I

3 suspect that the data from a bank account is

4 probably used differently for that kind of lender

5 than for a credit card like Petal.

6 I think it's important to note that

7 credit risk which is what the FinRegLab's report

8 was getting at. Credit risk from the lender's

9 perspective is not the same thing as

10 affordability or ability to pay from the

11 borrower's perspective particularly because I

12 suspect that the vast majority of the consumers

13 in your study were using electronic -- automated

14 electronic repayment.

15 And so the fact that a particular line

16 of credit or loan was repaid doesn't necessarily

17 answer the question of whether it jeopardized the

18 person's ability to pay other expenses, led to

19 overdraft fees, NSF fees, late fees on other

20 credit lines that aren't even visible in the bank

21 account.

22 So it's not necessarily the same thing

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1 for all consumers. It's also possible that some

2 lenders might use this data to find that sweet

3 spot of where they can collect but not

4 necessarily help their financial health. It's

5 not all about, is there money left over at the

6 end of the month? It could be how do we time our

7 payments? What does the ebb and flow look like

8 throughout the month? What kinds of expenses is

9 the person spending money on?

10 In fact, we don't even know -- correct

11 me if I'm wrong -- if some of the vendors might

12 look not just at amounts, what comes in, what

13 comes out, what's left over, but where the money

14 is spent. How is that information used?

15 So those are things that give me pause

16 and make me worry. Obviously disparate impacts

17 is a very important question. I really

18 appreciate the effort that FinRegLab went into to

19 dive into that question. And I would be remiss

20 if I didn't point out that we think disparate

21 impact analysis is a critical tool that we hope

22 will be retained in the broader financial

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1 marketplace.

2 I would think it's a fintech friendly

3 tool because it gets at that very question of, is

4 something predictive of credit risk, or is it

5 just a proxy for race or a proxy for

6 disadvantaged populations? And hopefully we all

7 really want to treat people as individuals in

8 their own right. And can they afford to repay

9 the loan and not look at what populations they're

10 a part of?

11 So we support using disparate impact

12 analysis. Obviously, there were limits given

13 what you could do in your analysis that lenders

14 can do more of. But it certainly is a very

15 important question.

16 I'll also note that your study -- I'm

17 sorry to keep pointing to you. But it's the

18 study we have. As I understand it, it didn't

19 look at people who were denied. And we may see

20 very different results there.

21 And that also gets to the question of

22 right now in this early stage, this data is being

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1 used on a voluntary basis with consumer

2 permission. But I can see us rapidly moving to

3 the place where it's not so voluntary.

4 And frankly, even today, I think --

5 correct if I'm wrong -- if you want to apply for

6 a Petal card, you have to get permission. Now

7 there's other products that you can choose, and

8 you can choose whether it's worth it for you or

9 not.

10 But eventually, if this becomes much

11 more widely accepted, agreeing is just clicking

12 that I Agree box that you really don't have that

13 much consent over. And so that gets us into the

14 broader questions of how is it going to be used,

15 how it impacts people. How will it impact people

16 who may be denied credit and not just as a second

17 chance score? And so those are much broader

18 questions.

19 And then, of course, I'm sure we

20 probably don't want to dig in today to the

21 broader issue of data aggregators and the privacy

22 issues and security risks and all of that. But

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1 of course, it's an elephant in the room because

2 you're giving access to a lot of very personal

3 and powerful data.

4 And we don't know who's going to use

5 it, how they're going to use it, who they're

6 going to sell it to, what will be done with it. 

7 I would love to try out all sorts of interesting

8 fintech products, but I just can't bring myself

9 to say yes to those data aggregators, even the

10 ones who I've met with. And I probably know a

11 lot more about them than your average consumer. 

12 But it makes me very nervous.

13 So I'm just happy to be part of this

14 conversation and have thoughtful people looking

15 at a lot of these questions. And thank you for

16 having me.

17 MR. ERNST: Great. Thank you. So I'm

18 going to open the floor here in just a second for

19 general observations and questions. Jason, maybe

20 I'll give you a chance to ask the question. I

21 don't know if you want to address that.

22 But before I give you that chance and

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1 open the floor, I'll just say if I could give an

2 award for explaining a chart at this table,

3 Kelly, I think you'd get it.

4 (Laughter.)

5 MR. ERNST: I don't know that we've

6 ever had to give such a cogent explanation to

7 such a complicated chart. So thank you for doing

8 that. I'm sure we'll have many questions

9 remaining nonetheless. Jason, do you want to

10 speak to that?

11 MR. GROSS: Sure. And I think that

12 Lauren raised a number of really important

13 issues, and I think that it's a balanced

14 assessment of the different considerations in

15 play. Taking a step back very quickly and then

16 I'll kind of respond to some of the specifics

17 around consumer permission and pricing and use of

18 data, et cetera.

19 Part of our sort of vision for where

20 all of this goes over the long term I think is

21 worth mentioning very briefly. Data aggregation,

22 although in its infancy, is allowing for the free

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1 movement, fairly frictionless movement of

2 consumer financial records from provider to

3 provider.

4 More and more, your financial record

5 is the basis for which a provider can serve you

6 will in the financial services marketplace. It

7 is what your bank knows about you. It allows

8 your bank to extend credit to you for the most

9 part, to customize financial products for you or

10 recommendations about how you should manage your

11 financial life. It is similar to your healthcare

12 record, and it is very personal, sensitive

13 information.

14 What we said in Dodd-Frank, Section

15 1033 is that information is the property of the

16 consumer and the consumer should have a

17 reasonable digital access to that information. 

18 And so if that information is essential to you

19 receiving well-priced, well-tailored financial

20 services, then you as a consumer must be able to

21 obtain your property and move it from place to

22 place if you want to so that we can have a

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1 marketplace with competition and options, et

2 cetera.

3 As this information becomes more

4 available, we now have a very different backdrop

5 upon which we can reimagine something like a

6 credit score. The credit score -- the classic

7 FICO score that we're familiar with is based on

8 the information available at the time to come up

9 with our best metric for someone's likelihood to

10 repay a loan obligation.

11 But in a world where the full set of

12 consumer financial statements are available and

13 we kind of think about the credit report like the

14 liability side of the consumer's balance sheet. 

15 But it doesn't have the assets and it doesn't

16 have the cash flows or the income statement.

17 In a world where that full record is

18 available to the consumer to share with the

19 service provider of their choosing, then how can

20 we not create a comprehensive score that looks at

21 the whole thing? And we're talking about some of

22 these very specific, acute examples where

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1 consumers have no liabilities. They have only

2 assets and income and they don't qualify under a

3 mainstream credit system, right?

4 But overall, we can make credit

5 decisions more accurate and more inclusive

6 decisions for more people if we look at the full

7 financial picture instead of just one facet. So

8 that's where we think that credit scoring is

9 moving over the long term.

10 So I thought that was an important

11 kind of framing for how we view the topic. And

12 something that Kelly pointed out as well is that

13 the addition of these other financial statements

14 actually increases the predictive power even when

15 folks have a full traditional credit record. So

16 even if you have lots of information about the

17 liabilities, adding the rest increases your

18 predictiveness.

19 To respond to the specific question

20 about whether linking a bank account is required,

21 we've been trying to find the right way to do

22 that vis-a-vis our customer based when we ask for

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1 cash flow information. Do you use it as a second

2 chance, for instance, like with UltraFICO?

3 For certain consumers that have no

4 traditional information, we have to ask for

5 something or else there's no way to underwrite

6 them at all. Where we're moving as a firm is

7 towards a system that allows consumers to opt in

8 to sharing more information and then receive

9 benefit for doing so.

10 So if you want to share only your

11 credit report, we're building the ability for you

12 to do that. But we can only underwrite based on

13 the information that we have. And if in addition

14 adding the rest of your financial picture would

15 give us a better view, a view as a potential

16 borrower. And it would better represent your

17 ability to repay the loan. We want to give the

18 consumer the ability to share those records with

19 and then potentially improve their access or

20 improve their pricing to the product.

21 MR. ERNST: Great. Thank you. I'm

22 going to come to the committee members now. And

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1 with John in mind, I'm going to start this way

2 and scanning over. Alden and Andrea, I see. Any

3 other hands? I see Raphael and Bob and Jonathan. 

4 Okay.

5 MEMBER MINTZ: Thank you. Thank you,

6 guys. This is super interesting and a very

7 important topic. I just want to make sure that

8 as you're thinking through the cash flow analysis

9 and the underwriting particularly with -- I think

10 with the UltraFICO. What I'm hearing is just a

11 reminder that -- again, this is a little bit

12 parochial.

13 But as Bank On stakeholders are

14 bringing in hundreds of thousands to millions of

15 people into accounts where they can't go negative

16 by mutual design, I want to make sure that your

17 standards are inclusive of those accounts and how

18 they're performing in those accounts as opposed

19 to seeing the inability of a negative to

20 therefore be insufficient information for your

21 scoring.

22 MR. ERNST: That's helpful. Thank

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1 you. Alden?

2 MEMBER MCDONALD: Thank you very much. 

3 This has been very interesting for me because a

4 lot of the techniques that you guys are changing

5 to a digital world we've been using it for the

6 last 30 years to underwrite our customer base. 

7 And as I went through the presentation with you

8 guys, a couple of things jumped up.

9 Jason, you first. What is the cost of

10 the interest rate on the card that you're going

11 to be issuing and how are you going to price that

12 out? And what is the history so far that you've

13 found --

14 MR. GROSS: Yeah.

15 MEMBER MCDONALD: -- from the

16 delinquency point of view.

17 MR. GROSS: Yeah. So we use

18 risk-based pricing for the APR on the card. The

19 APR is a range from about 15 percent to around 26

20 percent. So that's roughly in line with the

21 credit card market overall.

22 This product in particular serves the

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1 new to credit market. So it's compared to

2 introductory credit products which usually carry

3 subprime interest rates. The card has no fees as

4 well, one of the only no-fee cards in the market. 

5 And so when you look at the overall effective

6 cost to the borrower, we think that it's the

7 lowest cost product available to our consumers.

8 And then in terms of performance,

9 while I can't share too much, I can say that

10 we've seen thin file customers underwritten using

11 cash flow data, perform about twice as well as

12 thicker file consumers with the same credit score

13 underwritten using traditional metrics.

14 MEMBER MCDONALD: Going to Kelly, and

15 I wrote down your pass through history for the

16 research. What is it that you're finding for the

17 pass through history and the interest rate that

18 are being charged?

19 MS. COCHRAN: There was a range of

20 price structures on the products that we looked

21 at. Some used a fee-based system. Some use

22 interest rates. So it really varied. And

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1 because we looked at each participant company by

2 itself, within that population, we were able to

3 hold the price and the product structure

4 consistent and look within some unit as to how

5 predictive was it.

6 So to Lauren's point about different

7 pricing product structures, that's exactly right. 

8 There was a variation. What really strikes us is

9 how consistent the results are across all the

10 participants, whatever those details were. So it

11 really varied.

12 Some of the participants used a

13 default metric. Some used delinquency. It

14 really depended on what their product structure

15 was as to how they were measuring it. And we

16 generally use their measures. We confirmed that

17 they were kind of an objective standard but then

18 looked at whatever they were using in their own

19 underwriting model. So it really was all over

20 the place.

21 MEMBER MCDONALD: Daniel, I do have a

22 question for you, so don't feel left out. In

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1 looking at the whole FICO piece, the credit

2 report piece, you're coming up with this new

3 product that is going to give a new credit score. 

4 It was interesting to hear you say that an

5 average balance of $400.

6 That's not going to cover a whole lot

7 of people in the marketplace with negative

8 credit. I can tell you we have 40,000 customers

9 and we have a huge percentage with average

10 balances less than $250. And so the predictor

11 that you're going to use there you might want to

12 look at again.

13 And in the second part of the question

14 would be that how do you rate a credit that

15 perhaps has a regular 30-day delinquency but yet

16 no sixties and no nineties? Because those are in

17 the individuals who are working day to day, hand

18 to mouth, and how they going to pay.

19 And those individuals are really left

20 out of the cycle. So I would like to sort of

21 suggest to all of you that there's a huge amount

22 of that data out there. And we've been doing

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1 cash flow lending utilizing the manual system of

2 which you guys are doing. And I think if you

3 could begin looking at the 30-day chronic

4 delinquencies and have some data on that, that

5 would be very, very helpful.

6 MR. NESTEL: So Alden, both excellent

7 points. To the $400, we -- I asked this specific

8 question of our data scientists as we presented

9 this. As you know, I'm the government relations

10 guy. Unfortunately, we couldn't have our subject

11 matter expert here today.

12 You make a good point. And the point

13 that they made is we use that -- we kind of put a

14 pin in for $400. It could've been 300 or 200. I

15 don't want you to be left with the implication

16 that the $400 is some kind of threshold.

17 We are trying -- what we're hoping to

18 do with consumers is not only is this a slide

19 that shows the results to lenders and encourages

20 them to look deeper in this. But also to help

21 with consumer management issues.

22 We've used the $400 one because there

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1 are significant benefits that we've seen. But to

2 your point, we're acutely aware that not

3 everybody has $400. And this score can be

4 beneficial.

5 I think -- and even to your point

6 about delinquencies. When I talked about the

7 thin young, I think this slide shows thin young,

8 we're not looking at 30 and 60 days. We're

9 looking at 90-day delinquencies and that in terms

10 of benefits.

11 So there can be significant benefits

12 derived by someone who doesn't have a charge off

13 or a serious delinquency. So to your point on

14 the 30 to 60 days, but both very --

15 MEMBER MCDONALD: And I share all the

16 grievances. One additional piece, if I may, for

17 the FDIC. The FDIC policy for examination drills

18 down into a lot of the data points that we talked

19 about here. I remember years ago there was a

20 Rule 660. It was an automatic classification for

21 consumer credit. That has been changed a little,

22 and it uses it as a guide.

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1 In looking at the underwriting based

2 on cash flow, you might want to take a look at

3 what do you have in your examination procedures

4 that will help offset some of the negative

5 guidelines for the financial institutions.

6 Because if you raise those issues, and

7 with this type of information, you may find more

8 community banks that might be willing to lend to

9 the population that we're talking about. Because

10 this has been a deterrent. Because normally

11 those banks are basing their delinquency ratio

12 and they're charge offs and their risk factors. 

13 And I think it's a perfect way of encouraging the

14 larger banks or community banks to do more

15 lending in the consumer space.

16 Just as one note, I'd like to speak to

17 you a little bit more, Jason, because we have a

18 credit card that starts at 12.9, fixed rate, not

19 a teaser rate. And we can't seem to find the

20 consumers to really like it the way we would like

21 to encourage them. So this is something that

22 we've been having for a while and maybe we could

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1 be a part of your study on that.

2 MR. GROSS: Sure.

3 MR. ERNST: Great. Thank you very

4 much. Andrea?

5 MEMBER LEVERE: Could we get the heat

6 map back up? And by the way, Daniel, 400 is the

7 most popular number in the world because of the

8 study. It's also the most misquoted, erroneously

9 quoted data point. That's what all my Fed

10 friends tell me. So it's not that they don't

11 have the 400. It's how they would get it.

12 So what I was struck by, Kelly, here,

13 first of all, what I'm really interested in is 

14 how the research you done is tracking the

15 financial volatility at the household level in a

16 way that deepens our understanding of the

17 implications of that volatility. And also how we

18 think about the volatility now part of labor

19 markets and how that changes how we assess credit

20 and underwriting because it really is

21 transformative in terms of cash flow.

22 So what I want to make the point is

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1 there, I don't know if you're familiar with the

2 data point we created called liquid asset poverty

3 which is the data point that actually measures

4 financial insecurity, not just income poverty. 

5 And basically it measures the ability of a

6 household to have three months' worth of cash so

7 they can exist at the poverty level if their main

8 source of income is disrupted.

9 What's incredible about that data

10 point, 40 percent of American households are in

11 liquid asset poverty, 51 percent of households of

12 color. And what is interesting is it goes all

13 the way up the income level, right to the fifth

14 quintile in terms of high levels.

15 So when I look at your heat map which

16 goes down to those very high credit scores,

17 right? And you see the same point. So I'm just

18 very interested in how you're looking at -- how

19 does the transformation of our labor markets and

20 your ability to look at it with cash flow help

21 you rethink underwriting?

22 MS. COCHRAN: I think that's a really

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1 important question. We didn't have the data to

2 look at that here.

3 MEMBER LEVERE: Yes.

4 MS. COCHRAN: What we had was just the

5 variables that the participants had actually

6 used. We didn't have a picture of their

7 underlying accounts ourselves.

8 MEMBER LEVERE: Yes.

9 MS. COCHRAN: So we couldn't study

10 that kind of question. But I think one of the

11 reasons why this data is so important is it

12 really focuses on income and reserves whereas

13 traditional credit reports, while they are very

14 useful, don't directly --

15 MEMBER LEVERE: Right.

16 MS. COCHRAN: -- reflect that

17 information. And as we see more income

18 volatility, that's usually important. We think

19 that's one of the reasons why it may be spreading

20 more rapidly in small business is because income

21 forecasting on small business is incredibly

22 important. It's incredibly hard to do.

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1 MEMBER LEVERE: Right.

2 MS. COCHRAN: It's never been

3 automated very easily. And so we think that

4 there's a range of lenders in that space who are

5 really interested in working with this data

6 because they see the value of it on the income

7 side. And there's also research. I think some

8 of the research this afternoon in the mortgage

9 market suggesting that reserves can be incredibly

10 important.

11 So we are definitely thinking along

12 those same lines. We weren't able to study that

13 question directly here. But we're really

14 interested in continuing to work on that topic.

15 MEMBER LEVERE: That's great. So,

16 Daniel, here's the most important question for

17 this town. Is the schedule of games for the

18 Nationals the right schedule or not?

19 (Laughter.)

20 MS. COCHRAN: No, because I'm going to

21 be out of town.

22 MR. NESTEL: I'll have to put it into

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1 our optimization so we can figure that one out. 

2 I don't have that one, Andrea.

3 CHAIRMAN MCWILLIAMS: Only after you

4 put the gate.

5 (Laughter.)

6 MEMBER LEVERE: Thank you.

7 MR. ERNST: Let the record show we

8 made it almost to noon before baseball. I've got

9 Rafael, Bob, and Martin.

10 MEMBER BOSTIC: So I had two comments

11 and two questions. The first was to agree with

12 the $400. That jumped out at me as well. And it

13 would be interesting to see how the chart

14 changed, if you moved it to 300, 200, 100, and to

15 zero just to have a sense of how important

16 picking some kind of average balance is for how

17 we think about access to credit. That would be

18 useful information for all of us.

19 I only had one comment, then Andrea

20 spoke and got me thinking. So this issue about

21 really continued labor force engagement is an

22 important one. So what I've seen consistently is

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1 that people in low income neighborhoods and in

2 our neighborhood in particular feel like their

3 attachment to the labor force is more tenuous.

4 And so that puts a constraint. I

5 think you've shown pretty clearly. It puts a

6 constraint on ability to be included in financial

7 markets. And so talking about this is something

8 that is going to be important if we're going to

9 see this expand in a significant way. So trying

10 to make sure that we understand that is

11 important.

12 My questions are really to the whole

13 panel, and they are larger issues about this

14 whole approach. So Jason, you said something

15 very interesting which I had not thought about in

16 terms of Dodd-Frank which is the idea that

17 financial records or consumer financial records

18 are -- I wrote this out because you said it's

19 similar to a healthcare record.

20 And I think about all the protections

21 around healthcare records and the hoops you got

22 to jump through to share that with one doctor let

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1 alone a whole system. How do we compare the

2 regulatory infrastructure for financial data with

3 that of the healthcare system? And if there are

4 differences, does that suggest that we may be

5 exposing people in a financial space to different

6 kind of greater risks? And I hadn't thought

7 about it like that. But that's actually an

8 interesting question.

9 And then my second question is really

10 about the operations of the marketplace. So if

11 we're going to allow a potential applicant to opt

12 in for a lender or for a rent payment or for a

13 utility payment, that, I guess, would mean that

14 all the landlords would have to agree to

15 participate and all the lenders would have to

16 agree to participate to share this information.

17 And that seems like a high bar to me. 

18 And I know rental markets better than others. 

19 But in some markets, what you have are dominated

20 by some national players. So getting a big

21 landlord multifamily company to say, yeah, we'll

22 do it if anyone participates.

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1 But the thing about Atlanta or Los

2 Angeles where ownership of rental property is

3 highly distributed and disaggregated, how do you

4 think about participation in that context? And

5 does that requirement then implicitly mean that

6 we're going to have some sort of geographic

7 selection or market-based selection? And to what

8 extent does that translate into just for impact

9 that we actually care about?

10 And so I don't know the answer to the

11 question. But it seems to me that figuring how

12 that's going to work out is going to be something

13 that's really important.

14 MR. ERNST: I'd invite anybody from

15 the panel to respond.

16 MR. NESTEL: I can start. We were

17 very fortunate on a couple of things on security

18 and privacy. We were very fortunate the -- and

19 access. The partner that we have, Finicity, as I

20 said, has been in this space for 20 years doing

21 data aggregation. They're not only a data

22 aggregate. They're also a consumer reporting

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1 agency.

2 The way our score works is that it is

3 this information is not going in your credit

4 bureau file. Both Experian and Finicity are

5 consumer reporting agencies. They do have to

6 keep the data for regulatory reasons, dispute

7 resolutions, corrections for some time. But they

8 have added responsibilities being regulated and

9 pretty closely regulated as a consumer reporting

10 agency.

11 So the other thing I just wanted to

12 note is that while we're fortunate because our

13 partner is a trusted partner of thousands of

14 banks. And so we aren't having challenges

15 getting access to the DDA account information. 

16 There are efforts being led by not only our

17 partner, a number of banks, consumer advocates to

18 come up with a uniform API standard that would

19 enable -- and perhaps Jason can expound upon this

20 -- but that will enable both banks, consumers,

21 and the lenders themselves one kind of uniform

22 tool that everyone is comfortable with and that

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1 has the adequate security around it.

2 So these efforts are going on. And I

3 think we're getting closer and closer to having

4 that where to reference Jason's point that you'll

5 be able to really have a passport where you can

6 take your information and share it when you give

7 permission to others, not just your own bank

8 having that information and do it in a safe way.

9 MS. SAUNDERS: And if I could address

10 the credit reporting issue. Yes, Finicity

11 accepts being a credit reporting agency and

12 follows the Fair Credit Reporting Act. That is

13 not uniformly true among other data aggregators.

14 And if they are not following the Fair

15 Credit Reporting Act, people don't have the same

16 rights to know what information is being used

17 about them, to know if it results in a higher

18 price or they've been denied to correct errors,

19 to get a copy of their report. So those are

20 definitely issues that hang over the broader

21 issue of data aggregation.

22 MR. GROSS: Lauren, that's not my

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1 experience with the data aggregators in the

2 market, and we don't work with all of them. So

3 there could be others that you have in mind. But

4 the ones that we've worked with, all are either

5 credit reporting agencies or are compliant with

6 the FCRA for the way that they treat any data

7 that's being supplied to a lender.

8 Because arguably under the FCRA, if

9 you are knowingly providing data that will be

10 used in an underwriting decision, you're

11 transformed into FCRA anyways. Or at least you

12 could make the argument that you have those same

13 obligations.

14 And so all of the data aggregators

15 that I'm aware of that provide data for

16 underwriting meet those same obligations with

17 respect to corrections and copies of the report

18 and all those sorts of things.

19 Now if they're providing data for a

20 non-credit use case, if an aggregator is

21 providing data to power mint.com, for instance,

22 then the same obligations do not necessarily

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1 apply.

2 MS. COCHRAN: I might just jump in

3 with a few last thoughts. First of all, on the

4 question about specific expenses like rent or

5 utilities, I think that's one of the reasons why

6 transaction account data is useful because

7 potentially you can pull that information from

8 the transaction account data without having to

9 have every last landlord report.

10 Now transaction account data will tell

11 you what the consumer or small business applicant

12 paid. It doesn't necessarily tell you what they

13 owed. So in some ways, it's not as complete of a

14 source on that expense side as a traditional

15 credit report might be. But it is useful, and

16 that's one of the reasons why I think it's

17 appealing to a lot people in the market right

18 now. So you can get most of that information in

19 one place as opposed to having to have a lot of

20 individual sources feed in.

21 In terms of the aggregation question,

22 a couple of things. This is usually important. 

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1 Aggregation is spreading very widely. At this

2 point, it's estimated to cover about 95 percent

3 of U.S. deposits.

4 The industry is about 20 years old and

5 has been growing. And it is not only essential

6 for this kind of underwriting, but it is also

7 being used in a huge range of personal financial

8 management wealth advisory activities. It's

9 being used in payments and all sorts of kind of

10 account identification and verification of

11 consumer identity and other purposes.

12 So it's becoming really important to

13 the broader financial system. Not just for

14 underwriting but in other ways. And that's

15 certainly going to be something that we're

16 focusing on. In our last report is what are the

17 practical and regulatory issues arising in that

18 space.

19 There are questions in terms of Fair

20 Credit Reporting Act, Gramm-Leach-Bliley Act. 

21 Some of the laws that do apply in the financial

22 services space are a little different than what

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1 applies in health. And I think there are a

2 number of questions about is that the right

3 framework as data use and transfer becomes more

4 and more common in the financial services system

5 overall.

6 So there are a number of questions

7 there. There are some self-regulatory efforts

8 and industry working together to try and solve

9 this, especially on the technology side which is

10 encouraging and I think in everyone's best

11 interest.

12 But there are a number of other

13 questions. I think one of the important things

14 is how authorization works and what the applicant

15 understands when they are authorizing access in

16 terms of what's being pulled and what will it be

17 used for. What are the data management practices

18 of the institutions that are involved in both the

19 ultimate end user like the lender as well as the

20 aggregator?

21 So there's a lot to think about in

22 that space. It's a really important question not

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1 even just for underwriting.

2 MR. ERNST: Thank you. We have just

3 a handful of minutes left. I'm going to try and

4 get both Bob and Martin into the conversation. 

5 Thank you.

6 MEMBER ANNIBALE: Data protection

7 certainly being based in your GDPR, of course, I

8 mean, is incredibly controlling now as to how

9 much you control your own data and privacy. And

10 banks have gone through an enormous amount and

11 all firms and you're trying to protect that.

12 I would just reemphasized the income

13 volatility question. I mean, you can look at

14 things like The Financial Diaries which many of

15 us sponsored Jonathan Morduch and Rachel

16 Schneider who authored that.

17 Young people -- this is not just low

18 income. It's young people. It's many

19 professions, consulting, independent employees,

20 contract workers. It's across that. So we're

21 going to see a lot more volatility, and that's

22 going to show up immediately in the transaction

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1 account.

2 The other is for low hanging fruit if

3 anybody ever thinks of it. For anybody who's an

4 immigrant who happens to come from a country

5 where there was credit bureaus, you come here and

6 you're a thin file. I was a thin file being away

7 for 15 years graduate school. But I had a full

8 credit file in the UK.

9 We tried to use the credit files in

10 Mexico here, but one of the agencies or

11 regulators -- I won't point fingers --- but they

12 were concerned you're only picking Mexico. But

13 it was clearly the most relevant in a place like

14 ours. But people don't -- they have a history

15 but you can't track it. So they lost the history

16 when they moved here. That happens to a McKinsey

17 partner coming from London. That happens to a

18 worker, a middle class worker or anybody else.

19 Last thing was overdrafts versus

20 availment of credit lines. Transaction accounts,

21 I really wonder. When you look at the data,

22 you're going to see people going dipping in and

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1 out sometimes negative. Sometimes that's a

2 prearranged credit line. So just no different

3 than using a credit card. You can choose to do

4 that.

5 The others might be people going into

6 overdrafts and for a different mechanism. I'm

7 just wondering if you -- I don't know if you

8 should. But would you be able to differentiate

9 those behaviors? And is it pejorative or not,

10 having frequent use of an overdraft? Because one

11 is a credit line. The other is an overdraft.

12 MR. ERNST: Anybody want to take a

13 crack at a response?

14 MR. GROSS: Yeah. I would -- I think

15 it's a great point and on the immigrant market as

16 well. I think I mentioned that one of our

17 cofounders was an international student and then

18 was getting his PhD at MIT and he couldn't

19 qualify for a credit card.

20 And so we certainly see that show up

21 in our customer base quite a bit. We are not

22 able to bring in credit bureau information from

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1 other countries, at least not today.

2 But what we can do is generate a score

3 for you after just three months of banking at a

4 U.S.-based institution. So that's a lot faster

5 than the time that it typically takes to ramp up

6 into the traditional credit system. And so we do

7 see a lot of folks with international backgrounds

8 that are a good fit for our product.

9 On the question regarding sort of

10 overdraft versus other forms of credit line. It

11 all depends -- and this kind of goes back to

12 Rafael's point as well. It all depends on what

13 we see, what information is passed through that

14 digital bank statement that we're accessing,

15 right?

16 So what information is in the metadata

17 or sort of the memo line that describes the

18 transaction? And oftentimes with overdraft, you

19 see an overdraft fee clearly labeled. And so we

20 know what that is. For other sorts of products,

21 there could be other labeling.

22 But at the end of the day, these are

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1 not being made in a judgmental fashion. The

2 decisions are all being made in a statistical

3 one. And so we would look at that particular tag

4 and the number of instances overall of the

5 historical data that we're analyzing and

6 determine what relationship it ultimately has to

7 someone's likelihood to repay an obligation.

8 I know that's not the most satisfying

9 answer, but that's the way that we would do it.

10 MR. ERNST: And Martin, I think you

11 may get the last opportunity to put a question on

12 the table.

13 MEMBER EAKES: So a couple points. 

14 One, I want to make sure that the panelist and

15 others -- thank you for the presentation. I have

16 read the book, Shoshana Zuboff's Surveillance

17 Capitalism. You heard of it or read it? If you

18 haven't read it, I think it's the most important

19 single book in the last two or three years.

20 It's a Harvard business school

21 professor, 1,200 pages, 400 pages of footnotes. 

22 And it talks about basically how the financial

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1 sector has ripped off people's data. So using

2 Google and Facebook in particular, taking -- and

3 so it is really terrifying to read it and it's

4 very well documented. I think it's -- if you

5 haven't read it, then you're on the border of

6 malpractice in the jobs that you're in, I would

7 say.

8 Secondly, I want to frame a little bit

9 from this committee. Over the last 12 years,

10 we've talked a lot about serving low income

11 individuals and said that we can't really expect

12 a banking sector or banks to scale a program

13 either of safe accounts for deposit transactions

14 or small-dollar loans unless it can at least

15 break even.

16 So what that meant for us was small

17 account balances that we had to drive cost down,

18 whether it was figuring out how you have branches

19 be more effective, whether you have algorithms. 

20 So this is all a prelude to saying I actually do

21 believe that algorithms are important.

22 But as soon as I say that, I would say

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1 that just listening here that we need a whole

2 hell of a lot more humility than what I heard. 

3 If you've been four years and you got data, you

4 can't tell me what the default data is going to

5 be over a stream of different periods. You just

6 simply -- your data -- you just can't get there.

7 And when I look at the heat map here,

8 what I see is just that the two different grids

9 don't match. It doesn't tell you anything about

10 reliability necessarily. It just says that

11 they're different. Because you can go down the

12 charts and they're just -- they don't go linearly

13 up or down on any single row or any single

14 column.

15 So to me, the question that Lauren

16 raised are really going to be the exact question

17 is, what's going to be the transparency? What

18 commitment are you going to have, not just that

19 you can assert to us, that we have reliability,

20 that you get the same test over and over, that

21 you have predictability, and that you have fair

22 lending?

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1 I don't believe it until I see it. So

2 when I hear a number that 70 percent of the

3 people's scores went up, well, that's great. But

4 I usually care about the bottom 30 percent that

5 went down. And I don't even care if they go down

6 if it's accurately predicting what the actual

7 risk will be.

8 But most of the fintechs and folks

9 that I see who are talking about new scoring do

10 not provide any transparency whatsoever. The

11 amount of difficulty I've had trying to just get

12 what is the median FICO score or the median

13 credit score for African American borrowers. 

14 Nobody wants to report it because they know it's

15 disparate from what it is for white.

16 So transparency of what you would do,

17 I just would love to hear what'd you say. When I

18 hear people talk about opt-in, my whole question

19 having dealt with subprime mortgage loans and

20 single premium credit insurance, opt-in is truly

21 a language without more that is a refuge for

22 scoundrels. And I'm not saying you're the

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1 scoundrel. I'm just saying that language is not

2 sufficient by a long shot.

3 The question is how do you opt out? 

4 How do you get out of -- if I've shared my data

5 from Self-Help Credit Union to you and you have

6 shared it with somebody that I don't even know

7 about, how do I get it back? How does my member

8 or customer -- in European terms, it's, how do

9 you have a right to be forgotten? How do you

10 clear out the crap that's inaccurate and wrong

11 when there's no regulation in this sector of data

12 sharing virtually whatsoever?

13 So opt-out, I just would love to hear

14 you talk just about algorithms and the dangers

15 that come from it. And how do you opt out of

16 getting the data back? Because digital data once

17 shared is really hard to get it back. So what do

18 you have as safeguards? How can you prevent my

19 doomsday scenario which I predict will happen if

20 we don't have regulation to control it?

21 MR. GROSS: I'll jump in with a few

22 things and then we'll let others respond as well

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1 because I think there were a number of different

2 topics that were touched on there.

3 So first of all, just to maybe help to

4 alleviate some of the concern, we're talking

5 about making statistical determinations based on

6 financial data inputs into a model. That's done

7 at every single financial institution that I'm

8 aware of using the exact same methods that we are

9 using and that the other lenders that we've

10 discussed are using.

11 This is a question of using different

12 inputs into the model. And those inputs are --

13 for the most part, that we're discussing DDA

14 information that comes straight from the system

15 of record of a financial institution in most

16 cases, right?

17 We're not talking about black box

18 machine learning techniques which is certainly

19 something that folks need to be aware of and that

20 folks need to understand. These are transparent

21 models. We understand what goes in. We

22 understand how the decision was made, what

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1 factors went into the decision. And then we can

2 provide that transparency to the consumer --

3 MEMBER EAKES: But --

4 MR. GROSS: -- on the other side.

5 MEMBER EAKES: -- it's proprietary,

6 right? You're not publishing it. It's not peer

7 reviewed. It's not --

8 MR. GROSS: Well, every proprietary

9 model inside of a lender is not peer reviewed or

10 --

11 MEMBER BOSTIC: But I don't think

12 that's the issue. So I think about the Great

13 Recession, and we had a bunch of models of how

14 risk was going to be distributed for these new

15 products. And they were wrong. And they were

16 horribly wrong. And I think Martin's point is

17 that risk certainly exists here because we don't

18 have time to go through any kind of business

19 cycle. We don't have time to go through any kind

20 of perturbations, even minor ones in labor

21 market.

22 And so we should be -- I think his

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1 admonition is we should be cautious because given

2 that reality, you heard it going around the

3 table. All the wealth that was lost in African

4 American communities because we were wrong in

5 that estimation.

6 And I think Martin is just saying I

7 don't want to see that happen again. And the way

8 we prevent that from happening is by taking baby

9 steps before we run all the way in.

10 MR. NESTEL: Martin, as you know, FICO

11 can put compelling research forward. But our

12 customers are credit unions, the community banks,

13 medium-sized banks, and the lenders. And they're

14 not going to take our word for it. The whole

15 reason for pilots -- and as you know, banks are

16 very cautious in introducing new products. They

17 are going to kick the tires on every assertion

18 that we make and then double down and scrutinize

19 it further.

20 So we're not designing the model for

21 our personal use. We're doing it because we know

22 that our customers are going to scrutinize it

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1 even further. And hopefully we'll be able to

2 share more insights as we get out of the pilot

3 phase. But we hear you. And certainly I think

4 our customers and the banks are going to really

5 scrutinize how this -- what this impacts are,

6 what the benefits are. And perhaps if they find

7 that there are challenges, we certainly will hear

8 about it.

9 MR. GROSS: Yeah. This is being done

10 very much in baby steps, and it is nascent. 

11 We're one of the few examples up here of anyone

12 experimenting with these approaches. But the

13 results so far have been very interesting. And

14 so we think it's important for folks to be aware

15 of them.

16 There is quite a bit of history

17 associated with using residual income based

18 underwriting models over many, many years and

19 many credit cycles. And so this is not without

20 precedent either.

21 I've heard of a lot of different

22 approaches, some being used in the third world

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1 and other countries where there's less

2 traditional credit infrastructure where

3 completely exotic data sets are being used that

4 have little relation to your financial position,

5 your social media data or the battery on your

6 cell phone.

7 And I think that there's an even

8 degree of skepticism that we should apply when

9 looking at those sorts of data sets. But in this

10 case, it really is more about bringing down cost

11 so that we can serve folks that are otherwise

12 excluded from the system.

13 We are automating underwriting

14 techniques that are being used in a manual

15 fashion every single day today to underwrite

16 mortgages and small business loans, et cetera. I

17 think that's really what's at the core of this,

18 and it's all about how we get the data for that

19 automated process.

20 One more thing that I would add

21 regarding opt-in versus opt-out as well because I

22 know that was another part of your question. For

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1 our model at least, and I can just kind of speak

2 to our experience, the opt-in requires you to

3 authenticate through your bank. So it's not as

4 simple as kind of the click wrap sort of thing. 

5 It actually introduces quite a bit of friction. 

6 And then the opt-out is a simple click, and then

7 you have severed the connection.

8 Now if you submit certain information

9 for use in a credit underwriting decision, of

10 course, there are rules about keeping that

11 information on file for a certain amount of time. 

12 But those are regulatory requirements.

13 MEMBER EAKES: Regulatory by whom? 

14 You're not a bank, right?

15 MR. GROSS: We work with a bank. 

16 That's the issuer of the card. And so we are a

17 critical third party vendor to an FDIC regulated

18 financial institution subject to all the same --

19 MEMBER EAKES: Do you override any --

20 MR. GROSS: -- regulations.

21 MEMBER EAKES: -- state consumer

22 protection laws with that bank's charter?

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1 MR. GROSS: Sorry?

2 MEMBER EAKES: Do you override any

3 state level consumer protections?

4 MR. GROSS: The bank charter is

5 subject to federal preemption, so --

6 MEMBER EAKES: Do you utilize it?

7 MR. GROSS: The bank is the owner of

8 the account.

9 MEMBER EAKES: Would you be out of

10 compliance in any state if you did not have this

11 state preemption by federal charter?

12 MR. GROSS: I don't know the answer to

13 that.

14 MS. COCHRAN: I might have a few

15 thoughts. Is there time, or --

16 MR. ERNST: I think probably we are

17 out of time and well into the lunch period. We

18 won't answer every question fully that's been

19 addressed at this table. But we'll have an

20 opportunity at lunch for members of the committee

21 and panelists who can join us. So I probably

22 need to wrap up and thank everyone for the

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1 participation and the robust conversation we've

2 had. Thank you very much.

3 (Applause.)

4 (Whereupon, the above-entitled matter

5 went off the record at 12:11 p.m. and resumed at

6 1:23 p.m.)

7 MR. MILLER: So to moderate this next

8 panel on mortgage market trends, Mark Pearce, the

9 director of the Division of Deposit and Consumer

10 Protection.

11 MR. PEARCE: You're turning it over to

12 me?

13 MR. MILLER: I am.

14 MR. PEARCE: No long introduction? So

15 thanks, everyone. Good afternoon. I've got a

16 pretty easy task moderating this panel. I think

17 as we were going around this morning, I think

18 there are only four former officials from HUD

19 here with us today to talk about housing markets

20 and mortgage markets.

21 I think in the comments this morning,

22 I noted seven of you talked about mortgage or

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1 affordable housing in your comments. So some of

2 the issues that were brought up earlier around

3 disparities in home ownership rates for whites

4 versus African-American households and Latino

5 households was raised. Talking about having

6 liquidity, post-closing liquidity was a topic

7 that came up and some of the transitions in the

8 mortgage market in general.

9 All three of those issues came up, and

10 we've got three experts here. And they're going

11 to answer all your questions. Well, maybe not

12 all your questions, but they're going to present

13 some of their research.

14 And we're going to take John's advice

15 and go in reverse alphabetical order. So we're

16 going to start -- 

17 (Off mic comments.)

18 MR. PEARCE: It actually just happened

19 that way, but Ryan Goodstein, who is a researcher

20 here at the FDIC, will talk a little bit about

21 insights from the 2018 HMDA data. We have

22 Lariece Brown from Freddie Mac to talk a little

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1 bit about transition rates among rental -- from

2 rental to home ownership for African-American

3 households; and Kanav Bhagat, who's here from

4 JPMorgan Chase Institute to talk a little bit

5 about post-closing liquidity and the importance

6 of that for sustainable home ownership.

7 And with that, I'll turn it over to

8 Ryan.

9 MR. GOODSTEIN: All right. Thank you,

10 Mark.

11 So while I'm waiting for the slides to

12 come up, I'll just say thanks, I'm pleased to be

13 here today to talk to you about what's happening

14 in mortgage markets.

15 That's me who's supposed to be moving

16 the slides. Okay. All right. 

17 So I have about 10 minutes, so rather

18 than covering everything, I'm just going to focus

19 on a few topics that I think will be of

20 particular interest to this group. Specifically,

21 I'm going to discuss the increasing role of

22 non-banks in the mortgage market and how this has

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1 impacted consumers as far as access to mortgage

2 credit.

3 So one of the biggest stories in

4 mortgage markets over the past decade or so is

5 the growth of non-bank lenders. So this -- the

6 chart on the left here, we'll start with that

7 one. This is a look at all single family

8 residential mortgage originations. If you look

9 at the red line, that shows the share of

10 originations made by non-bank lenders.

11 As you can see, non-banks had more

12 than half of originations in 2018. That's about

13 -- you know, about 56 percent. That's more than

14 twice the share they had following the crisis in

15 the 2008, 2009, 2010 period, and it's even higher

16 than the share they had in the pre-crisis period.

17 Another thing this chart shows very

18 clearly that -- is that as non-bank share has

19 increased, the decline in bank share has occurred

20 disproportionately among the largest banks. So

21 that's the blue line, the dark blue line in the

22 chart.

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1 As you can see, the share of the loans

2 by the largest banks has fallen sharply. It's

3 about 37 percent in 2010. That's down to about

4 13 percent in 2018.

5 And in contrast, the share of loans

6 made by other banks has not fallen nearly as far. 

7 That's the light blue line there. And in fact,

8 I'm not showing it here, but if you look just at

9 community banks or very small -- or smaller

10 banks, the share is essentially flat over this

11 period.

12 There's a lot of explanations for why

13 these trends have occurred, and, you know, I'm

14 happy to talk more about that in the discussion

15 period. But for now I'm just going to highlight

16 a second important point, which is that the

17 non-bank share growth has differed substantially

18 across markets.

19 So on the right, the chart shows

20 shares of government lending. Non-banks are the

21 dominant player in this market now. So three out

22 of every four government loans is made by a

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1 non-bank. Large banks, on the other hand, have

2 really pulled back from making government loans. 

3 Less than 10 percent of government loans were

4 made by the largest banks in 2018.

5 And of course, you recall that

6 government loans include, you know, things like

7 FHA loans and VA loans. These are often taken up

8 by first-time home buyers or other segments of

9 the population that have lower levels of wealth,

10 or, you know, because they offer -- they allow a

11 lower down payment, right?

12 So the fact that non-banks differ from

13 banks in the markets that they serve means that

14 the characteristics of their borrowers also

15 differ. So this -- these -- this basically shows

16 the distribution of loans across different

17 borrower characteristics. So just focusing on

18 the top three bars, the red bar says that

19 basically 31 percent of loans by non-banks went

20 to low and moderate-income, or LMI, borrowers.

21 In comparison, only about 22 percent

22 of loans by the biggest banks went to LMI

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1 borrowers. Similarly, you can see loans to

2 properties in LMI tracts or to black and Hispanic

3 borrowers are disproportionately made by

4 non-banks.

5 And so overall, the data suggests that

6 non-bank lenders are filling a gap in access to

7 mortgage credit by making loans to borrowers that

8 some banks, particularly some large banks, seem

9 less willing to serve, so for example, borrowers

10 with less than pristine credit scores. So that's

11 what these charts will show you.

12 Many of you or some of you may not

13 have seen charts like these. Let me just sort of

14 walk through how to understand them.

15 So if you just focus on the top line,

16 this is a look at the credit score distribution

17 for on all single family residential

18 originations. And the white line in the middle

19 of that red bar there, that's the median credit

20 score among non-bank loans. That's 717. That's

21 what the median credit score on non-bank loan was

22 in 2018.

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1 The box shows the range from the 25th

2 to 75th percentile, so from about 670 to 765, and

3 then the whiskers, those are the little lines at

4 the end, they show the values at the 10th and

5 90th percentile. So for non-banks, this ranges

6 from 636 to 792.

7 And so if you compare non-banks to

8 banks, you know, especially large banks, you can

9 see that the credit score distributions are

10 substantially lower, right? So for example, the

11 largest banks, the median credit score is 755, so

12 about 40 points higher than for non-banks.

13 And so of course much of the

14 difference in these credit score distributions

15 ties back to the differences in the markets that

16 these lenders are serving, right? So the bottom

17 chart shows -- it's essentially the same chart,

18 but now it's just limited to a more homogenous

19 market. This is the market for single family

20 residential conforming purchase. These are loans

21 that are eligible for sale to the GSEs. And you

22 can see the differences are much less

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1 substantial, right?

2 I'm just showing one aspect of credit

3 risk here, you know, the actual credit score. 

4 There are of course other elements of credit

5 risk. So for example, DTI, LTV, and you see

6 similar patterns, you know, for those types of

7 measures.

8 Okay. So let me just shift now away

9 from the focus on non-banks versus banks and

10 discuss a broader trend in the overall mortgage

11 market, which is that we've seen an expansion of

12 credit availability in recent years. So these

13 are charts all based on HMDA, all on single

14 family residential purchase originations.

15 The chart in the upper left, this

16 shows basically the share of loans to LMI

17 borrowers and LMI tracts over time. You can see

18 it's been increasing. On the upper right, you

19 see the share of loans to black and Hispanic

20 borrowers has also been increasing. Denial rates

21 have been falling steadily. That's what you can

22 see in the bottom left there.

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1 And I should pause here. I'm an

2 economist, so I sort of -- I have to have at

3 least one caveat per, you know, few slides. 

4 That's part of my nature.

5 So I'll just note that, you know,

6 these trends, these reflect not only changes in

7 supply of mortgage credit but also demand, right? 

8 So these are just raw trends. I'm not

9 controlling for differences in borrower

10 characteristics. That -- you know, that of

11 course could be driving some of what we're seeing

12 here. But in general, I would say that these are

13 suggestive that credit availability has been

14 expanding.

15 The chart on the lower right, this

16 shows the share of single family residential

17 purchase with rate spread above either 1.5

18 percent or 2.5 percent. So you can think of rate

19 spread as an indicator of risk on the loan. So

20 under Reg. -- the Truth in Lending Act, Reg. Z,

21 higher priced loans are defined as those with

22 rate spread of 1.5 percent of higher.

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1 You can see that's been increasing. 

2 That's the solid blue line. And I'll note that

3 these are really -- it's really most of the

4 action here is in the government loan market. 

5 That's where we're seeing these increases.

6 CHAIRMAN MCWILLIAMS: Are these HOPA

7 loans? You're talking about HOPA loans?

8 MR. GOODSTEIN: These are not HOPA

9 loans. These are something different, just --

10 they're called higher priced loans under -- 

11 CHAIRMAN MCWILLIAMS: Just higher

12 priced on -- 

13 MR. GOODSTEIN: Yeah.

14 CHAIRMAN MCWILLIAMS: -- Reg. Z.

15 MR. GOODSTEIN: Right. So I wanted to

16 put not only the share that are higher priced but

17 also another measure of risk which is, you know,

18 I could have picked any number, but here I'm just

19 showing 2.5 percent rate spread. That's the

20 dotted blue line.

21 So it's worth noting that, you know,

22 so although the share of higher priced loans has

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1 gone up, a lot of the action is really close to

2 this threshold. More deeply higher priced loans,

3 if you think of these sort of larger rate spreads

4 the incidents of those loans have not been

5 increasing. You know, less than 1 percent of

6 loans in 2018 had a rate spread of 2.5 percent or

7 more.

8 Thinking back to the pre-crisis

9 period, it's hard to compare this directly

10 because of differences in measurement, but just

11 for reference, you know, about 25 percent of

12 loans in 2005 and '06 were kind of classified as

13 higher priced, and about half of those were

14 deeply higher priced. So this does not look like

15 what things looked like in the crisis.

16 And continuing on the same theme -- so

17 it's important to keep in mind that while it

18 looks like credit availability has been

19 expanding, we're talking about an expansion from

20 historically tight levels of underwriting.

21 And it's worth emphasizing that the

22 level of risk in the market now doesn't look like

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1 -- you know, doesn't -- things don't look now

2 like what they looked like in the crisis, right? 

3 So this is just one example to support that.

4 This is a chart from the -- using data

5 from the New York Fed Consumer Credit panel. It

6 shows the distribution of credit score at

7 mortgage origination, you know, on originations

8 back to 2003. And so if you look -- let's just

9 focus on the median. That's the green line.

10 You can see that credit scores have

11 declined in the last few years compared to, you

12 know, 2010, '11, '12. But the median credit

13 score is still substantially higher now than it

14 was, you know, in the run up to the crisis, about

15 50 points higher.

16 And at the lower end of at the 10th

17 percentile, the credit score is about 70 points

18 higher in 2018 than it was in the 2005, 2006

19 period.

20 Another item worth pointing out is

21 that the actual loan products themselves look

22 different now compared to what they looked like

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1 during the crisis. So again, I'm borrowing

2 another chart here. This one is from the CFPB. 

3 It shows the incidents of loans with risky

4 features. 

5 So it shows that, for example, about

6 20 percent of loans originated in 2005 and '06

7 had less than full documentation. That's the

8 dotted yellow line. About 15 percent of loans

9 allowed interest only or negative amortization

10 payments. These loan features, as I'm sure you

11 know, were associated with elevated risk of

12 default during the crisis. Almost all loans made

13 now, you know, don't -- you know, these features

14 are absent from loans being made now, right?

15 So the takeaway I want to leave you

16 with is that, you know, while credit availability

17 has been expanding recently, the pool of

18 borrowers getting loans is still relatively of

19 low risk by historical standards.

20 Underwriting practices generally

21 appear to be sound, and lenders -- you know,

22 basically the loan products don't have these

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1 risky features baked into them as they did during

2 the -- in the run up to the crisis.

3 So let me just wrap up. Again,

4 non-bank lender share has been increasing

5 dramatically in part because they're filling a

6 gap in access to mortgage credit. And

7 underwriting standards are loosening, but you

8 know, this is not a return to pre-crisis, at

9 least not yet.

10 To state an obvious point, you know,

11 continued monitoring of the mortgage market is

12 critical, right? It's important to really keep

13 an eye on things to see how much mortgage credit

14 will continue to expand, how much will loan

15 performance deteriorate.

16 I mean, right now, performance looks

17 good, particularly on conventional loans by

18 historical standards. We're in a good economic

19 period, so how will things evolve, you know, when

20 we hit the next recession.

21 So I will stop there.

22 MR. PEARCE: Hand the baton to

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1 Lariece.

2 MS. BROWN: Thank you. I want to

3 spend just a few moments talking about what the

4 minority home ownership gap has been looking like

5 since the crisis.

6 So we've spent -- we've seen a lot of

7 papers that have talked about during the crisis,

8 the loss in wealth due to foreclosures and some

9 of the lending practices that have taken place

10 during that time. Now we're looking at are we

11 seeing recovery. And when we look at the

12 recovery, who do we -- who are we able to observe

13 a recovery for and who might be lagging in that

14 recovery.

15 So this is some of the research that

16 we're doing now, but I'll start off with some --

17 a little background information for us. The --

18 first off, the home ownership rate chart here,

19 this is probably one that you've seen before. 

20 It's public data.

21 When you take a look at the home

22 ownership rate, in 2019, overall the home

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1 ownership rate was -- is at 64 percent. What I'm

2 showing here in the top time series is the

3 non-Hispanic white home ownership rate and then

4 for blacks in the lighter blue down below and for

5 Hispanics in the orange-ish red color.

6 And one of the disturbing things we're

7 noticing -- I have circled there -- is a

8 divergence in home ownership rates post crisis. 

9 That's one of the things we want to take a closer

10 look at with the data that we have available to

11 us. In particular, the black/white home

12 ownership rate gap is now at 30 percentage

13 points, and Hispanics are 26 percentage points

14 less likely than whites to actually own a home in

15 2019.

16 And what -- the other thing I have

17 circled there is that the story for blacks is

18 even more dire in that the home ownership rate

19 for blacks is actually declining while the home

20 ownership rate for Hispanics is actually

21 increasing in the most recent years.

22 Okay. A few -- a couple of other

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1 research studies that have come out looking at

2 this post-crisis period include Acolin, Lin, and

3 Wachter. This particular paper looks at what are

4 the main factors that seem to be able to explain

5 the home ownership gap, the home ownership rate

6 gap kind of pre-crisis leading right up to the

7 recession and then post recession.

8 And one of the main things that we see

9 in this work, they talk about endowment, that

10 endowment measure that Wachter, Lin, and Acolin

11 come up with is this idea of permanent income,

12 where the permanent income that they measure

13 includes all of the factors that you might want

14 to consider such as education, age, gender,

15 marital status, and other family attributes and

16 as well as kind of access to funds based on their

17 location and parental wealth.

18 When they look at all of those

19 potential explanatory variables, they're finding

20 that when they decompose how much they're able to

21 explain versus what's left in the residual, the

22 residual portion is actually increasing in the

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1 post-crisis period.

2 The residual in their situation is

3 maybe dominated by credit factors. They're not

4 able to observe the actual credit profile of the

5 consumers that they're looking at. But it's

6 still a pretty interesting tell there.

7 Another paper put out by the Urban

8 Institute this year, which Choi is the author,

9 does a similar thing where they actually are now

10 able to bring in credit attributes and look at

11 the MSA level credit attributes as well as what

12 may be driving the home ownership rate gap across

13 different MSAs.

14 And again, they're finding that they

15 have a residual; it's very similar to what

16 Wachter, Lin, and Acolin find, and they say, you

17 know, basically 17 percent explained in the 2017

18 data. But, you know, they do find that things

19 like family -- things that may drive demand such

20 as marital status, age, and so forth are very

21 major determinants of whether or not someone will

22 -- is a homeowner when we observe them in the

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1 2017 data.

2 Let me turn to our work a little bit

3 here now. What we've decided to do is with all

4 of the information about home ownership rates and

5 kind of the static that -- that we see, we wanted

6 to understand could we -- we wanted to see if we

7 could start to disentangle what is going on post

8 crisis.

9 So one measure that we could -- that

10 we decided to come up with is, well, who's

11 actually transitioning into home ownership in the

12 post-crisis period. This is one piece of our

13 research. You can certainly ask other questions

14 about what the -- what other aspects of recovery

15 you may want to measure.

16 In this particular measure, what we do

17 is we have a random sample of 5.8 credit visible

18 consumers and an additional 8.9 million consumers

19 who are in the household with those consumers in

20 2016. For all of them, we have information about

21 their detailed credit profile, including

22 foreclosures, the amount of debt held,

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1 delinquencies, charge-offs, bankruptcies,

2 inquiries, DTI, and of course credit scores as

3 well as whether or not they're thin file which

4 becomes pretty interesting as well.

5 We use that, and we are able to look

6 at transition from 2012 through 2018 into a

7 mortgage situation which we proxy for one aspect

8 of potential recovery during this time period.

9 Okay. And the top chart, what we are

10 looking at here are the people -- what percent of

11 the people who did not have a mortgage when we

12 look at the data in 2012, which -- what percent

13 of them actually observe -- are observed with a

14 mortgage by 2018. So that's the measure there.

15 And you can see the differences in transition

16 rates as we look at those percentages.

17 New mortgage holders in 2018 as a

18 share of non-mortgage holders in 2018, we look at

19 the gap in the transition rate for

20 African-Americans, for instance, is the 5.8

21 percent. While delinquencies and foreclosures

22 are down, we believe the new transition -- this

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1 new mortgage transition is an important metric

2 for this time period.

3 We analyze the extent to which racial

4 difference and individual characteristics

5 contribute to the white/black and the

6 white/Hispanic gap in the transition rate to new

7 mortgage ownership.

8 We find that -- so to talk about the

9 second chart here, we find that age, income,

10 unemployment, and housing demand are big drivers

11 for determining who acquires a new mortgage.

12 Consumers with no credit score or

13 insufficient credit histories are less likely to

14 transition, of course. Student loan debt,

15 gender, and education level matter, but they

16 actually play a lesser role in explaining the

17 transition. And in this situation, blacks and

18 Hispanics are one-half and two-thirds as likely

19 to transition into mortgage ownership compared to

20 non-Hispanic whites, respectively.

21 Okay. In this chart, again, we're

22 decomposing the factors that explain now the

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1 transition to mortgage ownership, which we used a

2 proxy for a new homeowner. We do some test by

3 age and so forth to make that more robust going

4 forward.

5 But in this situation, you see that

6 for the black/white gap, we're able to explain 66

7 percent with the various credit attributes as

8 well as macroeconomic factors in the geographies

9 that they live in. And a very -- you can't even

10 see the tiny sliver of socioeconomic demographic

11 information that's included there, which matters.

12 For the Hispanic to white gap, we do

13 explain a little bit more, substantially more, 79

14 percent with the factors that we're able to

15 control for in our analysis. I'm going to break

16 that down just a little bit to talk about what

17 specific factors matter and how they matter.

18 Okay. It turns out that individual

19 demographics, when we look at the distribution of

20 individual demographics, which includes things

21 like age, female headed households, and so forth,

22 that is actually decreasing.

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1 If we look at the differences in the

2 distribution between blacks and whites and

3 Hispanics and whites, those factors are actually

4 decreasing the gap in transitioning. In our time

5 -- in the data that we observed, we're actually

6 seeing that the older millennials are indeed

7 transitioning into home ownership in this -- at

8 the highest rate for this particular time period.

9 So again, this is different from just looking at

10 the home ownership rate and looking at who our

11 new homeowners are. 

12 Okay. Of course, credit worthiness or

13 credit factors or attributes are lumped together,

14 and that explains, again, the more than 60

15 percent for the white/Hispanic gap. Some people

16 were surprised to see it being that large for the

17 white/Hispanic gap, and then 49 percent for the

18 white/black gap. Okay. And the chart that I

19 showed you before is basically combining all of

20 this information into one picture.

21 Geography is different. So geography

22 in this situation includes two primary macro

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1 variables: house prices and unemployment. And

2 what we're finding is for Hispanics, they -- the

3 mortgage-ready Hispanics in our data tend to be

4 concentrated in higher-cost markets, and that's

5 driving a lot of the difference in the

6 Hispanic/white gap that we're observing in

7 transition rates.

8 Okay. Since we -- after taking a look

9 at the role of geography, we decided to do a

10 little bit of additional disaggregation in

11 geography and look within these metropolitan

12 areas. I -- we have a lot more in the actual

13 paper, and we can tell where -- everyone where

14 the paper is located.

15 But for today, just a few key markets

16 that we wanted to show you to demonstrate that

17 the impact of these different factors does vary

18 by geography. Okay?

19 So for instance, when we look at this

20 -- all this picture here is for the white/black

21 transition gap. And you can see in each of these

22 situations, the lighter blue bar is what we saw

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1 when we just did that national. That's the same

2 result that was in the prior slide when we just

3 look at the national result.

4 And now the darker blue bar is when we

5 look within -- when we do it by -- for just that

6 geography and look at what are the main

7 determinants of the transition rate gap for just

8 that particular geography.

9 Okay. And unequal -- and you can see

10 kind of a few different cases here. Florida down

11 in the far right looks very much, very similar to

12 the national picture while California and New

13 York -- we're surprised by California.

14 In California, you do get geography

15 mattering more for African-Americans for the

16 black/white gap there. But in New York, that was

17 one of our starkest differences there compared to

18 the national picture. House prices -- so there -

19 - it could be a factor of segregation within the

20 state or blacks living in different urban pockets

21 or cities that are more expensive when they're

22 pursuing home ownership potentially.

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1 Mississippi, completely different

2 story. Mississippi is a scenario where we

3 actually have unequal unemployment. I have the

4 numbers for that somewhere. For Mississippi, the

5 mean unemployment rate for blacks is 9.8 percent. 

6 And this is just 2018, as a matter of fact, and

7 8.6 percent for whites. High across the state

8 compared to the national average, but even higher

9 for African-Americans in that state. And so

10 that's the -- kind of the driver for that -- for

11 the Mississippi picture.

12 For Hispanics, again, we see variation

13 across markets in what is explaining the gap. So

14 this -- if you have interest in some local policy

15 makers or people thinking about those local

16 effects. In Illinois, I provide that example

17 because there, again, the lighter bar is the

18 national picture. The darker bar is for that

19 particular state.

20 When we look at Illinois, for

21 instance, the geography piece looks very similar

22 to their national story. But the impact of the

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1 credit factors is very different in Illinois for

2 explaining the Hispanic/white gap when we look at

3 that picture.

4 In New York, we do still see that the

5 credit factor portion is a little lower while the

6 geography piece is now higher, again, likely due

7 to house price differentials. New Jersey is

8 provided there. We have -- I think we were

9 looking at some of the differences in the

10 demographics, wanted to have a contrast, similar

11 to the national picture for New Jersey.

12 Texas was one that was another stark

13 difference that we observed where geography,

14 maybe house prices aren't as dramatically variant

15 for where Hispanics are living within the State

16 of Texas. So that's what we observed there.

17 To summarize, the racial differences

18 in the distribution of credit attributes does

19 explain a large part of the racial gap. In

20 aggregate, while racial differences and household

21 composition contribute significantly and explain

22 the white/black gap, geography matters more in

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1 explaining the white/Hispanic gap. However,

2 there is a lot of heterogeneity in the

3 contribution of the factors across different

4 states.

5 You know, we're doing this research,

6 so it would be very natural to ask what is

7 Freddie Mac doing about all of this and how are

8 we thinking about some of these issues. That

9 would be a fair question, so I'll go ahead and

10 start it off now.

11 First, want to point out that we are

12 partnering with Borrower Help Centers. Borrower

13 Help Centers are non-profit -- it's a network of

14 non-profit counseling agencies and other

15 non-profits, CDFIs for instance, with HUD

16 certified counselors providing home ownership

17 preparation and foreclosure prevention services

18 to their clients.

19 Of course, Freddie Mac has for a while

20 now had flexible products for lenders that we

21 want to encourage more lenders to use for -- with

22 their client including our Home Possible and Home

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1 One products where the Home Possible product is

2 to meet the home financing needs of the low to

3 moderate income borrower.

4 Home One is relatively new. That's

5 for first-time home buyers to also offer a low

6 down payment option, but it doesn't have the

7 income restrictions or the geography restrictions

8 that Home Possible has. Home Possible has income

9 restrictions or geography restrictions and also

10 offers the down payment, the lower down payment

11 option, the higher LTV option.

12 We're also working with Down Payment

13 Resource and other non-profits to assemble

14 assistance options for consumers to help bridge

15 the gap for the down payment that they need to

16 link up with the actual mortgage products that

17 they're interested in.

18 Okay. As a matter of fact, on our

19 website, we have a link for our Home Possible

20 eligibility tool, and when you go into the tool

21 to try to see if the home and the consumer would

22 be eligible for the Home Possible product, you

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1 actually receive a link to Down Payment Resources

2 that may be available in your community.

3 Okay. Freddie Mac has partnered with

4 FHFA and the other GSE to offer Spanish

5 translations of the Fannie Mae and Freddie Mac

6 uniform documents. And the translated documents

7 include Freddie Mac's Your Step-by-Step Mortgage

8 Guide. These documents are available as

9 resources for lenders, servicers, housing

10 counselors that are working directly with their

11 communities.

12 Okay. And of course, we're working

13 with MBA, their members, other research partners

14 to come up with more targeted strategies to

15 expand access to mortgage credit.

16 Thank you.

17 MR. BHAGAT: Okay. Well, first let me

18 just start by saying thank you very much for

19 inviting me and for the interest in our research.

20 Just to spend 30 seconds, the JPMorgan

21 Chase Institute, just in the event people don't

22 know, is an internal research organization. You

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1 can think of it as a think tank with a public

2 good mandate.

3 So our goal is to use the

4 administrative data sitting within JPMorgan Chase

5 to do economic research, and our hope is that

6 business leaders, non-profit leaders, and policy

7 makers will find that research useful as an input

8 into their decision-making processes.

9 All of our research is free. It's

10 available on our website. The report that I'm

11 going to talk about today was published back in

12 June. The URL is at the bottom. It's -- there's

13 quite a bit more detail there in case folks are

14 interested.

15 And before I start, I'll just offer

16 this disclaimer. This -- what I'm going to say

17 reflects my views and not necessarily the views

18 of JPMorgan Chase.

19 Okay. So the research question that

20 we wanted to answer or attempt to answer in this

21 piece is what evidence do we have in our data --

22 and I'll talk a bit about what that data are --

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1 about the role of liquidity, equity, income

2 level, and payment burden as determinants of

3 mortgage default.

4 And within that research question, we

5 wanted to ask something very specific, which is

6 if we took a new homeowner and at origination

7 apply the treatment of reducing their down

8 payment by a small amount and taking that

9 residual cash and setting it aside in a liquidity

10 account, and we've named it an emergency mortgage

11 reserve account, and keeping that liquidity

12 available in the event of financial distress, how

13 might that impact default rates going forward.

14 And so just to put it -- provide a

15 numerical example, imagine a homeowner that might

16 put 5 percent down. Well, instead of putting

17 that 5 percent down and being left with very

18 little in post-closing liquidity, put down 3

19 percent, take that 2 percent of house price

20 residual, put it aside in this emergency mortgage

21 reserve account.

22 And what we'd like to know is how

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1 would that impact default rates relative to a

2 similar cohort of borrowers, so folks with

3 similar credit attributes that might have bought

4 a similar home. What would be that default rate

5 differential?

6 To answer this question, we use a

7 deidentified sample of Chase customers that had

8 both a Chase mortgage and Chase deposit accounts. 

9 And I'm going to highlight two findings, and I'll

10 talk about them in quite a bit more detail.

11 First, we found that borrowers with

12 little post-closing liquidity defaulted at

13 considerably higher rates than borrowers with at

14 least three mortgage payment equivalents of

15 post-closing liquidity.

16 Secondly, and this gets at that

17 trade-off between home equity and liquidity, we

18 found that borrowers with little liquidity but

19 more equity defaulted at considerably higher

20 rates than borrowers with more liquidity but less

21 equity. And so from this analysis, we conclude

22 that liquidity may, in fact, be a more important

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1 determinant of mortgage default than equity,

2 income level, and payment burden.

3 And so what I'm going to present to

4 you in the pages that follow is really

5 correlation-based evidence. And because it's

6 correlation-based evidence, we come to the first

7 implication, which is a pilot program that

8 essentially took a set of borrowers, again, with

9 similar credit characteristics interested in

10 buying similar homes and applied the treatment

11 that I described before, so a slightly smaller

12 down payment with a liquidity account setup,

13 comparing that to a control group that made the

14 normally sized down payment and didn't have the

15 liquidity account.

16 That type of pilot program might be

17 very instructive in terms of the effectiveness of

18 this type of program. And then secondly, there

19 are some implications for how we think about the

20 impact of total DTI as a default prevention

21 mechanism. And it could be the case that

22 liquidity might be a better measure of the

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1 ability to withstand financial volatility.

2 Okay. So that's an overview. If we

3 dive in, as I said, the first finding, borrowers

4 with little post-closing liquidity defaulted at

5 considerably higher rates than borrowers with at

6 least three mortgage payment equivalents of

7 post-closing liquidity.

8 So let me define some terms and talk

9 a bit about the data. So here we used a sample

10 of about 300,000 Chase customers, again that had

11 both a mortgage and deposit accounts. The

12 mortgages were originated between 2012 and 2016.

13 We observed default rates for three

14 years after origination, and we defined default

15 as missing three or more mortgage payments. We

16 also observed the balance in these new

17 homeowners' checking and saving accounts in the

18 month after they closed on their home. So this

19 is our concept of post-closing liquidity.

20 And what we plot here in the line is

21 essentially the default rates for this sample

22 according to how much post-closing liquidity they

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1 had. And importantly, we take that balance in

2 their checking and savings account and we

3 normalize it by dividing by their scheduled

4 mortgage payment. So we take the balance in the

5 checking and savings account, and we divide it by

6 principal, plus interest, plus taxes, plus

7 insurance, plus association dues, and so that

8 gives us this concept of liquidity normalized,

9 and we're going to -- I'm going to refer to that

10 as the number of mortgage payment equivalents, or

11 MPEs, of reserves.

12 So on the Y axis of this chart, we're

13 plotting default rates for this group. And on

14 the X axis, we're plotting the number of MPEs

15 that they had in their checking and savings

16 account in the month after closing on their home.

17 And what you notice right away is for the group

18 on the left-hand side, folks that had less than

19 one MPE, the default rates are about five times

20 higher than the folks that had, let's say,

21 between three and four.

22 In addition, you notice as you move to

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1 the right in the chart, the marginal benefit of

2 additional liquidity seems fairly small, so

3 default rates don't continue to drop once you get

4 beyond three and four. The blue and green bars

5 tell another interesting story. The blue bars

6 show the share of the sample in that liquidity

7 bin after closing. And the green bars show the

8 share of defaults in that liquidity bin.

9 And so you see right away that while

10 20 percent of our sample at origination was in

11 that first bin, that first bin accounts for over

12 half of our defaults. And so this tells us that

13 liquidity in the month after origination is an

14 important factor in terms of the impact on

15 default.

16 Flip to the next page, we'll see that

17 this relationship persists over the life of the

18 mortgage. So now we've done almost exactly the

19 same analysis, but rather than measuring default

20 rates in the first three years after origination,

21 we take a slice or a snapshot of our servicing

22 data in 2014. So in this case, it's about

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1 500,000 Chase customers with both a mortgage and

2 deposit accounts.

3 These mortgages were originated in

4 2009 or later. And now we measure liquidity in

5 January of 2013. So again, it's the same

6 liquidity measure that I mentioned before, and we

7 plot exactly the same thing, default rates in

8 2014.

9 And the pattern persists here. Once

10 again, for the borrowers who, again, over the

11 life of their mortgage are in that left-most

12 bucket with less than one MPE of liquidity, we

13 see default rates are about six times higher than

14 borrowers with between three and four MPEs of

15 liquidity.

16 And again, the marginal impact of more

17 liquidity on default rates seems to flatten out

18 over time. And the disproportionate nature of

19 the first bin is again evident. It makes up

20 about a third of our sample but three quarters of

21 our defaults.

22 Okay. So that's our first finding. 

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1 Our second finding on Page 5, borrowers with

2 little liquidity but more equity defaulted at

3 considerably higher rates than borrowers with

4 more liquidity but less equity. And so now what

5 I'm doing here is I'm taking that same sample

6 that we just looked at, so the same roughly

7 500,000 borrowers, and we're looking again at

8 2014 default rates.

9 But now we're looking at it in a cross

10 section, and so I'm adding in this concept of

11 equity, and I'm adding it in by observing their

12 LTV on their mortgage, not at origination but in

13 January of 2013. So our observation of their

14 home equity or their LTV is consistent with our

15 observation of their liquidity which is also

16 January 2013.

17 The Y axis is still default rates. 

18 And in this instance, we're showing that LTV on

19 the X axis. And in order to see the cross

20 section, we're showing a different line based on

21 liquidity. So the dark blue line at the top is

22 the borrowers with less than on MPE, and then we

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1 also show one to two, two to three in the light

2 blue line, which is folks with three to four MPEs

3 of liquidity.

4 And immediately you see right away

5 that there's a significant gap between that dark

6 blue line and all of the other lines, and that's

7 roughly about 3.5 percentage points.

8 Now, I mentioned at the outset this

9 concept of trading equity for liquidity. How are

10 we going to accomplish that? Well, if you think

11 about at origination -- we're going to use the

12 following translation mechanism, which is 1

13 percentage point of house price at current

14 interest rates and at a 95 LTV is about equal to

15 one and a half fully loaded monthly mortgage

16 payments. So again, PITIA.

17 So that means that I could create

18 liquidity by taking a 1 percent of house price

19 smaller down payment, I could create liquidity of

20 about one and a half monthly mortgage payments if

21 I did that exchange at origination.

22 Now, if you remember from the previous

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1 chart, we saw that the steep part of that slope

2 was between less than one and three to four. So

3 I'm going to anchor on that, and I'm going to

4 say, well, let's think about exchanging two

5 percentage points of house price, so reducing

6 your down payment by two percentage points of

7 house price and setting that aside in this

8 liquidity account, and that will take us from

9 that less than one bucket into that three to four

10 bucket. And so that's the way that we're going

11 to make this exchange.

12 And so if we use this chart to think

13 about that, what I'm -- what we're actually --

14 the impact of making that trade would be think

15 about taking borrowers out of a particular point

16 on that dark blue line, sliding them two points

17 to the right because their LTV would go up by 2

18 percentage points and then sliding them down from

19 the dark blue line to the light blue line.

20 And you can see right away that while

21 default rates do increase at higher LTV levels,

22 the decrease from adding the additional liquidity

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1 is significantly larger.

2 Important, important caveat, I'm not

3 an economist, but just like Ryan, I have a caveat

4 here. This is correlation-based evidence, right? 

5 And so the borrowers within each dot and the

6 borrowers on each line are substantially -- could

7 be substantially different, right? Different

8 borrower characteristics. And so -- and that's

9 super important to keep in mind, and I'll talk a

10 little bit about how that might impact our

11 results.

12 So what do we think that -- if I was

13 to try to reproduce this chart but instead of

14 just showing you correlation-based evidence,

15 actually show you the causal version of this

16 chart, what do we think that that would look

17 like? I'm going to split it up, and I'm going to

18 talk about first the impact of lower equity on

19 default, and then I'll talk a bit about the

20 impact of more liquidity on default.

21 So if I think about the first one, if

22 I think about the impact of more equity on

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1 default, and that's sliding on the line. That's

2 essentially the slop of that line. We think that

3 the causal version of this chart would actually

4 be a bit flatter.

5 And the reason we think that it would

6 be a bit flatter is, first, borrower credit

7 quality tends to decrease with LTV, so as I move

8 to the right, we're talking about lower FICO

9 scores. Now, if I controlled for that, that

10 would flatten the line, number one.

11 Number two, we also think that lenders

12 use down payment size as a proxy for the

13 borrower's ability to save, and therefore, as a

14 predictor of default risk. However, as you can

15 see from the -- this chart and the previous

16 charts, our data suggests that there's a stronger

17 relationship between liquidity and default than

18 between LTV and default.

19 And I should add a very important

20 point here that for this discussion, we ignore

21 the loss mitigation effects of a larger down

22 payment. Clearly in the event of a foreclosure,

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1 having a larger down payment makes a very large

2 difference.

3 So that's how we think the -- so just

4 to summarize, we think that the slope of the

5 causal version of these lines would be a bit

6 flatter. With respect to the impact of

7 additional liquidity on default rates, here we

8 think that -- here we're thinking about the

9 difference in the levels between the lines. Here

10 we think we're probably overstating that impact a

11 bit.

12 And there are three reasons why we

13 think that we're overstating that impact. First,

14 as I talked about before, the borrowers in each

15 point and on each line are different. And so

16 with respect to the preferences of the borrowers

17 on the dark blue line relative to the preferences

18 on the light blue line, there are probably

19 important differences in terms of preferences

20 regarding savings and consumption, and so it's

21 important to keep that in mind.

22 Second reason is that we don't see

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1 liquidity. We don't see checking and savings

2 accounts held away from Chase. And while we

3 would like to believe that all of our customers

4 hold all of their liquidity with Chase, we know

5 that that's not the case.

6 If we were to include what I would

7 call unobserved liquidity, we think that the gaps

8 between those lines would shrink slightly, and I

9 can talk about that more during the Q&A if people

10 are interested.

11 And thirdly, we think that one

12 countervailing measure that would actually

13 increase the impact of more liquidity on default

14 rates would be to have some access requirements

15 for this emergency mortgage reserve account. So

16 you could imagine certain hurtles that the

17 borrower would have to cross in order to access

18 that liquidity would actually improve the impact

19 on default.

20 Okay. So one question, natural

21 question would be, look, you just showed me

22 results using this snapshot of servicing in 2014. 

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1 Why didn't you use the origination sample, so

2 that first sample that I talked about? Why

3 didn't you use that sample to produce these

4 results?

5 And the reason we didn't use that

6 sample to produce the results is because of

7 selection bias. And what I'm showing in the

8 chart here, and I'll just focus really -- let's

9 just focus on the green and blue bars.

10 What I'm showing here is now for both

11 samples, I'm showing you the share of mortgages

12 in those bars at origination. That's the blue

13 bars. And the share of mortgages in each LTV bin

14 in January of 2013, and that's the green bars.

15 And unsurprisingly, at origination,

16 there's this big spike at 80 percent, again not

17 surprising. There are also peaks at 90 and 95

18 and 99 all created by the pricing incentives that

19 are part of mortgage financing, right?

20 And so you get a lower interest rate

21 if you meet a particular LTV hurdle, but there's

22 no additional incentive to go, for example, from

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1 making a 20 percent down payment to making a 21

2 percent down payment. And so we see a lot of

3 bunching in our data at origination that also

4 affects the amount of liquidity people have on a

5 post-closing basis.

6 Again, if there's no incentive to put

7 down 21 percent, we'll put down the 20, the 1

8 percent remains in your checking and savings

9 account, and that spikes liquidity at 80 as well.

10 And so for those reasons, when we do

11 this analysis using our origination sample, it's

12 noisy, and it looks like this. So the message is

13 we think consistent -- you have to squint a bit

14 to see it, and the noise creates some of the

15 bumpiness in the data.

16 Okay. In the interest of time, I'm

17 going to skip that slide and just go straight to

18 the implications. So I'm on Page 9.

19 As I've talked about, we're providing

20 what we think is correlation-based evidence about

21 the impact of -- the potential impact of

22 liquidity and improving default rates. A pilot

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1 program could test this, right? And so again,

2 you'd set up a treatment and control framework

3 where you had certain folks in the example that I

4 used at the outset put down instead of 5 percent,

5 3 percent, take the residual 2 percent, hold it

6 in this emergency mortgage reserve account, and

7 compare the default rates for that group versus

8 the control group that put down the original 5

9 percent.

10 Importantly, you'd have to think a bit

11 about balancing access versus restrictions,

12 right? And so you could think about at one end,

13 if you make the restrictions too tight, the

14 account loses its value, right? If the borrower

15 can't actually tap into the funds, they're not

16 helpful to the extent that they end up in

17 financial distress.

18 On the other hand, at the other end of

19 the spectrum, probably don't want to give people

20 a debit card associated with that account because

21 you're likely to have less of an impact on

22 reducing default.

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1 And then there is a lot of takeaways

2 from behavioral finance that we could use just in

3 terms of naming conventions, setting a target,

4 automatic enrollment, and things of that nature

5 that would probably increase the effectiveness.

6 So maybe I'll stop there.

7 MR. PEARCE: Thank you. We had three

8 great presentations on different aspects of the

9 mortgage market. I know we've got a lot of

10 housing and market -- mortgage market expertise

11 in this room, and so without further ado, I'll

12 just open it up for questions from committee

13 members.

14 We've got about 20 minutes to -- Bob?

15 MEMBER ANNIBALE: Hi. Bob Annibale. 

16 Just a quick question on -- it's so informative,

17 all of you. Thank you for this.

18 And on this last presentation, have we

19 thought about, you know, any kind of incentive

20 for someone to keep that liquidity premium

21 because there's a bit of a drag obviously on --

22 in the markets, today's rates, too.

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1 So if you had an incentive to keep

2 that 1 percent signed, and could it be then kept

3 in some sort of escrow. In other words, to

4 dedicate it as a backstop, you know. You might

5 make some difference on the interest rate, or

6 not. And would that have then -- if this data

7 proves itself on your loan loss reserves and

8 other things, it should also prove your own

9 economics and capital allocation. Have you

10 thought about that at all?

11 MR. BHAGAT: Yeah, no, that's exactly

12 right. Escrow account would be one way to

13 implement this type of account. And I think

14 importantly, particularly in the treatment and

15 control framework, but -- and if that works, you

16 would want to provide the borrower that sets up

17 this type of account with interest on that

18 mortgage reserve account, for example, and

19 potentially a lower mortgage rate -- 

20 MEMBER ANNIBALE: Yeah.

21 MR. BHAGAT: -- that reflects the

22 lower probability of default. And importantly,

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1 those two things could actually make the monthly

2 outlays for that -- folks that have that account

3 -- 

4 MEMBER ANNIBALE: Right.

5 MR. BHAGAT: -- the same as the

6 monthly outlays for the folks that made, in our

7 example, the 5 percent down payment.

8 MEMBER ANNIBALE: That's exactly what

9 I had in mind.

10 MR. BHAGAT: Right. And the numbers

11 work out. I think we looked at about a 12.5

12 percentage point -- 12.5 basis point discount in

13 terms of mortgage rate, and about 2.25 percent

14 interest. Obviously with the Fed easing, that's

15 potentially a bit high compared to where we were

16 in -- a few months ago.

17 But 2.25 percent on the balance in

18 that emergency money reserve account would

19 accomplish equalization of monthly outlays

20 ignoring the effect of taxes.

21 MEMBER ANNIBALE: Yeah. And it would

22 reduce your loan loss reserve requirement and

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1 your capital allocation as a lender.

2 MR. PEARCE: Great. Andrea, you were

3 talking about reserves earlier.

4 MEMBER LEVERE: So you can get your

5 payment from me before you leave. I don't know

6 if you were here when I said that we are piloting

7 exactly this. And with Homewise, many people may

8 know Homewise in New Mexico and with the

9 Statewide Housing Authority in Oregon.

10 We're not doing an RCT, but we are

11 testing out all these different designs exactly

12 as you're saying but in the terms of how --

13 what's easiest way for people to set up these

14 accounts, what are the incentives both with the

15 mortgage or with the savings, and it is on a

16 voluntary basis at this point.

17 But with the results from this -- so

18 we'll have real live results. It takes a while

19 obviously, in terms of what this can do. And it

20 also, what's interesting, and it could get to

21 Lariece's piece is how does this help people

22 imagine themselves as homeowners as well.

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1 And so to me, that's another benefit

2 of what this could be doing. And we are

3 discussing an expansion of this program with your

4 colleagues.

5 Since I also have the floor, I'd love

6 to know -- we were, Margaret and I, talking. In

7 your data, are you looking at all at student

8 debt?

9 MS. BROWN: We do. We have the

10 student loan debt because it's part of the credit

11 bureau data.

12 MEMBER LEVERE: Right, because it

13 wasn't on any of the slides.

14 MS. BROWN: It is part -- we've -- I

15 have it separate in the analysis and then we --

16 in order just to summarize the slides, I've

17 included it as part of the credit attributes, and

18 it's also in the DTI.

19 So yes, we know whether or not they're

20 delinquent on their student loans, as well as the

21 amount of the student loans. So that's part of

22 the full credit picture.

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1 I think I mentioned in brief passing,

2 it does matter. Student loan debt matters. It's

3 not the major determinant of all of the credit

4 factors. In fact, the -- of course the credit

5 scores and income -- income matters more. But

6 yes, student loan debt does matter.

7 MEMBER LEVERE: I think there's a very

8 broad sense that it is an incredible driver of

9 the lack of household formation -- 

10 MS. BROWN: Right.

11 MEMBER LEVERE: -- and homeownership. 

12 And so being able to tease out how much of it is

13 truly that and how much is other factors would be

14 very helpful.

15 MR. PEARCE: Jonathan?

16 MEMBER MINTZ: I just have a quick

17 clarifying question and then a question for Ryan.

18 Your findings around the importance of liquidity

19 in comparison to equity I assume accounts for

20 income. Is that -- 

21 MR. BHAGAT: We -- so that's a good

22 question. So we did not -- 

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1 MR. PEARCE: Could you ask the

2 question again?

3 MEMBER MINTZ: Oh, I'm sorry. I

4 prefer to mumble.

5 I just want to make sure that your

6 findings about the importance of liquidity in

7 comparison to equity accounted for income.

8 MR. BHAGAT: So that's a great

9 question. Thank you for that. We, at the time -

10 - at the time that we produced this research and

11 still today, we do not have access to credit

12 scores for research purposes.

13 And so we didn't -- because of that,

14 controlling for variables including income and

15 others, felt wholly incomplete, and so we didn't

16 do this analysis in a way -- in a statistical way

17 where we control for the typical underwriting

18 variables.

19 That is something that we would --

20 that we're hoping to do going forward if and when

21 we get access to credit scores.

22 MEMBER MINTZ: But then how -- I'm

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1 going to ask this in sort of a dumb way. How do

2 you decide that what you're looking at is in

3 comparison to equity as opposed to being rich?

4 MR. BHAGAT: Right. It's a good

5 question. So we're essentially assuming that

6 people that are in adjacent LTV bins at

7 origination are similar.

8 MEMBER MINTZ: I see.

9 MR. BHAGAT: Not -- but not the same. 

10 But it's absolutely -- that's something that with

11 the ability to look at credit score we would test

12 in a more -- 

13 MEMBER MINTZ: You also can look at

14 deposits, can't you? Can -- you guys can -- I

15 think you can look at models of income projection

16 based upon direct deposits.

17 MR. BHAGAT: Yes, we can.

18 MEMBER MINTZ: Right.

19 MR. BHAGAT: We can. We haven't

20 incorporated that into this analysis. The thing

21 that we do do -- and I would direct you to the

22 website or I'd be happy to follow up after -- is

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1 we do show that there is a very close connection

2 between a loss of income and mortgage default.

3 So for folks that default, there is a

4 drop in income that precedes their third missed

5 mortgage payment of only about a couple of

6 months, and that that pattern persists -- if we

7 break our sample up by income quartiles, that

8 pattern persists for both the highest and the

9 lowest income quartile.

10 So that's what points us at --

11 obviously the default rates for folks in that

12 higher income -- highest income quartile are

13 lower than the default rates for folks in the

14 lowest income quartile.

15 However, the proximity of the loss in

16 income to the first missed mortgage payment in

17 terms of time suggests that even folks in that

18 highest income quartile did not have enough

19 liquidity to withstand the loss of income.

20 MEMBER MINTZ: Interesting. And,

21 Ryan, can I just ask you a super quick question? 

22 Is there a -- when you were talking about the

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1 movement of availability and in -- of mortgages

2 and you're talking about the versions -- I'm not

3 a housing person, so my words are going to be

4 wrong, but the versions of mortgages that are

5 riskier products, is there an industry sweet spot

6 on what that balance is? Is there a kind of a --

7 like a safe harbor-ish policy component around

8 where ideally that mix is, presumably then to be

9 able to encourage lenders to be hitting more of

10 our intended market?

11 MEMBER BOSTIC: So I'm not -- 

12 MEMBER MINTZ: I told you I was dumb

13 about this. I'm just -- you were talking about,

14 you know, where available mortgages were and the

15 percent of mortgages that were riskier mortgages,

16 right, and that obviously -- that riskier

17 mortgages are now way down from where they were.

18 MR. GOODSTEIN: You're talking about

19 like -- 

20 MEMBER MINTZ: Right. And so -- 

21 (Simultaneous speaking.)

22 MEMBER MINTZ: -- I guess what I'm

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1 asking is: is there some sort of a policy-based

2 or other indicator of where it ought to be for

3 purposes of like sound -- safety and soundness

4 for lending institutions?

5 MR. GOODSTEIN: So I don't think

6 there's a right answer on that. I mean, I can

7 just point to, you know, there are obviously a

8 number of people in this room and other, you

9 know, researchers in the industry, analysts who

10 have thought a lot about this.

11 So just one thing that comes to mind

12 is like Urban Institute has a housing finance

13 policy center. They have an index of credit --

14 mortgage credit availability which is just sort

15 of -- really they look at sort of the ex-ante

16 riskiness of loans and they sort of trace that

17 over time.

18 And so if you were to ask them that

19 question, they would point to like the 2002, 2003

20 period as like a reasonable -- 

21 MEMBER MINTZ: Got it.

22 MR. GOODSTEIN: -- standard. That's,

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1 you know, one opinion. I don't think there's a -

2 - 

3 MEMBER MINTZ: I just wonder whether

4 that could play a role in safety and soundness

5 examinations that might help the lenders feel

6 more comfortable.

7 MR. PEARCE: One thing that just from

8 a regulatory perspective, CFPB has the ability to

9 repay rule that has a qualified mortgage bucket. 

10 And one of the elements of qualified mortgage

11 definition involves the absence of some of the

12 risky features that Ryan was talking about from

13 the data.

14 Martin then Rafael -- or Rafael and

15 then Martin.

16 MEMBER BOSTIC: So thank you, Martin.

17 MR. PEARCE: He's never done that

18 before, by the way.

19 MEMBER BOSTIC: He's a trailblazer,

20 right?

21 I guess I had a couple of reactions. 

22 One is I agree with Andrea and Margaret. There

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1 should be a whole separate student loan paper

2 because the marginal impacts of student loan

3 debt, those dynamics are not well understood, but

4 we know they're out there. And clarity we can

5 get on that can sort of help us partition the

6 population.

7 I cannot -- I would strongly recommend

8 that you do the controls, do some analysis of the

9 controls when -- very early in my career, I was

10 at the board, and we did some early warning stuff

11 for fair lending exams, and we didn't have all

12 the controls.

13 But if you do the controls and things

14 disappear or shrink to small levels, and you're

15 pretty sure you don't have anything else, right,

16 and you can sort of be clear because your

17 statement that liquidity may be more important

18 than equity, income, and payment burden, that's a

19 strong statement.

20 And to me, it just raised the

21 question, if that's true, then probably almost

22 all of our underwriting models are not right.

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1 MR. PEARCE: Right.

2 MEMBER BOSTIC: So we should sort of

3 really unpack this and really understand it, and

4 yeah, it would be very interesting to have deeper

5 understanding on.

6 And then my last question is really

7 about the geography results. So they were very

8 interesting. You showed aggregate results and

9 then you showed breakdowns for some states and

10 they didn't all balance out.

11 So for the African American/white home

12 ownership gap, in the aggregate it was like

13 three. It was -- 

14 MS. BROWN: That's right.

15 MEMBER BOSTIC: -- a small number. 

16 But then you showed New York and Mississippi as

17 being gigantic.

18 MS. BROWN: That's right.

19 MEMBER BOSTIC: Right? And so where

20 are the offsets? Like how do we get to an

21 aggregate of basically zero if you've got these

22 big effects in a small number of places? And

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1 like does the big positive and the big negative -

2 - 

3 MS. BROWN: They kind of cancel each

4 other.

5 MEMBER BOSTIC: -- they probably

6 balance out, but they don't really balance out. 

7 Right? That can have implications for how we

8 think markets will work in those places. 

9 Similarly on the Hispanic/white gap, there's a

10 big gap there. The four examples you showed,

11 none of them had a big gap.

12 So for me, it was like where are the

13 four or five -- you ranked in order of the states

14 by biggest contribution to gap. Like what are

15 those states? What do they look like? How does

16 that play out?

17 And then the other question, the last

18 question I would have, is: is this at the state

19 level? Can you do this at the MSA level?

20 MS. BROWN: I think we can. We have

21 -- 

22 MEMBER BOSTIC: Because that would be

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1 super interesting.

2 MS. BROWN: -- county data.

3 MEMBER BOSTIC: I think about, like I

4 say, for Florida or Texas where you have

5 significant demographic variation, or Ohio. 

6 Cleveland, Columbus, Cincinnati, they're not

7 going to be the same.

8 MS. BROWN: That's right.

9 MEMBER BOSTIC: And so just sort of

10 breaking that out I think would be really

11 interesting for -- you know, for helping to think

12 about how -- what's really driving these

13 differences and getting that understanding. But

14 this is all very interesting stuff.

15 MS. BROWN: Yeah. Thank you.

16 Just a quick comment, thank you so

17 much for the additional paper idea. We're always

18 looking for those. 

19 (Laughter.)

20 MS. BROWN: For the -- I think we can

21 do MSA level. We actually received feedback to

22 do the state level. We had not done that in an

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1 earlier version of our draft. So if our data

2 allows us to go more granular, I think we'd be

3 interested in taking a look at that.

4 On the student loan piece, I -- you

5 know, maybe in a future iteration we can even

6 have the bar graph just with the student loan

7 piece broken out. It is in our data. I do --

8 wanted to look up what the magnitude of that

9 impact was for you. 

10 We have -- when we include the student

11 loan relative to the income, the effect of that

12 on the likelihood of transitioning to a mortgage

13 is -- it decreases that likelihood by 1.3

14 percent. And -- but that's the -- that may sound

15 small, but that's with a lot of other factors in

16 play. Put that in context with another similarly

17 scaled measure such as house prices relative to

18 income, for instance, that is a negative 5.6, so

19 there's -- that's a larger effect than the

20 student loan debt.

21 However, another smaller effect is the

22 auto debt and the credit card debt, which we also

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1 include as separate controls, and those were

2 actually much closer to zero in our analysis. So

3 there -- it matters, but there's a lot of

4 different things. So it may sound like a small

5 effect, but it does matter.

6 MR. PEARCE: So Kanav, did you want to

7 say anything on here?

8 MR. BHAGAT: Yes. I do -- yes.

9 MR. PEARCE: I just want to give you

10 a -- 

11 MR. BHAGAT: Thank you very much for

12 the suggestions. And that is absolutely -- once

13 we have access to FICO, which hopefully knock on

14 wood will be in a few months, we will absolutely

15 do -- 

16 MEMBER BOSTIC: Yeah, don't wait for

17 FICO.

18 MR. BHAGAT: -- we do the tests.

19 MEMBER BOSTIC: You could do it right

20 now. Like to me, if you ran the analysis and you

21 had payment burden, liquidity, and income all

22 just together, you're going to get three

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1 coefficients that will rank order their

2 importance.

3 MR. BHAGAT: Right.

4 MEMBER BOSTIC: Right? That's a

5 baseline. And that will give you some sense. 

6 You know, credit score in my experience basically

7 just reduces everything. It has not historically

8 led to sort of those interactions such that the

9 ranking changes. So I think you can know much of

10 the answer now.

11 MR. BHAGAT: Right. One thing we have

12 done is we've looked at -- we have looked at

13 event studies around mortgage default, and

14 whether you segregate your sample by total debt

15 to income above or below 43, whether you look at

16 the highest income quartile or the lowest income

17 quartile, or whether you look at under water

18 versus above water in terms of home equity, the

19 connection between the income loss and mortgage

20 default is always there, and it's always income

21 tends to drop somewhere between one and three

22 months before people start missing their mortgage

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1 payments. And so it suggests to us again these

2 other factors might be compounding factors, but

3 the single constant in all of those equations is

4 this drop in income that -- 

5 MEMBER BOSTIC: So can I just react to

6 that because, you know, life happens. What

7 you're saying, you live, and then there comes an

8 episode and you lose your money, you lose your

9 income, and so you default.

10 I don't think that would surprise

11 anyone here. I guess for me, the question is

12 like if I'm ex-ante, and I'm looking at an

13 applicant or I'm trying to figure out who needs

14 the extra counseling or extra servicing

15 attention, like what information would be useful?

16 And that's kind of why I want you to

17 just give me everything you know about me right

18 now, and can I use that to identify my higher

19 risk population, and maybe if you have other

20 algorithms to say who is more likely to have life

21 happen, right? But that's a different kind of

22 exercise. And so the part -- like separating

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1 them out I think would be really an important -- 

2 MR. BHAGAT: Yeah.

3 MEMBER BOSTIC: -- but useful thing.

4 MR. BHAGAT: No, that's a great

5 suggestion. We'll absolutely do that. And I

6 think part of me wants to say that knowing what I

7 know already is that -- and the reason why we

8 think this emergency mortgage reserve account

9 might be impactful is because life does happen,

10 and actually predicting ex-ante who it will

11 happen to and when is super hard. And that's why

12 the presence of that liquidity through this

13 reserve account we think could be super

14 impactful. But it's a great point. Thank you

15 for that.

16 MR. PEARCE: Martin, and then I

17 thought I saw Margaret raise her hand, if you

18 want to give that -- and then Pam at the end.

19 MEMBER EAKES: Thank you. These are

20 all fabulous presentations of data and really

21 appreciate it. I have a question I think for

22 each of you.

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1 Kanav, the first one for you is I

2 would like to see some sort of breakout on race

3 and wealth. When you talk of that three payment

4 equivalents, I think that that will eliminate

5 virtually all black applicants that if we said

6 that the wealth or cash wealth for black

7 families, the median is $200 to zero, you're not

8 going to have -- and it's somewhat consistent

9 with what I think I remember from HMDA data for

10 Chase that it was a little bit truncated for

11 African American borrowers during this time

12 period for low LTV -- I mean, high LTV, low down

13 payment loans.

14 So when you break them out, my

15 hypothesis listening to you is that you are

16 really -- you're proxying for wealth, for family

17 wealth, and you're excluding by having the three

18 payments. In that category, the risk factors --

19 that somehow race is going to play in there, it's

20 going to be -- I agree with Rafael that credit

21 score is generally a compilation of other

22 variables so that it by itself doesn't give you

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1 much intuition about explanatory power that the

2 underlying variables do.

3 So I don't know. I mean, I would love

4 that.

5 And just another data geek point is

6 that since you weren't measuring cash in accounts

7 that were in escrow, these were just actual

8 operating accounts that they had, and the way

9 that you define default as being three payments

10 behind, that if you have cash and people use

11 their cash to make the payment, of course you're

12 going to have a lower default if you started out

13 with three payments as a gimme.

14 See what I'm saying? That if you have

15 cash there and you got behind one month, and you

16 depleted your cash by one month, you got behind

17 two months, you got behind three months, by

18 defining it as three months past due within the

19 first three years, then the people who actually

20 default who started with a lot are really six

21 months past due. They just had reserves to make

22 that payment.

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1 So one of the things we learned early

2 on is that delinquency does not ultimately equal

3 default unless you have a period like 2008

4 through '12, and lots of people who have very low

5 cash reserves, they get behind 90 days --- I

6 mean, we did this for 30 years -- and they catch

7 up. They catch up when things get better. They

8 have the same life events: death, illness,

9 divorce, job loss. Those are the four income

10 disruptors that really hit you.

11 So I just would love to see that for

12 -- but Lariece, what I wanted to ask you is a

13 thought about DTI. So DTI is the most robust

14 discussion right now trying to figure out the

15 qualified mortgage, whether or not we go back to

16 a 43 percent DTI without any GSE patch.

17 And Peter Zorn, one of your

18 colleagues, had done a paper at Harvard, which

19 you may have seen or disqualified, I'm not sure,

20 but basically concluded that 95 percent of low

21 income borrowers, which would be a good proxy for

22 communities of color, would be excluded from

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1 being approvable loans if you had a 43 percent

2 debt to income ratio.

3 So it's -- my question is whether you

4 think that that is a valid standalone overlay, or

5 whether it should be built into the model with

6 all the other compensating factors. And then for

7 you on student debt, the piece that I wanted to

8 comment or think about there is that anecdotally,

9 when we see borrowers and we see, you know, a few

10 thousand, so it's not enough to really conclude

11 anything, we're seeing that a third of the

12 applicants we have that are African American are

13 disqualified from getting loans, not that they

14 get the loan and then default later. They simply

15 are not able to even qualify because of the

16 student debt.

17 So that when you do your study on

18 student debt, in addition to seeing whether it

19 impacts the default for people who get the loan,

20 if there's some way to measure how many people

21 disqualified and don't -- aren't able to get the

22 loan at all. And finally, Ryan, I was thinking

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1 about -- and I don't know the answer to this, but

2 it's really interesting to me, is that thinking

3 about what causes a bubble and then a collapse.

4 And most of us think of it as loose

5 underwriting. So we had several places in there

6 where you concluded that the underwriting is

7 nowhere near -- I mean, despite the rhetoric we

8 sometimes hear in the press, the market still

9 feels pretty tight in terms of underwriting in

10 even the special programs where we used to try to

11 have 20 percent of borrowers below 620, nobody's

12 trying that anymore.

13 The question is can we get the minimum

14 threshold to be lower than 680? So the -- and

15 all the products that went away with Dodd-Frank

16 were clearly part of the problem.

17 The question is can you have a deep

18 recession that is based on asset bubble that is

19 simply the fact that we've got real interest

20 rates at zero or negative for a long period of

21 time so that asset prices are going up. Can you

22 have a bubble just from that without having the

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1 additional increment by loose underwriting?

2 So loose underwriting creates

3 defaults, but it also creates more demand. So

4 I'm trying to get you to break those two out if

5 you have any intuition at all from the data that

6 you presented.

7 MR. GOODSTEIN: So let me just repeat

8 the -- again, I want to make sure I sort of get

9 where you're going with that. So are you asking

10 whether, you know -- so given that for the most -

11 - you know, from what we see the loan products

12 themselves don't have these risky features. Are

13 you asking whether -- how likely it is that we're

14 going to see sort of a fall in real estate

15 prices?

16 MEMBER EAKES: Yeah. I mean, what I

17 took from your presentation is we haven't seen

18 underwriting in terms of FICO score and other

19 things really deteriorate back to 2005. We don't

20 have any of the products of the NegAm, No-doc,

21 all those kinds of products that were essentially

22 prohibited under Dodd-Frank.

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1 So I'm asking -- and so you were

2 concluding from that, I take it, that we're

3 really not near to hitting a 2008 event. And

4 being the paranoid fool that I am, I'm sort of

5 thinking if we also haven't had a period where we

6 had 10 years at zero interest rate, real interest

7 rates, that do we have an asset bubble.

8 MR. GOODSTEIN: Right.

9 MEMBER EAKES: Could that alone create

10 a collapse without underwriting weakness?

11 MR. GOODSTEIN: So yeah, I guess I'm

12 -- so I don't really know the answer to that. I

13 guess, what I -- I do sometimes worry when I make

14 a presentation like that I'm too rosy, you know,

15 in terms of, you know, trying to make the point

16 that we're not -- 

17 MEMBER EAKES: It's better to be rosy

18 than a paranoid fool.

19 MR. GOODSTEIN: I mean, you know,

20 there -- underwriting is loosening, right? There

21 are loans. You know, so for example, you know, I

22 shared credit score trends.

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1 LTVs are going up, DTIs are going up

2 on average, so I mean there is some risk going

3 back, you know. Risk is expanding.

4 That, together with, you know, the

5 fact that we have not had a recession, you know,

6 so I think performance of loans is going to

7 deteriorate.

8 I mean, I -- certainly when we see the

9 next recession, right? The question is: how bad

10 is it going to be? You know, I don't have a

11 great answer to that other than I don't -- you

12 know, I don't expect it to look like that.

13 MEMBER EAKES: My own intuition is

14 that the underwriting risk that's been assumed

15 now -- 

16 MR. GOODSTEIN: Yeah.

17 MEMBER EAKES: -- is maybe 10 percent

18 of what it was in 2005 or -- I mean, it's, you

19 know, we had 25 percent of the entire mortgage

20 market that were subprime exploding arms that had

21 no chance of doing anything but exploding into

22 foreclosure. We don't have any of that now.

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1 MR. GOODSTEIN: Right.

2 MR. PEARCE: So I've gotten permission

3 from the conductor of master of ceremonies here,

4 we are already over time, and so I want to go to

5 lightning round.

6 And so anybody has a quick response to

7 one of Martin's questions, if we could be quick,

8 and then I want to try to get Margaret and Pam to

9 have a moment to ask a quick question, if we

10 could have another five minutes or so.

11 MS. BROWN: Thank you. We -- you've

12 -- this will be quick because you've touched on a

13 topic that in my current role I can't speak much

14 on at this point with respect to the DTI

15 potential overlay.

16 Peter Zorn is still in the business of

17 dealing with trying to make sure that the models

18 are incorporating the risk associated with DTI as

19 robustly as he possibly can with his particular

20 group.

21 And yes, you're absolutely right that

22 minorities are going to be kind of

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1 over-represented and that higher DTI range, so

2 that would be relevant considerations for those

3 who are able to make those considerations, of

4 which I can't.

5 MEMBER EAKES: Study which didn't use

6 Freddie Mac data said that DTI alone between 43

7 and 50 would add like two to four loans of

8 default per 10,000 loans.

9 MS. BROWN: Right.

10 MEMBER EAKES: Very small -- 

11 MS. BROWN: Additional default -- 

12 MEMBER EAKES: -- additional default

13 and -- 

14 MS. BROWN: -- relative to the -- 

15 MEMBER EAKES: -- very high amount of

16 exclusion for access.

17 MS. BROWN: Right. And the student

18 loan piece I think, yeah, we're definitely

19 interested in taking a look at that group, thank

20 you.

21 MR. BHAGAT: So I'm not prohibited

22 from commenting on total DTIs.

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1 (Laughter.)

2 MR. BHAGAT: Everything that we have

3 looked at has suggested that total DTI is not a

4 particularly effective measure for preventing

5 default. There is academic research that suggests

6 that had the 43 percent limit been in effect in

7 the pre-crisis period, it would not have had a

8 material impact on defaults during the crisis,

9 just as -- just to provide one example. And our

10 research suggests something similar.

11 With respect to the point you made on

12 a breakout by race and wealth, I think it's

13 important to note that because we're talking

14 about trading equity for liquidity, and if you

15 were going to make whatever down payment you were

16 going to make whether it was 3 or 5 or 7 or 20,

17 reducing that slightly and taking that residual

18 cash and putting it aside in a reserve account,

19 it shouldn't impact affordability at all, right?

20 We're not asking for additional

21 liquidity. We're asking for just the same amount

22 of the down payment you were going to make, make

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1 that down payment slightly smaller and set up

2 this liquidity account. So I think that's

3 important to note. I think the cuts by race and

4 wealth are still super important, but I want to

5 emphasize that we're not impacting liquidity in

6 the way that -- in a way by having an additional

7 down payment or an additional cash requirement.

8 MEMBER EAKES: That's your proposal,

9 but your data that you had doesn't make that

10 breakout because it's not an escrow account.

11 MR. BHAGAT: No, that's right. 

12 Absolutely. Absolutely.

13 And then just with respect to -- yes,

14 it does seem somewhat mechanical like if you have

15 money in your account, you're going to make your

16 mortgage payment. But that -- I would just note

17 that that effect seems to persist over three

18 years. So the level of cash that you had in the

19 month after closing seems to be impactful for

20 three years hence.

21 And so I think it's -- that's the

22 thing that I think is powerful. But you're

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1 absolutely right that there is a mechanical

2 relationship between the amount of cash you have

3 in your checking account and whether or not you

4 can make your mortgage payment. So point well

5 taken. Thank you.

6 MR. PEARCE: All right. Margaret, can

7 I ask you to ask your question?

8 MEMBER LIBBY: Yes. I will ask my

9 question much in the way that I made my final

10 statement earlier, try to like by not asking it

11 or keeping it short. But just short of building

12 on the questions around the student debt, and I

13 think that breakdown that Martin eluded to around

14 race ethnicity, but I think even more broadly I

15 would be really interested in looking at cutting

16 these data by gender as well, just knowing how

17 the race gap I think is -- or the racial wealth

18 gap, I should say --- is really I think

19 intersects with the gender wealth gap, and would

20 just be really interested in -- I don't know if

21 that's another paper idea, but --

22 MR. PEARCE: You've got a lot of work

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1 to do, right?

2 MEMBER LIBBY: Yeah. Yeah.

3 MS. BROWN: Thank you.

4 MEMBER LIBBY: But thank you all, this

5 was really -- 

6 MS. BROWN: Yes.

7 MEMBER LIBBY: -- very interesting

8 stuff.

9 MR. PEARCE: Great.

10 And Pam?

11 MEMBER PATENAUDE: Ryan, you list four

12 government loans on the second slide on the

13 non-bank share of mortgage originations. John

14 Weicher and I don't know what FSA stands for. 

15 The VA, FHA, and Rural Housing?

16 PARTICIPANT: Yeah, farm -- it's farm

17 credit.

18 MEMBER PATENAUDE: Oh, it's farm

19 credit.

20 MR. GOODSTEIN: They're both rural

21 housing programs, right? Yeah. But most

22 government loans are FHA or VA. These are

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1 smaller programs.

2 MR. PEARCE: And anyone else with a

3 question, we'll -- we can cover during the break

4 maybe.

5 So thanks again to the panelists for

6 the work on the mortgage market.

7 (Applause.)

8 MR. MILLER: Why don't we reconvene at

9 2:50, and we'll try to make up a little bit of

10 time. Thank you to the panelists.

11 (Whereupon, the above-entitled matter

12 went off the record at 2:41 p.m. and resumed at

13 2:51 p.m.)

14 MR. MILLER: Well, we've -- as we

15 often do, we saved the best for last. So Emerson

16 Hall, the associate director in consumer

17 community affairs, is going to moderate this

18 panel for us. Emerson?

19 MR. HALL: Good afternoon, everyone. 

20 I'm delighted to have an opportunity to be here

21 and to moderate this panel on sustainable bank

22 accounts.

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1 It's always a pleasure. I think this

2 is my second time, but I've been to these

3 meetings probably about four times now, and it's

4 always a pleasure to be the last panel because

5 you get a chance to hear a lot of the comments

6 that takes place during the day.

7 I always write out my remarks, so I'll

8 know exactly what I want to say because at the

9 beginning of the day I have certain things I want

10 to say, and towards the end of the day I have

11 other things I want to say because of what I've

12 heard.

13 But it's been really interesting

14 today. And as John has said, I'm an associate

15 director of community affairs, recent position

16 that I was selected for. But I'll -- I do want

17 to share this because of the fact of what I've

18 heard today is every time I come to these

19 meetings, I get inspired. I get motivated. I

20 get encouraged, energized as a result of

21 listening to what you guys share with us.

22 We provide a lot of information. We

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1 have good panels that provide quality

2 information, but you guys give us good feedback.

3 In community affairs, it's our job to go out and

4 execute the strategies to try to get results,

5 impact, and outcomes.

6 And what I see and what I hear when

7 I'm in the meetings here is I get a chance to be

8 among the champions, the heroes, those that are

9 actually not just talking the talk but walking

10 the walk. I was impressed by all of you in

11 regards to the information you shared this

12 morning, and I was particularly impressed with

13 the information that Martin shared. I had a

14 chance to talk to him at lunch about black

15 mothers. I have a black mother, and it certainly

16 is -- we don't just talk about what we're trying

17 to do. We're talking about what's actually being

18 done, and that's impressive.

19 So we get as much here at the FDSE as

20 we give, and so we certainly appreciate that. So

21 I'm going to get on point here with my remarks.

22 But we have a great panel. We appreciate you

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1 guys staying the entire day to get a chance to

2 participate with this panel as well.

3 So again, my name is Emerson Hall, and

4 I'm pleased to have the opportunity to come

5 before the Committee as a moderator for this

6 panel today. One of the key challenges to

7 expanding participation in the nation's banking

8 system is undoubtedly getting the attention and

9 commitment of unbanked and under-banked

10 consumers. And for all -- most people, including

11 the unbanked consumers, don't wake up in the

12 morning thinking about their choice of financial

13 service providers.

14 So our panel this afternoon will

15 showcase and address several effective strategies

16 that have helped increase the rates at which

17 consumers are willing to consider to obtain and

18 use bank accounts. Experience has taught us that

19 sincere intentions and enormous efforts do not

20 necessarily translate into successful outcomes.

21 To help us, our panelists will draw on

22 a wealth of experience, research, and insight. 

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1 First, we will hear from Lindsay Ferguson,

2 director of strategic engagement for America

3 Saves. Lindsay is responsible for creating and

4 managing American Saves outreach week,

5 communications and partnership strategies

6 including ongoing technical assistance for new

7 and existing America Saves local campaigns. Ms.

8 Ferguson has been in the non-profit sector with a

9 focus on financial stability for over a decade. 

10 She will share with us principles of social

11 marketing and behavioral economics that motivate

12 and encourage Americans to save. Over 710,000

13 Americans have established saving goals and

14 created savings plans using the America Saves

15 pledge.

16 Next, Katy Davis, managing director

17 for ideas42. Ms. Davis oversees portfolios in

18 financial health, charitable giving, and related

19 areas. Katy has more than 13 years of experience

20 working across the public and private sectors in

21 economic development, finance, and behavioral

22 design. At ideas42, Katy has led engagements in

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1 applying behavioral science insights for

2 financial institutions, fintech startups,

3 colleges and universities, housing providers and

4 asset building non-profits. Ms. Davis will

5 present topics addressing behavioral perspective,

6 closing the intentional gap, what deeper supports

7 look like, and what bid design means.

8 And finally Amelia Erwitt is managing

9 director of Cities for Financial Empowerment

10 Fund. Amelia provides leadership for CFE fund

11 staff in the development and execution programs,

12 technical assistance, research, policy and

13 communications. Ms. Erwitt will share findings

14 from research they have coordinated to uncover

15 what types of messaging can most effectively move

16 unbanked people to consider opening bank

17 accounts. 

18 So as you listen to these

19 presentations, we would encourage you to consider

20 what resonates with your own experiences, and

21 conversely to identify where something you are

22 hearing that's not consistent with what you may

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1 have thought before today. So we'll start with

2 Lindsay.

3 MS. FERGUSON: Okay. First of all, I

4 want to say thank you for letting us be a part of

5 today's meeting and share with you what we are

6 doing at America Saves. I just want to highlight

7 what I'm going to talk about. I'm going to share

8 a quick overview of America Saves, who we are,

9 what we do, share with you some of the

10 communication strategies that have been most

11 effective for us, what that looks like, what are

12 the interventions we're using. I'll talk to you

13 about how we're engaging with financial

14 institutions at a national level, and then I'll

15 wrap it up with showcasing what we're doing at

16 the local level through our local campaigns and

17 the financial institutions that they are working

18 with.

19 So America Saves, who we are, we're an

20 initiative of the Consumer Federation of America. 

21 We were founded on principles of social marketing

22 and behavioral economics, and our main mission is

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1 to motivate and encourage Americans to save. We

2 focus on those who are most vulnerable, and we

3 focus on creating savings accounts with at least

4 $500. 

5 We focus on two main audiences

6 throughout all of our campaigns, our initiatives:

7 savers, which are individuals or Americans who

8 are aspiring to save, they need a little bit of

9 help to save, or just seeking more information

10 about saving. And Emerson mentioned to date we

11 have encouraged over 710,000 Americans to create

12 a goal and a savings plan with the America Saves

13 pledge. Our second audience are what we call

14 partners, which are organizations that use the

15 plug and play resources, messaging tools, et

16 cetera, that we provide to help them promote

17 saving and taking financial action to a targeted

18 community or audience.

19 So the nucleus or the foundation of

20 our campaign and all of our initiatives is the

21 America Saves pledge. This is a simple savings

22 plan that allows somebody to make a commitment to

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1 saving, which we know is important when it comes

2 to behavior change. Write down a plan and create

3 a savings goal. And after that, we follow up

4 with them with ongoing, what we call saver

5 supports, which essentially are nudges and

6 communication strategies to help them accomplish

7 their savings goals and be more successful at

8 saving.

9 So I do have some statistics I wanted

10 to share with you from some recent surveys. We

11 did a saver survey in 2017 where we asked our

12 savers, those who have taken the pledge -- again,

13 when I use the term saver, it's the people who

14 have taken pledge. We've asked them how

15 regularly are they saving or putting away money

16 since after taking the pledge. And 60 percent of

17 them told us that they are saving and putting

18 away money on a regular basis versus before they

19 took the pledge. That was only 29 percent of

20 them.

21 We also recently used -- played around

22 with the statistic that, you know, the now famous

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1 $400 statistic and polled our savers through a

2 text message and asked them if they had enough --

3 $400 for an emergency. And 69 percent of them

4 said yes, and 31 percent of them said no. And

5 these, again, are the individuals that are

6 receiving what we call our saver supports. So

7 they're getting those ongoing messages about

8 saving, reminding them to save, giving them

9 information, et cetera.

10 And I just want to share with you all

11 that the top savings goals as of last week -- I

12 think I pulled them -- were emergency fund, debt

13 repayment, and vacation or a special event. So I

14 wanted to uplift here that these savings goals

15 kind of change, and -- but consistently we have

16 seen emergency fund and debt repayment be one of

17 the top savings goals for our campaign.

18 So the main interventions that we use

19 to encourage Americans to save and our savers

20 specifically to take financial action are email,

21 text, blog, and social media. We are

22 communicating with over 100,000 savers through

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1 email and about 50,000 through text message on a

2 weekly basis. Our blog is robust and we're

3 always directing them to more resources that we

4 have been creating in-house. And then for social

5 media, we have an expansive network of about

6 25,000 followers on Facebook, another 15,000 on

7 Twitter, and then our Instagram is growing since

8 we know that's a new hot social media platform.

9 So I put on the screen kind of what

10 we're sending through text message because I

11 think it shows a lot of the objectives I'll talk

12 about in a minute that we're using. So actually,

13 I'll talk about the objectives first. So we

14 really have made a conscious effort to have

15 objectives for all of our communication

16 strategies so that we're not just sharing out

17 information to share out information, right? We

18 know that we are being inundated with information

19 from all over the place. And America Saves

20 doesn't want to get lost.

21 So we made sure that all of our

22 communications are accomplishing one of the five

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1 objectives, and I left one off, so I'll have to

2 share with you guys what that is last. It is to

3 provide a value, so something that is a tip,

4 information, something of value for that

5 individual, provide support. Again, this is when

6 we check in with them on their savings goals. 

7 Have you made your savings deposit this month?

8 Response seeking, we want to have a

9 relationship with our savers, and we know that a

10 relationship you have to have engagement. So we

11 want to have them share something back with us,

12 whether it's that statistic I shared with you

13 earlier when we asked them: do you have $400 for

14 an emergency fund?

15 We obviously send them nudges or

16 reminders. It's the 15th of the month, some of

17 you may have been paid. Don't forget to transfer

18 money into your savings account. Then the one I

19 left off, which is I think one of the most

20 important ones is humor. We have started to

21 incorporate humor into our communication

22 strategies, again, to try to rise above some of

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1 the noise and be more -- have a personal approach

2 with our savers to let them know that we're

3 people, too. The people that are sending you

4 messages about savings has humor as well.

5 So the two yes and no kind of

6 statements here, those were the responses that

7 were automatically populated after we asked the

8 question: have you made your savings deposit this

9 month? And if they replied yes, we say, like

10 nicely done, save automatically with split

11 deposit to transfer money into your savings

12 account every paycheck, and then it links them to

13 a helpful resource from our website. If they say

14 no, we let them know there's still time. Here,

15 you know -- here again, is a way to split your

16 deposit.

17 And I think these are really, again,

18 humanizing what we do and telling people like

19 it's okay if you have not made that savings

20 deposit this month. And again, these messages are

21 going out on a weekly basis to all of our savers. 

22 And oh -- well, I'll go back really quickly

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1 because I wanted to lift up something here, too.

2 Some things that we have been really making a

3 conscious effort to do when it comes to

4 communications is removing any type of saver

5 chain.

6 So all of our communications are,

7 again, we're trying to make them more personal. 

8 We're removing the "skip the $5 latte" statement

9 as a part of our communication strategy, and

10 we're really making a conscious effort to meet

11 savers where they are, and we're introducing a

12 think like a saver mentality where saving is for

13 you, it's not for me, it's not for America Saves,

14 it's not for your bank, it's for you. So we are

15 truly making a conscious effort with our

16 communications to incorporate that language.

17 And then, of course, we're also

18 incorporating video, images, GIFs, and we are

19 shortening our messages because we all know that

20 the more information you can punch in a couple

21 sentences, the better, right? So that's kind of

22 our communication strategies, what's working, and

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1 what we're finding. And then I wanted to talk to

2 you guys about financial action, which in our

3 case means specifically working with financial

4 institutions.

5 So the theme of taking financial

6 action is one of the key messages throughout our

7 entire campaign. While we're wanting to motivate

8 and encourage Americans to save, we want them to

9 do something with that money. So we want them to

10 put it into a safe and secure account, set up

11 automatic deposits, set up split deposit. You

12 all know the drill. So that is a common theme

13 throughout all of our messaging, and we work with

14 financial institutions to provide safe and secure

15 vehicles for our savers to take financial action

16 throughout all of our America Saves programs.

17 So we do have programs that are

18 actually embedded into financial stability

19 programs through our Young Workers program which

20 targets youth employment programs. And then we

21 also have the Split to Save program which targets

22 employers. It provides them a platform and an

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1 avenue to share information about split deposit

2 with their employees. So a piece of all those

3 programs is working with the financial

4 institutions so that they have access to a safe

5 and secure account.

6 And then another way that we're

7 working with financial institutions on a national

8 level is obviously through America Saves Week. 

9 We have had America Saves Week since 2017, and

10 since then financial institutions have been a

11 large participating organization of the week.

12 Last year we had around 2,000

13 participating organizations, and of them over 400

14 were from a financial institution. And the

15 financial institutions are using the week to just

16 amplify the savings message to their communities,

17 and again amplify that they do have safe and

18 secure products that people can take advantage

19 of.

20 We also are highlighting those

21 financial institutions who we think are going

22 above and beyond to promote saving and provide

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1 safe and secure accounts through our Designation

2 of Savings Excellence program. So last year, we

3 awarded 18 financial institutions and you can see

4 some of the impact that they made here on this

5 screen. Over 246,000 people have deposited over

6 $500 million during America and Military Saves

7 Week 2019. And this is from that group of 18 who

8 we awarded the designation to. It's an

9 application process and then they also are --

10 their application is reviewed by a committee for

11 both banks and credit unions.

12 And then lastly, I just wanted to

13 highlight what we're doing at the local level

14 through our local campaign network. So America

15 Saves, we have almost 70 local campaigns across

16 the country who basically use the America Saves

17 branding, resources, tools, et cetera, to share

18 the message of saving to their specific

19 community. And with that, we provide a lot of

20 technical assistance. So we provide them their

21 own branding. We provide them a database. We

22 provide them a website, and then we also make

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1 sure that they are connected with a financial

2 institution, the more the merrier, but at least

3 one in their community.

4 And a great example of this is LA

5 Saves, which is actually a part of the AEI

6 network in Los Angeles. And it is coordinated by

7 a local non-profit, but they have over 100

8 organizations that support the campaign. They

9 have embed what they call team leaders, which are

10 volunteers from various financial institutions

11 and organizations that go in the community and

12 talk about saving and talk about financial --

13 taking financial action through various events

14 and outreach opportunities. So I wanted to make

15 sure that we gave them a little bit of credit

16 since they are part of the FDIC family. 

17 And that's kind of it. I just want to

18 make a selfish plug here that America Saves Week

19 is coming up in February 2020, and I hope that

20 you and all of everybody in the room can

21 participate with us.

22 MEMBER LEVERE: You have an extra day.

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1 MS. FERGUSON: I know. We do. We do.

2 So thank you.

3 MS. DAVIS: Good afternoon.

4 There's a great cartoon. I don't know

5 if any of you have seen it. It shows a boy

6 sitting in a classroom with his hand up, and he's

7 saying may I be excused, my brain is full. I'm

8 sure that's how many of us feel right now. So

9 I'll try to keep things as lively and

10 entertaining as Lindsay has.

11 So some of you may have seen this

12 image before. It's sort of a classic in the

13 behavioral economics world.

14 (Laughter.)

15 MS. DAVIS: I think it really captures

16 a couple of important things about humans. And

17 it really describes not other people that we're

18 designing for, but all of us. So these fellows

19 have made it all the way to the gym. They have

20 their towels in their hands, and yet at the last

21 second, they're taking the escalator instead of

22 the stairs.

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1 I, of course, packed my gym shoes

2 coming here to D.C. Did I take them out this

3 morning? No, I did not. So they actually made

4 it farther than I have. But this tells us how

5 humans have very good intentions when it comes to

6 things like healthy eating, exercising, financial

7 management, not texting while we're driving, but

8 that doesn't mean that we necessarily follow

9 through on these actions. And it's not something

10 where we want to say humans make mistakes or are

11 fundamentally flawed, but it's just the way our

12 brain is designed. It means that we react to our

13 context.

14 So consider if this staircase and

15 escalator had been placed in slightly different

16 places, you might actually see a very different

17 outcome here. And that's something we see from

18 social psychology over and over again is that as

19 humans, we react disproportionately to our

20 environment. Now, that's actually good news from

21 a design perspective because it means that the

22 way we design things, the way we craft messaging

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1 actually can have a lot of impact on how people

2 behave.

3 It's easier to imagine how people

4 might behave when we're designing tangible

5 objects because you can imagine how someone might

6 interact with them. It's absurd to imagine that

7 someone could use this fork and successfully lift

8 food to their mouth. It's absurd to think that

9 someone could use this teacup and teapot to

10 actually drink a steaming hot cup of tea. 

11 It's harder to imagine how people

12 might be interacting with financial products, and

13 that's often because they're sometimes

14 intangible, and often the way that people

15 interact with them is subconscious. Even they

16 might not be aware of exactly why and how they're

17 making decisions. So as designers, we should be

18 using what is known in behavioral science about

19 human behavior to be able to better predict how

20 people might interact with my product.

21 I'm sure this study is also familiar

22 to everyone in the room. So over the past few

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1 decades, there have been many explorations of

2 financial education as a way to increase

3 financial well-being. This meta-analysis back

4 from 2014 looked at over 200 studies of

5 traditional classroom-based workshops around

6 financial education and financial literacy. Now,

7 the general findings in that meta-analysis were

8 that, yes, these types of workshops can increase

9 people's knowledge in a measurable way.

10 Unfortunately, that doesn't mean that they

11 translate to a lasting change in behavior over

12 time.

13 And a more recent meta-analysis that

14 came out last December in 2018, again, reexamined

15 that set of studies, and they actually found two

16 examples of successful financial education

17 programs -- one with the U.S. Army, one a

18 Brazilian program conducted in high schools, and

19 found positive results from those studies. So I

20 think the takeaway here is not that information

21 doesn't matter. But why might some of these

22 programs not be successful?

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1 There are a couple of reasons. One is

2 perhaps the information that I'm receiving isn't

3 relevant to my life or isn't translated into

4 something that I can apply. Often they teach

5 abstract concepts like the value of compound

6 interest or saving over time, but they don't

7 translate that into something I can do today in

8 my own life. 

9 A second challenge is information

10 decay. As humans, we don't remember things

11 persistently throughout our lives. There's a

12 decay effect. So even if I learn something in an

13 eight-hour workshop, I may not remember it two

14 years later, especially if it's about a future

15 action that I might take.

16 (Laughter.)

17 MS. DAVIS: So knowing that, what are

18 some ways that we can prompt people not just to

19 understand, to learn, but also ideally take

20 action in the same moment to improve their

21 financial health?

22 And just to share a quick example from

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1 ideas42, we worked with Alliant Credit Union. 

2 it's a credit union based in Chicago but with a

3 national membership base where they rolled out a

4 new mobile remote deposit capture feature. We're

5 very excited about it. Sent out many

6 solicitations to their member base, and probably

7 not surprisingly to anyone in this room, most

8 people did not even try that new method of

9 depositing.

10 So our objective here was to think

11 about: how can we encourage members to try this

12 given that it's much more convenient to not go

13 into a branch or mail in your deposit and

14 continue using it over time? So we believe

15 strongly in not taking behavioral interventions

16 and throwing them into the field, but really

17 trying to understand the context.

18 So first we started looking at

19 existing data and we found that actually once

20 members used mobile deposit once, they were quite

21 likely to continue using it over time. So we saw

22 once a subset started depositing, they continued

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1 persistently, which meant that we don't have to

2 sustain this behavior over time. Maybe we can

3 just get people to try it once, and there will be

4 some persistent effect.

5 We also started talking to members and

6 asking them their perceptions of mobile deposit. 

7 At first, we thought that most of the answers we

8 would hear would be around things like trust or

9 data security or comfort with technology. But

10 instead, these were the kinds of answers we

11 started hearing. First was just that the app

12 wasn't top of mind. They remembered vaguely

13 there being some kind of mobile deposit thing,

14 but they couldn't recall, and they didn't think

15 about it at the moment when they actually might

16 use it.

17 So we designed a communication that

18 went out to members. It was personalized and

19 addressed to them. It highlighted some social

20 norms around other Americans using mobile

21 deposit, and it was signed by a real person from

22 Alliant Credit Union, so it felt personalized. 

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1 We wanted to send it in a large envelope, knowing

2 that often what distinguishes something in the

3 mail can be the size and the appearance of the

4 envelope, and that good things sometimes come in

5 large envelopes like college acceptance packages.

6 Unfortunately, that was cost prohibitive in the

7 real world, so instead we put some messaging just

8 on the outside of the check highlighting what was

9 inside.

10 And the thing inside was actually a $5

11 starter check for people to try out mobile check

12 deposit. This was based on hearing from people

13 that, yes, they were interested in mobile

14 deposit, but when it came time to get that paper

15 check deposited, it was just easier to do what

16 they'd always done. This is status quo bias,

17 right? Even the thought of switching to another

18 method and the uncertainty and effort involved

19 can be prohibitive. So we put a starter check

20 inside the package, and we had very clear step by

21 step instructions of exactly what to do.

22 A third thing we found from members

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1 was that they had tried the starter check --

2 sorry, they had tried the mobile deposit feature,

3 but it didn't work. And often these were

4 preventable things like the person putting it

5 down on too light of a surface for the check to

6 be captured. So we actually created a little

7 place mat where they could put the check right in

8 between the window and follow the instructions,

9 and they would already be able to see what

10 correct and incorrect capture would look like.

11 So this fun kit went out to members. 

12 We randomly assigned members into a treatment

13 group that got this package as well as a control

14 group who got just standard messaging about the

15 value of mobile deposit generally. So this chart

16 shows mobile deposits per thousand members. It

17 does not include the starter check itself. So

18 you can see here where there was an initial

19 excitement effect after people got the starter

20 check, and it definitely dipped over time. You

21 can see there's a persistent increase in usage of

22 mobile deposit between the treatment and the

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1 control group.

2 This ended up translating into

3 doubling adoption rates. It was about a 40

4 percent increase in mobile deposits relative to

5 other methods. And interestingly for Alliant, it

6 actually increased the total number of checks

7 deposited, not just deposited from mobile,

8 suggesting that it didn't just switch people in

9 terms of what channel they were using, but it

10 actually ended up bringing in more deposits,

11 which ended up making this more economically

12 sustainable for the credit union given that a $5

13 starter check for every member can be a little

14 costly.

15 So that's a fun example where we have

16 a group of people who are maybe likely to be

17 using a feature but simply aren't following

18 through on that action. That's what we call the

19 intention action cap. But often when it comes to

20 financial health, there can be more complex

21 situations to tackle such as savings and debt

22 reduction, many of which Lindsay has already

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1 talked about.

2 So I wanted to highlight one other

3 example which is a partnership with multiple

4 credit unions in the Pacific Northwest around a

5 program called the Financial Health Check. And

6 the basis for that financial health check was the

7 recognition that wealthy people have access to

8 very deep supports via wealth advisors, and not

9 everyone else does. And oftentimes, those

10 supports can be very specific, such as debt

11 counseling, but may not cover everyday things

12 like savings and basic debt reduction.

13 So the financial health check was

14 created as a moment for members to schedule time

15 with their credit union to make decisions and

16 take actions just around setting aside savings

17 for making reductions in their debt. We tested

18 in-person vision -- in-person sessions in an

19 earlier pilot and then tested a 30-minute phone

20 call in a second pilot.

21 We had live support from a human, and

22 I think importantly, that human could actually

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1 mirror the member experience to see what they

2 were doing in online banking and help them

3 through those action steps.

4 They were able to, similar to a

5 financial coaching session, identify some

6 personal goals, labeling them in a really vivid

7 way, hopefully setting up a labeled savings

8 account for that goal, and then making a concrete

9 plan about how much of their cash flows could be

10 put aside on a monthly or weekly basis to reach

11 that goal.

12 And then they got support in real time

13 to set up an automated transfer or set up some

14 sort of reminder for the future for themselves,

15 knowing that members who are in more complex

16 situations would be routed to debt counseling or

17 a debt consolidation agency.

18 So the financial health check has

19 shown positive results on savings. We did a

20 randomized control trial and found positive

21 impact on the number of savings deposits. We saw

22 a positive impact on total dollars deposited,

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1 although that was weaker in terms of statistical

2 significance.

3 We actually didn't see any impact on

4 debt probably because a large percentage of

5 members did not take debt actions and the ones

6 who did were often referred to outside agencies

7 to handle those more complex situations.

8 But I think we're in a moment now

9 where we do see this positive effect but we

10 really want to know is this impacting the people

11 who could benefit from it the most who may not

12 have 30 minutes to spend on the phone with

13 someone or an hour to come in in-person.

14 So we're doing some work now to think

15 about more scalable solutions and we're doing

16 some testing with an academic at University of

17 Washington who specializes in study of emotional

18 stigma and shame and thinking about what kind of

19 language makes someone more likely to engage.

20 So we're running some tests around

21 normalizing language to help members who might be

22 in financial distress and feel like it's not

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1 stigmatized, it's normal, many Americans

2 experience this and hopefully make them more

3 likely to engage.

4 And here are my caveats at the end of

5 these two examples. In behavioral science we

6 find that there really aren't one size fits all

7 solutions.

8 There are themes, there are common

9 trends, but often people make the mistake of

10 taking a behavioral tool that worked in one

11 context and just blanketly applying it to other

12 contexts.

13 Here's where we really want to

14 emphasize the point of designing with communities

15 as opposed to four communities and really

16 listening to the barriers that people perceive in

17 their lives.

18 And I think a lot of those barriers,

19 some of them can be overcome with smart

20 behavioral design or messaging, some of them are

21 much deeper and core to the way products are

22 structured. And there's where I see a lot of

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1 potential to change the way products are

2 delivered on the way that would have outsized

3 impact on outcomes.

4 So first we hear from a lot of

5 families about liquidity barriers, about being

6 concerned am I going to actually be able to

7 access my money when I need it when I'm managing

8 a very tight budget and tight cash flows.

9 This is a quote about someone not

10 having a bank account because they save their

11 money below the mattress. If there's an

12 emergency, the money is right there when you need

13 it.

14 And here, I think there are inroads to

15 be made around settlement speed of transactions

16 so that people can get their money quickly when

17 they need it in real time as well as what Andrea

18 referenced earlier about now, soon, and later,

19 about easing restrictions on accessing funds

20 from, for example, a standard savings account

21 where you might not be able to make more than six

22 withdrawals in a month. That alone can be a

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1 deterrent to someone having an interest-bearing

2 savings account.

3 Secondly, around trust barriers, we

4 all know that many disenfranchised communities

5 rightfully have lack of trust in the financial

6 mainstream in wondering whether that money is

7 really going to where someone says it is.

8 We recently did some primary research

9 and talked with families who actually go together

10 in a group to the bank because of concerns around

11 those trust and not wanting to engage with people

12 who work in that bank as well as documentation

13 issues and being concerned about asking for

14 identification while they're in there.

15 And that gets to some of the

16 structural barriers. We're all aware of the very

17 real risks around documentation for people when

18 it comes to accessing these financial

19 institutions and we hear these stories over and

20 over again around even if there aren't actual

21 barriers there, the perception of barriers that

22 prevent people from engaging.

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1 Finally, I wish I could have spent all

2 day today talking about scarcity. For those of

3 you who are familiar with the scarcity research,

4 it's really about how when someone is in a state

5 with limited financial resources our mind

6 naturally tunnels on that resource which is

7 missing, which means that we can be so trapped in

8 day to day financial management that we don't

9 have a sense of being able to breathe and step

10 back and plan for the future.

11 So knowing this context of scarcity,

12 I think, gives a slightly different lens to

13 behavioral design around both cutting costs of

14 participating in programs or cutting perceived

15 costs of participating as well as ways to really

16 create slack in people's lives whether that's by

17 helping them accumulate a financial cushion,

18 reduce debt, or additional income supports or

19 income subsidies that might benefit them.

20 I'll pause there and pass it over to

21 Amelia.

22 MS. ERWITT: Thank you and thanks to

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1 everyone for remaining awake. I have the much

2 coveted position of the very last presentation at

3 the end of a long day, so I appreciate your

4 patience for that and I'll try to speed up a

5 little bit. I know we're low on time and I'm

6 sure you all have questions as well.

7 I'm Amelia Erwitt with the Cities for

8 Financial Empowerment Fund. I work with

9 Jonathan. We work with mayors and other local

10 leaders and their local partners to build

11 resident financial stability into the fabric of

12 local government.

13 And one of our key programs is the

14 Bank On initiative which I know many of you know

15 very well, so I won't go into too much detail on

16 what that is, but we'll just quickly kind of go

17 through currently today what the Bank On equation

18 looks like for us.

19 Really the CFE Fund is working to

20 negotiate safe and affordable banking accounts. 

21 We currently have 36 accounts available in 22,000

22 branches in all 50 states and Washington, D.C.

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1 As referenced earlier, many of you

2 around the table have been early partners in that

3 work.

4 We're working programmatically to

5 connect people into those accounts and then

6 supporting the almost 90 coalitions across the

7 country that make those programmatic connections

8 possible.

9 The programmatic connection points

10 come in a number of different ways as we've

11 discussed sort of earlier today including things

12 like our summer youth employment programs,

13 workforce development programs, one on one

14 financial counseling and coaching programs, et

15 cetera.

16 And as we're working with our partners

17 to make these integrations possible, we want to

18 be making sure that we're also informing the

19 interactions with the on the ground staff we're

20 having with unbanked folks in a research-based

21 way that we're really communicating in the best

22 way possible.

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1 So the key goal with the research that

2 I am sharing with you this afternoon was to build

3 a more complete understanding of the financial

4 attitudes and goals of unbanked people and to

5 explore the most effective messaging themes that

6 could drive account adoption.

7 We're looking to, again, inform

8 messaging that Bank On coalitions, financial

9 counselors and other front line stakeholders can

10 use to convince people of the benefits of banking

11 and the benefits of Bank On-certified products.

12 So just quickly on the methodology, as

13 always, our starting point with all of this work

14 is with the FDIC and the National Survey of

15 Unbanked and Underbanked Households.

16 We worked closely with the FDIC,

17 particularly with Keith and his team in the early

18 days of this work. By looking at segmenting the

19 unbanked and specifically what segment of

20 unbanked people were unbanked for reasons that

21 could be moved by a communication strategy.

22 So in other words, we focused on

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1 people with a motivational barrier for opening an

2 account and eliminated from our research anyone

3 who said that they have a structural barrier for

4 remaining out of the system like being on

5 ChexSystems.

6 We conducted focus groups in both

7 English and Spanish in four cities across the

8 U.S. We did some in depth inter -- phone

9 interviews with rural unbanked people and then

10 two different rounds of surveys, national digital

11 surveys, oversampling for Spanish speakers and,

12 again, sort of pegging that research sample to

13 the FDIC research sample as well.

14 And all of that was really got us to

15 these key messaging things that are up on the

16 screen that we really dug into in the second

17 survey, tools for easy banking, maintaining

18 control, rethinking the relationship and helping

19 to achieve goals, and I'll get into that in a

20 minute.

21 I have just one note as I go through

22 this, across the focus groups and the surveys,

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1 the respondents had very little familiarity with

2 the credit unions and there was no real

3 noticeable difference between perceptions about

4 credit unions.

5 And so we worded the research

6 questions to be specifically around banks.

7 Okay. Now to jump to our high-level

8 findings. And you obviously have the slides in

9 your packets. You also have I should say the

10 draft research brief behind my presentation with

11 more detail. It's not been published yet, so

12 your eyes are the first ones on this research, so

13 please be kind.

14 So the first finding that we found was

15 that messaging can really change minds. Unbanked

16 people in both the focus groups and the phone

17 interviews -- well, and the surveys, they all had

18 very negative feelings about banks starting out,

19 which is not surprising.

20 However, across both the English and

21 Spanish speaking audiences, almost half of those

22 surveyed were open to changing their minds about

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1 banks through messaging, and this was especially

2 true for people who had previously had and then

3 closed a bank account. This was really

4 surprising for us.

5 We had kind of designated that group. 

6 We were calling them burned by a bank internally

7 for a while thinking those would be the hardest

8 to get back into the system if they had had a

9 negative experience in the past. But much to our

10 surprise, they were actually among the most

11 interested in rejoining the banking system once

12 they heard about some of the changes in the

13 certified products.

14 Just to orient you to these graphics

15 a little bit and on the left you'll see -- on the

16 left of each of these squares, you'll see where

17 each of the groups started in terms of the three

18 factors up there: favorability towards banks,

19 interest in learning about banks, and intent to

20 get a bank account.

21 And on the right is there they ended

22 after being exposed to our messages. So the

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1 overall trend was positive across all of the

2 categories and the little blue arrows indicate

3 where the gains were statistically significant.

4 Overall, favorability towards banks

5 increased -- increased, rather, by a

6 statistically significant 17 percent after

7 hearing the messages. And intent to get a bank

8 account increased 14 percent overall.

9 And again, this was even more true for

10 the Spanish speakers, those who are younger and

11 surprisingly those who had had an account in the

12 past.

13 So a couple more high-level findings. 

14 The successful messages emphasize kind of two

15 basic topics. One are short-term goals and the

16 other being keeping money secure.

17 And we sort of structured the report,

18 as you'll see as sort of a myth-busting

19 structure. But really looking at this first myth

20 on this slide, the under -- you know, the

21 original thinking that the best way to convince

22 unbanked people about the importance of account

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1 is sort of looking again to the later savings,

2 the home ownership, the starting a new business

3 thing, which, you know, many coalitions have been

4 messaging that way.

5 In reality, although of course people

6 do connect, having those bigger assets with also

7 having a banking relationship, that wasn't

8 motivating right now. And so really needing to

9 focus on the right now, what are goals that you

10 have today, building, savings, getting out of

11 debt, improving your credit score, and how can we

12 be convincing that opening a bank account is a

13 good early step towards those shorter-term goals.

14 The second myth that we hear pretty

15 often is that those out of the system can't be

16 well-served by the traditional system and,

17 therefore, may be better served by technology, by

18 apps, by Fintech.

19 What we really found were that the

20 specific tangible features really well associated

21 with mainstream banks were the things that

22 resonated most with the people surveyed, things

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1 like fraud protection, having a debit card, and

2 access to direct deposit, these were the bread

3 and butter issues that people were most

4 interested in. And so that was sort of

5 interesting to learn.

6 So how we figured out what resonated,

7 this is another complicated and, again, I

8 emphasize draft, a draft, it's not very pretty. 

9 We will make it look nicer when we actually

10 publish this.

11 But just to kind of give you a sense,

12 the message themes that were tested and sort of

13 where they fell -- so just to, again, to orient

14 you to this graph, the Y axis shows the average

15 message score in terms of believability and

16 favorability. And then the X axis shows how

17 strongly the message is correlated with actually

18 changing your mind about opening an account.

19 And so the red box in the bottom left

20 corner, these are messages that both lack

21 developments and credibility, and they also

22 didn't motivate. You know, even if they were

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1 true, they wouldn't motivate anybody to open an

2 account.

3 This is very interesting to see how

4 lowly -- how poorly low cost scored on both of

5 these metrics. It was neither believable nor

6 motivating.

7 (Laughter.)

8 PARTICIPANT: In that order.

9 (Laughter.)

10 MS. ERWITT: The green box on the top

11 left, those messages were believable and

12 important but they didn't motivate action.

13 The yellow box, they motivated action

14 if they were -- if you could believe it, but we

15 really see this as sort of an opportunity area if

16 we can really work on the credibility of things

17 like banks are for everyone, which again echoes a

18 theme that many Bank Ons have used for many

19 years. It's sort of highly motivating but not

20 believable.

21 And then the top right blue box, these

22 were the most credible and relevant to the survey

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1 respondents and they were motivating to bank

2 account adoption. And so these are the key

3 messages that were really focused on leveraging.

4 The category that you can see in the

5 small text is sort of what they literally were

6 and what the messages tested were. But the kind

7 of overall categories that we've pulled from

8 those messages were around those three that I

9 mentioned earlier. Those were easy banking,

10 maintaining control and helping to achieve goals.

11 So then we -- so we're going to dig

12 into one of those examples. This is the

13 maintaining control example. And we've got this

14 for each of the themes in the report and more

15 background and detail. But in the interest of

16 time, I'm just going to dig into the theme around

17 maintaining control.

18 So in addition to rating the messages

19 in the way that I just showed you around

20 believability and motivation, we also look very

21 closely at the actual words that coalitions could

22 be using to resonate with individuals.

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1 So respondents were asked to highlight

2 the words they found most compelling in each of

3 these messages. So the bigger words are the ones

4 that were highlighted most frequently.

5 so maintaining control, you know,

6 scored well because losing control of money

7 through things like fraud and overdraft fees was

8 a barrier that came up over and over again in

9 both the focus group and the survey work we did.

10 The fraud messages were particularly

11 appealing to Spanish speakers, and the overdraft

12 messages were more resonant with the English

13 speakers.

14 So then finally just in sort of

15 summary, the winning themes, again, that we

16 pulled out, tools for easy banking, really

17 reinforcing how direct deposit and other tools to

18 manage and check money can make banking easy and

19 stress free were very compelling.

20 Maintaining control, explicitly

21 highlighting the no ability to overdraft

22 whatsoever and fraud protection really addressed

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1 the central concern among unbanked people about

2 being hit with unexpected fees and having their

3 balances threatened.

4 And then highlighting achievable

5 short-term goals, the idea of taking short-term

6 steps like savings and paying down debt rather

7 than focusing on bigger picture, further away

8 stuff.

9 Just a couple additional

10 considerations that weren't too surprising, but

11 keeping a positive tone is really important,

12 focusing more on how a bank account can help you

13 achieve your goals rather than using more things

14 like fear or urgency to say you're behind, you've

15 got to act now to kind of catch up.

16 Again, the short-term goals really

17 focusing on the specific and tangible account

18 features that are available, debit card, direct

19 deposit, that type of thing and being explicit on

20 cost.

21 Hidden fees are really top of mind for

22 people. So having a very transparent cost

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1 structure that -- and being clear on that, that

2 is important.

3 And then finally, the most persuadable

4 groups, those who are -- were previously banked,

5 Spanish speakers and the slightly younger

6 demographic.

7 So as I mentioned, the research is

8 still unpublished. We welcome your feedback and

9 questions as we continue to refine what we put

10 other there.

11 In addition to finalizing this report,

12 we're also going to be making a number of tools,

13 talking points, and things for front-line staff

14 to actually be implementing some of these

15 findings out in the field.

16 So any reflections or thoughts you

17 have on that, we -- you're welcome to.

18 MR. HALL: So we're open to any

19 questions that you have.

20 Maurice?

21 MEMBER JONES: On the America Saves

22 piece, how young do you target? Is your age

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1 range -- 

2 MS. FERGUSON: Yeah. So we -- America

3 Saves, the campaign, the parent campaign is

4 targeted for adults 18 and above, but we do have

5 America Saves for young workers, which targets

6 the 16 to 24 group with the custom messages for

7 that specific group.

8 So we're still kind of using the same

9 objectives, but we're changing them so that they

10 resonate with that population.

11 MR. HALL: A follow-up, so your data

12 top savings goes -- 

13 MS. FERGUSON: That's across all of

14 our campaigns.

15 MR. HALL: Including the young -- 

16 MS. FERGUSON: Including the young

17 workers.

18 MR. HALL: You know, this is

19 interesting education doesn't show -- 

20 MS. FERGUSON: But -- 

21 MR. HALL: -- theme, you know -- 

22 MS. FERGUSON: Yeah.

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1 MR. HALL: -- so I'm just trying to -- 

2 MS. FERGUSON: And when you pull out

3 the young workers data alone, education kind of

4 comes and goes as one of the top savings goals.

5 We see consumer product as a top

6 saving goal for young adults, which is fine. I

7 mean, we want them to have a savings plan for

8 whatever it is that their heart desires. We just

9 want them to have a plan and a goal.

10 MR. HALL: Yep. Don.

11 MEMBER GRAVES: Just a few quick

12 questions or comments.

13 Lindsay, thanks for -- well, thank you

14 all for your presentations. They were great, as

15 were all the others today.

16 My understanding is that America --

17 the America Saves designation you get that by the

18 change in deposits or -- amount of savings during

19 the America Saves Week. Is that -- am I correct

20 on that or no?

21 MS. FERGUSON: Yeah. It's an

22 application process where the financial

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1 institution reports on their activity during

2 America Saves Week.

3 MEMBER GRAVES: I don't know, for

4 those banks who have a very robust program that

5 is focused on increasing the savings, we have a

6 big financial wellness program, a platform when

7 we -- especially after we acquired HelloWallet.

8 For us, that is a yearly thing. It

9 doesn't coincide with that one week. So I would

10 just suggest that there are a number of banks,

11 some of whom are in the room today that might not

12 -- it might not be the best way to incentivize us

13 to be a part because that one week for us is --

14 well, it's an important week, obviously, because

15 of tax season.

16 It's not as -- much more important

17 than any of the other weeks. So just -- 

18 MS. FERGUSON: No, good feedback.

19 MEMBER GRAVES: Katy, thank you for

20 your presentation. You don't happen to know the

21 demographic breakdown of the 90 percent of

22 non-mobile depositors, the folks in that first -- 

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1 MS. DAVIS: We don't. So we did not

2 have reliable data on age, race, or ethnicity, or

3 income level, actually.

4 MEMBER GRAVES: And you don't -- do

5 you have any information on the types of mobile

6 devices or phones that they were using, that sort

7 of thing, because I suspect -- I don't know for

8 certain, but I suspect that there were issues

9 around -- and certainly age and income level as

10 well as the types of mobile devices that they

11 might have been using and the ability to use

12 those effectively for. But that's just my

13 assumption -- presumption.

14 MS. DAVIS: I would not be surprised. 

15 I would say in other studies that we've done

16 around forms of digital engagement in the

17 developing world, we've actually found very

18 surprising and counterintuitive trends around age

19 that don't match our often beliefs about older

20 people not using technology.

21 (Off mic comments.) 

22 (Laughter.)

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1 MS. DAVIS: But in this study, we

2 couldn't look at that. In the -- 

3 MEMBER GRAVES: Who's talking about

4 young people?

5 (Laughter.)

6 MS. DAVIS: In the Alliant study, with

7 the data that we had available, the only

8 predictor of whether someone used mobile deposit

9 was whether they had downloaded the app to begin

10 with on their phone, sort of the only helpful

11 predictor.

12 MEMBER GRAVES: That's great.

13 And then Amelia, when does this become

14 public?

15 MS. ERWITT: I think very soon. We

16 actually have, at the end of this month, a

17 grantee convening where we're going to be

18 distributing some of the tools I mentioned, the

19 practitioner tools. So as soon as we get any

20 final thoughts from you all, I think we're going

21 to print on this. So it'll be out soon.

22 MEMBER GRAVES: Thank you.

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1 MR. HALL: Yes, Wade.

2 MEMBER HENDERSON: Thank you.

3 This question is for Lindsay, but I

4 welcome the input of anyone, and it's about

5 childhood savings accounts.

6 A few years ago there seemed to be a

7 sort of growing sense that investments in

8 childhood savings accounts for newborns, an

9 investment of perhaps as much as $500 held in

10 trust until the newborn reached majority age, 21,

11 18, whatever, and restricting the use of that

12 money for some, you know, laudable goal,

13 education, a house, some other, you know,

14 long-term goal was one way of creating wealth and

15 helping to limit the wealth gap among the poorest

16 of the poor.

17 I'm not sure whether that's fallen

18 into disfavor or not, but I haven't heard nearly

19 as much clamor about it as there was a few years

20 ago when there was talk that I think England was

21 thinking about making these investments in other

22 communities.

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1 Obviously the population of people

2 that would be targeted in this country are far

3 too poor to offer that resource on their own. 

4 And the temptation to use it over the life of the

5 child is obviously very great, so there would

6 have to be some limitation held in trust of some

7 kind for purpose might be there.

8 Is there any research that your

9 organization has developed on this issue? Is

10 there any deeper analysis that you can offer

11 about the efficacy of these childhood savings

12 accounts? And where do you think that might

13 stand currently in the public debate around

14 policy?

15 MS. FERGUSON: Yeah. And I'm going to

16 punt this to any of my panelists because we

17 currently have not done research on child savings

18 accounts specifically, but I know that work has

19 been researched.

20 Prosperity Now has done a lot of it. 

21 Some of our municipalities are doing the child

22 savings accounts, so I'm going to punt the answer

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1 over to my colleagues.

2 MS. ERWITT: I think you should ask

3 your fellow committee members in --

4 MEMBER HENDERSON: I think I should.

5 (Laughter.)

6 MS. ERWITT: There are some experts

7 around the table here on your side. Yeah.

8 MS. DAVIS: I would add a quick

9 thought.

10 MEMBER HENDERSON: Yeah.

11 MS. DAVIS: I think to take Jose's

12 words away from him, there seem to be two

13 positive models of college savings accounts, one

14 which is an account at birth with a specified

15 amount of money -- 

16 MEMBER HENDERSON: Yeah.

17 MS. DAVIS: -- one which is in a

18 demand deposit account but with more continual

19 engagement over time because the family can

20 contribute. Both have been found to have

21 positive impact on college-going in some way.

22 So I think those are two different

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1 models that have some evidence behind them even

2 if it's not a randomized control trial.

3 And then I would add an example from

4 actually the cash transfer world. You've done a

5 lot of work with cash transfer programs

6 internationally where the traditional model was

7 making them conditional upon a specific use.

8 And we've actually tried different

9 ways of delivering them where the cash is

10 unconditional but it's labeled for a specific

11 purpose, for example, education or health.

12 And we find that just by labeling it

13 and framing it for that purpose, it has just as

14 much positive impact as having it actually

15 designated and restricted for that purpose.

16 I think there could be an in between

17 solution there where you still have some

18 flexibility around how to use it, but it also

19 achieves its original goal.

20 MEMBER HENDERSON: That's very

21 helpful. And so let me do one follow-up. 

22 Jonathan, this is sort of directed at you. And

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1 since I'm leaving, I feel free to throw new

2 assignments around to all my colleagues who are

3 here.

4 Look, I would think that the FDIC, if

5 there were any effort to examine this issue of

6 the efficacy of child savings accounts, I know

7 that the imprimatur of this agency obviously

8 carries a lot of weight.

9 In communities where they are

10 struggling to get a toehold on a build in wealth

11 providing resources for education and training

12 going forward, a study of the FDIC suggesting

13 that this may be a model that community

14 foundations or other philanthropies might look to

15 as a way of helping to build wealth among the

16 poorest of the poor would carry greater weight

17 than just the casual conversation among, you

18 know, people who sort of dabble in this area.

19 And I would think that given the

20 importance of this issue and the widening wealth

21 gap that this might be something that you would

22 take a look at. It also has implications for the

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1 unbanked and the under-banked.

2 So I'll leave it there as to whether

3 there could be a follow-up study that would show

4 some efficacy in this area. It would be helpful

5 if the FDIC would look at it.

6 MEMBER LEVERE: I don't know if -- I

7 mean, there are many researchers who are doing in

8 depth analysis of this. Willie Elliott, the

9 University of Michigan, has done extraordinary

10 research, Michael Sheridan, we have been lucky

11 from the very first day we worked on children

12 savings accounts that we have had a broad range

13 of academic researchers taking this every step of

14 the way.

15 And I just say live by Jose's

16 groundbreaking program. Last year we had more

17 children savings accounts opened than in any time

18 in the history of this initiative and we have

19 Cory Booker with a major proposal that many

20 people worked on explicitly to address the racial

21 wealth divide called baby bonds.

22 So Wade, I just want to say that

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1 things are very healthy --

2 MEMBER HENDERSON: Good.

3 MEMBER LEVERE: -- despite some of the

4 barriers we're facing.

5 MEMBER HENDERSON: Yeah, yeah, yeah.

6 MEMBER LEVERE: But there's -- I have

7 to say that this is one of the areas where we

8 started paired with researchers, so we have

9 incredible data, not just on the financial

10 impacts but the aspirational changes, the child

11 save -- the college saving mentality that happens

12 with households.

13 I'll say one other thing before Jose

14 jumps in. When we did the first experimental

15 site with the children's savings demonstration,

16 we had five Head Start centers right outside

17 Detroit.

18 We found that the majority of those

19 parents had given up on their child going to

20 college by age 3, and they had given up because

21 they had exactly the wrong information on what

22 the net cost of college would be for their

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1 household. And if they went to community college

2 or public college, they knew what it would cost

3 to go to Harvard, and so they thought I can't

4 disappoint my child, I could never pay for that.

5 Subsequently, we recruited Martha

6 Kanter, the former -- yeah, well, you were at our

7 wedding together. And she said every step of the

8 way, middle school, people had the wrong idea. 

9 This is something they track all throughout

10 education. So just to say there's a lot, but

11 thank you for being a booster.

12 MEMBER HENDERSON: Yeah, yeah.

13 MEMBER CISNEROS: I'll echo the thank

14 you, the gratitude, Wade. There is a lot going

15 on. There's a number of cities that have

16 launched programs very similar to San

17 Francisco's.

18 There's a number of states now. There

19 are a growing number of states and I'm happy to

20 add the State of California with our new governor

21 who has dedicated $50 million -- 

22 MEMBER HENDERSON: Wow.

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1 MEMBER CISNEROS: -- of this year's

2 state budget to be split amongst two different

3 models of children savings account programs.

4 But that all being said, I very much

5 second your request to the FDIC -- 

6 MEMBER LEVERE: Yes.

7 MEMBER CISNEROS: -- to get involved

8 in this -- 

9 MEMBER HENDERSON: Yeah, yeah, yeah.

10 MEMBER CISNEROS: -- effort because

11 with all these different efforts going on and

12 growing and many of them are getting set up in

13 slightly different ways or dramatically different

14 ways, we really could use more research, more

15 knowledge and more cold hard facts to help guide

16 us as we want to do more and more good things to

17 make sure we're doing things that are going to

18 make a difference.

19 MEMBER HENDERSON: I'm really excited

20 to hear both of you talk about this. It's

21 obviously something that's grabbed the

22 imagination of many who are trying to think about

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1 how you cut through that wealth gap divide and

2 the, you know, racial wealth gap divide in

3 particular and trying to aggregate that data,

4 presenting it in a coherent way.

5 Giving that the implementer of the

6 FDIC could carry some real weight and policy

7 circles around the country, so I hope that when I

8 come back as a visitor, perhaps this will be -- 

9 MEMBER LEVERE: But you're -- 

10 (Laughter.)

11 MEMBER LEVERE: -- right, Wade?

12 MEMBER HENDERSON: Yes. Thank you so

13 much. I appreciate it.

14 MR. HALL: I think what I heard either

15 Andrea or Jose say before is that in some of the

16 research -- I'm not real sure, but I heard

17 someone say this is that a child is more likely

18 to go to college -- seven times more likely to go

19 to college if they have anything in savings.

20 It doesn't matter if it's $100, if

21 it's $500. It's certainly something that it

22 encourages, it gives hope to specifically

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1 children of color that would not think to have

2 the ability of -- or the parents think they

3 wouldn't have the ability, the financial

4 capability to send them to school.

5 So whether it's true or not, whether

6 I heard that from one of you or not, I did hear

7 it, and I think it is -- it's hopeful, it's

8 beneficial.

9 Jose, I was talking about seven times

10 a kid more likely -- 

11 MEMBER CISNEROS: Seven times more

12 likely to go to college if their families engage

13 with the account, which builds -- making deposits

14 in account -- in an account on an ongoing basis

15 builds an identity, a savers identity, I'm a

16 person who saves.

17 And if that account has the name

18 college savings account and the name of the child

19 on the account, then it -- that saver's identity

20 gets translated to a college going identity. I

21 am a -- because I save for college, I am a person

22 who is going to college.

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1 MEMBER LEVERE: Can I ask one other

2 thing?

3 MR. HALL: Sure.

4 MEMBER LEVERE: One of the most

5 fascinating debates we've all had -- and this is

6 where we need numbers -- is every time they do

7 the unbanked and the under-banked when you ask

8 people the reason why they are unbanked, the most

9 common answer is I don't have enough money. And

10 that has always frustrated all of us because we

11 knew there is something behind it.

12 I'd love our three panelists to then

13 say how do we get deeper than that answer.

14 MR. HALL: A good question.

15 MS. FERGUSON: Well, I can start us

16 off. And since we are a social marketing

17 campaign and just use a lot of messaging, what we

18 tend to say is saving is a habit, not a number

19 and encouraging people that it doesn't -- we've

20 all heard this, but reiterating that message $5 -

21 - savings is not a number. It's a habit. It's

22 an identity.

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1 So in regards to what we're doing to

2 kind of tackle that challenge is we're really

3 focusing on those types of messages when we talk

4 about saving and taking financial action with our

5 savers.

6 It's not the answer to everything, but

7 that's kind of what we're doing.

8 MS. ERWITT: I would just add that, so

9 we heard that a lot, again, reiterated in our

10 focus groups, for sure and in the survey as well,

11 and I think it's -- you know, it's really kind of

12 fed into some of the messages that we developed.

13 And in the report you'll see literally

14 spelled out all of the messages that we tested,

15 and some of them got to a bit of that.

16 But really, it -- for us, it drove

17 home the point about emphasizing the short-term

18 goals. Not having enough money, you know, maybe

19 one day I'll have enough money, I'll buy a -- you

20 know, I'll think about purchasing a home and

21 having these sort of longer-term aspirational

22 things, and that's the thing that they connect

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1 with a bank account.

2 And so trying to figure out a way that

3 we could be making a better case for how

4 short-term goals could also be achieved with

5 entering the system and that taking a first step

6 of getting an account leads to short-term

7 achievements rather than being so connected with

8 like one day I'll be there and then I'll consider

9 it.

10 So you know, I think it's more about

11 the whole package. It's not -- you know, we've

12 also been frustrated by, you know, what does it

13 mean I don't have enough money. We know how low

14 -- 

15 MEMBER LEVERE: Right.

16 MS. ERWITT: -- these, you know,

17 balances can be in a formal account. 

18 And so I think being very clear on

19 cost structure, being transparent on what that

20 means, and then, again, connecting starting with

21 a bank account is the first step in a series of

22 short-term goals is something that we can do a

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1 better job of communicating.

2 MR. HALL: Thank you. Because of

3 time, we're going to probably cut it off. All

4 right. We'll turn it over to Jonathan. Thank

5 you.

6 DIRECTOR GRUENBERG: And I've always

7 thought, you know, the second most frequent

8 response to the question why don't you have a

9 bank account is the fees and minimum balances

10 associated with an account. I've always felt

11 there was a close connection between the first --

12 MEMBER LEVERE: Yeah.

13 DIRECTOR GRUENBERG: -- answer and the

14 second.

15 MEMBER LEVERE: Yeah.

16 DIRECTOR GRUENBERG: They don't think

17 they have enough money to make it worthwhile

18 because they're concerned that the cost of the

19 account is going to actually outweigh the -- and

20 in a sense trying to deal with that always seemed

21 to me to be threshold.

22 MEMBER MINTZ: And the just saying low

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1 cost -- 

2 DIRECTOR GRUENBERG: And the safe

3 account -- 

4 MEMBER MINTZ: -- doesn't ring true.

5 MEMBER LEVERE: Doesn't do it.

6 MEMBER CISNEROS: Yeah.

7 MEMBER MINTZ: Right?

8 MEMBER CISNEROS: Yeah.

9 MEMBER MINTZ: You have to be more

10 explicit.

11 MR. MILLER: So thank you very much to

12 the panel. I'll just observe -- that we spent a

13 lot of time in this committee building the safe

14 account standards promoting the idea of a safe

15 account.

16 We now have, as Amelia pointed out,

17 banks in all 50 states that offer the safe

18 account. We have a -- we have banks that cover

19 most of the -- you know, the vast majority of the

20 population.

21 I think what this panel has done is

22 given us a set of tools to think about how now we

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1 can drive consumers to those accounts,

2 particularly those consumers who are unbanked.

3 So really helpful panel. Thank you

4 very much. And to close it up, let me hand it

5 back to the Chairman.

6 CHAIRMAN MCWILLIAMS: Thank you very

7 much. I would suggest that you -- as you go to

8 your clients and your members you can do what I

9 did and tell them to lie to their kids. It works

10 wonderfully.

11 My daughter until she was probably 14

12 was convinced that she won't get into a good

13 college if she doesn't save money. And the

14 causality wasn't you can't pay for college. You

15 won't get into a good college until her friend

16 said what. And I never invited the friend back

17 to the house.

18 I want to thank you all for taking the

19 time. I know how busy you are and the work that

20 you do in all of your cases translates into

21 something on the ground that's tangible and real.

22 And for you to take the time to travel

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1 and spend time with us and to inform our policy

2 is extremely valuable to us and something that I

3 personally am very grateful to all of you for

4 doing.

5 I'm grateful to all of the presenters. 

6 You work on amazing stuff and it's exciting. I

7 wish I were younger and the opportunities would

8 have been endless in this space to actually

9 engage with a lot of things.

10 Now that I am not your age -- we'll

11 just leave it at that, and I can barely run from

12 a gate to a gate whereas my friends go -- I

13 marvel at the work that your groups are doing and

14 your organizations are doing in this space, and

15 thank you for that.

16 Before everyone leaves, I would like

17 to make sure that we all know that the National

18 Interagency Community Reinvestment Conference, an

19 interagency conference focused on CRA that is

20 held every two years, will be held on March 9th

21 through 12th in 2020 in Denver.

22 We encourage broad participation of

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1 all stakeholders, bankers, CDFIs, community

2 groups, for- and non-profit developers, and

3 anyone else with a stake in the growth of

4 economic and housing opportunities in our

5 communities. And I suspect we'll see some of you

6 there as well.

7 Finally, I know our staff is in touch

8 with the members to schedule our 2020 meetings. 

9 In the meantime, we'll work on the agenda.

10 I thought the agenda for today was

11 excellent. We had excellent presentations today. 

12 I'm very grateful for that. The discussion was

13 so robust. Even when I wasn't here, I was able

14 to watch on the screen as I had my conference

15 calls and I had to mute the phone and turn the

16 volume up a couple of times, but I won't tell you

17 who those calls were with because otherwise

18 you'll tell on me.

19 Thank you all, again, for coming here

20 today. For the members, if we won't see in this

21 capacity, we look forward to seeing you in other

22 capacities. And to the members of the committee

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1 who are new to us, welcome and thank you all. 

2 And I can't wait to see you again.

3 Thank you.

4 (Applause.)

5 (Whereupon, the above-entitled matter

6 went off the record at 4:00 p.m.)

7

8

9

10

11

12

13

14

15

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17

18

19

20

21

22

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A

a.m 1:10 4:2 89:21,22

abandoned 85:6

ability 22:15 55:9 77:3

91:2 96:3,11 126:10

126:18 135:11,17,18

145:5,20 149:6 209:1

217:13 230:11 234:8

304:21 310:11 322:2

322:3

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16:16 17:18 18:3,8,9

18:13 19:11,19 38:22

38:22 55:6 58:13 64:1

70:11,12 73:3 77:3

85:1 97:1,9 101:2,9

101:21 102:16 103:11

103:14,16 104:2

105:14 107:6,15

119:15 123:12 132:20

139:2 147:12 153:5

160:8,22 170:1

189:12 191:4,20

192:4,10 194:5 196:6

196:14 228:12 232:9

247:15,21 253:3

278:19 284:9 287:4

290:6,21 292:9

330:13

above-entitled 89:20

174:4 258:11 331:5

absence 12:1 234:11

absent 187:14

absolute 12:1

absolutely 61:12 77:12

230:10 240:12,14

243:5 252:21 255:12

255:12 256:1

abstract 280:5

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317:13

accept 66:11

acceptance 283:5

accepted 129:11

accepts 153:11

access 5:4 7:13 10:22

11:12 13:20 14:6

16:13 19:3 22:16,16

23:8 24:10 26:19,21

27:1,17 28:10,12 29:4

42:1 50:14 62:16 63:6

65:18 83:13 90:12

92:17 93:18 94:4

96:17 115:12,22

116:4 117:4 118:8

122:14 125:2,12

130:2 132:17 135:19

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157:15 177:1 180:6

188:6 191:16 204:15

219:14,17 222:11

229:11,21 240:13

253:16 273:4 286:7

290:7 301:2

accessed 125:11

accessible 38:3 96:1

accessing 51:3 101:10

124:20 125:6 161:14

290:19 291:18

accomplish 17:7

214:10 225:19 266:6

accomplishing 268:22

accounted 229:7

accounting 99:6

accounts 3:16 7:17

25:15,18 26:19,22

28:17 39:13,20 40:11

41:4,9,11 56:5 59:1

60:8 65:6,10,12,13,17

65:17 66:15,16,19

77:10,13,18 78:2,3,8

78:13,20 79:3,7,12,20

98:22 105:5 113:16

117:1 119:21 136:15

136:17,18 146:7

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209:11,17 211:11

212:2 219:2 226:14

228:19 245:6,8

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265:3 274:1 293:20

293:21 294:5 312:5,8

313:12,18,22 314:13

316:6 317:12,17

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accumulate 292:17

accuracy 125:4

accurate 28:19 94:20

134:5

accurately 165:6

achievable 305:4

achieve 41:19 51:7

296:19 303:10 305:13

achieved 325:4

achievement 86:13

achievements 325:7

achieves 315:19

acknowledge 8:12,18

44:21

Acolin 191:2,10 192:16

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acquires 195:11

act 37:10 41:18 44:11

45:5 46:20 57:20

153:12,15 156:20,20

183:20 305:15

action 33:21 184:4

185:1 265:17 267:20

272:2,6,15 275:13

280:15,20 285:18,19

287:3 302:12,13

324:4

actions 277:9 286:16

288:5

active 22:12

activities 84:14 156:8

activity 309:1

actual 32:7 60:19 165:6

182:3 186:21 192:4

198:12 203:16 245:7

291:20 303:21

acute 133:22

acutely 142:2

add 8:14 49:8 61:18

80:11 124:19 171:20

217:19 253:7 314:8

315:3 319:20 324:8

added 152:8

adding 14:21 103:7

134:17 135:14 213:10

213:11 215:22

addition 92:7 134:13

135:13 210:22 247:18

303:18 306:11

additional 10:16 74:20

94:22 96:8 97:11 99:5

102:17 112:19 142:16

193:18 198:10 211:2

215:22 218:7 220:22

238:17 249:1 253:11

253:12 254:20 255:6

255:7 292:18 305:9

address 61:1 109:18

130:21 153:9 261:15

317:20

addressable 116:8

addressed 123:7

173:19 282:19 304:22

addressing 4:21 26:12

46:13 59:18 263:5

adequate 153:1

adjacent 230:6

Adjourn 3:20

adjust 73:8

administrative 205:4

admitted 29:22

admonition 169:1

ado 92:22 223:11

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adopters 121:9

adoption 108:15 285:3

295:6 303:2

adult 75:17

adults 62:9 64:2 307:4

308:6

advance 34:4,7 44:16

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advances 76:22

advantage 46:1 49:15

273:18

advertising 9:11

advice 11:13 79:16

175:14

advised 77:12

advisors 286:8

advisory 1:3,9 80:13

81:20 156:8

advocacy 57:10 61:12

advocate 7:16 20:2

advocates 43:19 60:5

152:17

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affairs 7:20 11:22

258:17 259:15 260:3

afford 74:1 122:22

123:13 128:8

affordability 126:10

254:19

affordable 23:2 47:2

71:13,14,17 73:18

122:15,21 175:1

293:20

afraid 14:17

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165:13 169:3 236:11

244:11 247:12

African-American

175:4 176:2

African-Americans

194:20 199:15 200:9

aftermath 87:8

afternoon 88:5 147:8

174:15 258:19 261:14

276:3 295:2

afterward 80:5

age 60:18 191:14

192:20 195:9 196:3

196:21 306:22 310:2

310:9,18 312:10

318:20 329:10

agencies 152:5 154:5

159:10 202:14 288:6

agency 4:18 5:6 152:1

152:10 153:11 287:17

316:7

agenda 16:4 330:9,10

aggregate 151:22

201:20 236:8,12,21

321:3

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131:21 151:21 153:21

155:21 156:1

aggregator 125:12

154:20 157:20

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130:9 153:13 154:1

154:14

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52:19 57:3 84:8 85:16

85:17,18,18 87:15,16

88:3 93:17 94:4

104:20 142:19 225:16

312:6,20

agree 47:17 129:12

148:11 150:14,16

234:22 244:20

agreeing 129:11

ahead 202:9

AI 110:1

airlines 110:4,17

Airport 26:6

airports 110:7

airtight 66:16

Alden 1:20 68:13,15

136:2 137:1 141:6

algorithms 99:15

163:19,21 166:14

242:20

alleviate 167:4

Alliant 281:1 282:22

285:5 311:6

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allocation 224:9 226:1

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179:10

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265:22

alongside 81:4

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175:15

alternative 96:9 112:22

113:6,8 114:21

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Amelia 2:6 263:8,10

292:21 293:7 311:13

327:16

amenities 23:15

America 2:9,10 42:15

86:10 262:2,7,14

264:6,8,19,20 265:12

265:21 268:19 271:13

272:16 273:8,9 274:6

274:14,16 275:18

306:21 307:2,5

308:16,17,19 309:2

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86:12 145:10 165:13

169:4 244:11 247:12

262:4

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94:21 105:4 262:12

262:13 265:1,7,11

267:19 272:8 282:20

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amortization 187:9

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57:14,15 67:4 112:4

116:14 140:21 158:10

165:11 172:11 193:22

206:8 221:4 227:21

253:15 254:21 256:2

308:18 314:15

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127:21 128:12,13

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211:19 221:11 227:15

229:16 230:20 235:8

240:2,20 313:10

317:8

analysts 233:9

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195:3

analyzing 162:5

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Andrea 1:19 8:15 54:21

55:16 136:2 144:4

148:2,19 226:2

234:22 290:17 321:15

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Angeles 151:2 275:6

Annibale 1:13 10:4,6,10

13:10 158:6 223:15

223:15 224:20 225:4

225:8,21

anniversary 111:7

122:9

announce 11:4 12:19

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109:17 116:13 121:6

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162:9 173:12,18

175:11 205:20,20

207:6 233:6 241:10

248:1 250:12 251:11

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282:10

anybody 11:16,18 14:1

15:2 151:14 159:3,3

159:18 160:12 252:6

302:1

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248:12

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API 118:18 152:18

apiece 9:5

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appealing 155:17

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appear 187:21

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Applause 174:3 258:7

331:4

applicant 102:8 119:7,8

119:12,17 120:10

150:11 155:11 157:14

242:13

applicants 101:10

103:6,15 119:16

244:5 247:12

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274:10 308:22

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118:12 129:5 155:1

156:21 171:8 206:7

280:4

applying 41:8 263:1

289:11

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appointment 81:19

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43:13 48:18 76:16

80:6 110:22 127:18

243:21 260:20,22

293:3 321:13

approach 42:5 43:4

54:20 64:8,22 67:20

149:14 270:1

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APR 137:18,19

Arctic 85:11,12,14

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109:8 302:15 316:18

317:4

areas 33:18 50:7 70:19

71:12 103:11 109:12

198:12 262:19 318:7

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266:11,14 267:2

269:13 270:7 304:1

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250:1 254:20,21

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194:7

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324:21

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248:18,21 250:7

263:4

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134:2 300:6

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53:16 73:20,21 74:4

203:14 262:6 263:12

274:20

Assistant 5:21 6:13

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90:3 122:6 258:16

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187:11 222:20 252:18

300:20 326:10

Associates 100:16

association 17:21

74:12 210:7

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assuming 230:5

assumption 310:13

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Atlanta 1:14 5:17 151:1

ATM 13:14

ATMs 13:20,21 14:6

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192:10,11 196:7

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227:17

audience 17:19 35:14

76:17 265:13,18

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297:21

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Authority 226:9

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authorize 57:12

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automate 59:6

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126:13 147:3 171:19

287:13

automatic 142:20 223:4

272:11

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270:7,10

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183:13 185:18 187:16

232:1 233:14

available 39:22 65:10

100:22 102:13 114:11

114:12 121:19 133:4

133:8,12,18 138:7

190:10 204:2,8

205:10 206:12 232:14

293:21 305:18 311:7

availment 159:20

avenue 92:21 273:1

average 14:12 16:1,12

30:17 34:10 79:4

86:22 87:2,14,17

120:7 130:11 140:5,9

148:16 200:8 251:2

301:14

awake 293:1

award 131:2

awarded 274:3,8

aware 142:2 154:15

167:8,19 170:14

278:16 291:16

axis 210:12,14 213:17

213:19 301:14,16

B

baby 60:11,22 169:8

170:10 317:21

back 30:22 38:4,7 58:14

58:19 59:8 63:18

67:12 70:13 75:4

78:17 90:9 109:13

111:8 118:21 131:15

144:6 161:11 166:7

166:16,17 179:2

181:15 185:8 186:8

205:11 246:15 249:19

251:3 269:11 270:22

279:3 292:10 298:8

321:8 328:5,16

backdrop 133:4

background 87:22

109:9 189:17 303:15

backgrounds 161:7

backroom 17:10

backstop 224:4

backwards 74:5

bad 33:21 34:18 85:2

251:9

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baked 188:1

balance 42:3 102:6,7

120:7 133:14 140:5

148:16 209:16 210:1

210:4 225:17 232:6

236:10 237:6,6

balanced 131:13

balances 14:16 117:17

117:18 120:6 140:10

163:17 305:3 325:17

326:9

balancing 222:11

ball 76:21

balloon 126:1

bank 1:14,20 3:16 5:17

7:15 17:4 18:4 20:4,6

20:6,10,12,16 21:6

25:13,14,18 26:20

27:7,11,14 28:1,5

29:22 34:3,15 38:1

40:18 41:2 55:14

66:14,15 68:16,17,17

70:5 77:7,20,22 78:1

79:10,12,18,21 90:19

96:10 108:15 117:4

125:12 126:3,20

132:7,8 134:20

136:13 153:7 161:14

172:3,14,15 173:4,7

177:19 258:21 261:18

263:16 271:14 290:10

291:10,12 293:14,17

295:8,11 298:3,6,20

299:7 300:12 302:18

303:1 305:12 325:1

325:21 326:9

bank's 14:2 172:22

banked 56:18 57:8

306:4

banker 30:20 36:6

bankers 330:1

banking 5:3,5 7:4,14

17:3 27:17 28:5 31:2

43:12 44:1,9 45:16

46:11 47:16 62:17

63:6 65:1 68:19,21

69:7,22 77:1 79:9

80:1 82:12 83:5,22

117:16 161:3 163:12

261:7 287:2 293:20

295:10 296:17 298:11

300:7 303:9 304:16

304:18

bankruptcies 194:1

banks 13:16,19 14:6

16:15 20:2,13 24:12

24:12,21 28:6 29:21

32:14 38:10 44:6 46:2

47:18 69:2 99:21

108:2 109:16 143:8

143:11,14,14 152:14

152:17,20 158:10

163:12 169:12,13,15

170:4 177:20 178:2,6

178:9,10 179:1,4,13

179:22 180:8,8 181:8

181:8,11 182:9

274:11 297:6,18

298:1,18,19 299:4

300:21 302:17 309:4

309:10 327:17,18

bar 35:15 150:17

179:18 180:19 198:22

199:4 200:17,18

239:6

barely 329:11

Barrera 1:14 8:13 15:11

19:19 20:16

barrier 66:7 296:1,3

304:8

barriers 63:2,5 65:5

81:9 289:16,18 290:5

291:3,16,21,21 318:4

bars 179:18 211:4,5,7

220:9,12,13,14

base 47:20 97:8 118:1

137:6 160:21 281:3,6

baseball 148:8

based 50:19 62:2 91:17

105:17 107:7 109:7

133:7 134:22 135:12

143:1 158:7 167:5

170:17 182:13 191:16

213:20 230:16 248:18

281:2 283:12

baseline 241:5

basic 123:4 286:12

299:15

basically 13:19 70:18

100:17 123:2 145:5

162:22 179:15,19

182:16 187:22 192:17

197:19 236:21 241:6

246:20 274:16

basics 40:10

basing 143:11

basis 55:7 90:21 102:9

129:1 132:5 221:5

225:12 226:16 266:18

268:2 270:21 286:6

287:10 322:14

bastion 30:1

baton 188:22

battery 171:5

Bay 54:9

Beck 8:19

becoming 95:22 156:12

began 74:6 93:17

beginning 42:21 56:11

61:2 72:10 259:9

begins 79:8

behalf 56:18 62:8

behave 278:2,4

behavior 266:2 278:19

279:11 282:2

behavioral 223:2

262:11,21 263:1,5

264:22 276:13 278:18

281:15 289:5,10,20

292:13

behaviors 119:20 160:9

belief 30:7

beliefs 310:19

believability 301:15

303:20

believable 302:5,11,20

believe 12:22 31:13

44:16 163:21 165:1

194:22 219:3 281:14

302:14

belly 19:9

belong 18:16,18

benchmarked 100:22

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335

beneficial 142:4 322:8

benefit 91:4 135:9

211:1 227:1 288:11

292:19

benefits 11:17 120:4

142:1,10,11 170:6

295:10,11

best 35:15 47:18 59:10

79:15 110:8 133:9

157:10 258:15 294:21

299:21 309:12

better 10:15 43:3,19

92:1 102:14 124:20

135:15,16 150:18

208:22 246:7 250:17

271:21 278:19 300:17

325:3 326:1

beyond 50:10 91:22

211:4 273:22

Bhagat 2:2 176:3

204:17 224:11,21

225:5,10 228:21

229:8 230:4,9,17,19

240:8,11,18 241:3,11

243:2,4 253:21 254:2

255:11

bias 120:1 220:7 283:16

bid 263:7

big 12:7 14:20 17:15

111:6 150:20 195:10

220:16 236:22 237:1

237:1,10,11 309:6

bigger 15:5 17:19 20:13

85:7 300:6 304:3

305:7

biggest 46:16 47:11

177:3 179:22 237:14

billion 31:17 82:16,19

109:15

billions 57:16

bills 98:22

bin 211:7,8,11,11

212:19 220:13

bins 230:6

biotechnology 51:13

birth 314:14

bit 23:21 36:8 68:5

103:10 105:11 107:5

111:2 119:3 124:1

136:11 143:17 160:21

163:8 170:16 172:5

175:20 176:1,4 193:2

196:13,16 198:10

205:13,22 207:10

209:9 216:10,19

217:4,6 218:5,11

221:13 222:10 223:21

225:15 244:10 258:9

265:8 275:15 293:5

298:15 324:15

bittersweet 15:12

black 30:14,19 31:6

32:8 33:8 34:9 36:6,6

36:7 46:17 48:7 52:21

53:5 87:15 167:17

180:2 182:19 244:5,6

260:14,15

black/white 190:11

196:6 199:16

blacks 95:4 190:4,17,19

195:17 197:2 199:20

200:5

blame 110:11

blanketly 289:11

blight 74:18

blocked 28:4

blog 267:21 268:2

blue 177:21,21 178:7

184:2,20 190:4

198:22 199:4 211:4,5

213:21 214:2,6

215:16,19,19 218:17

218:18 220:9,12

299:2 302:21

board 1:10,12,16 8:3

29:6 48:2 55:20 80:13

80:22 81:20 88:19

104:18 235:10

Bob 10:3 68:5 136:3

148:9 158:4 223:14

223:15

body 42:19 70:20

bonds 60:22 317:21

book 162:16,19

Booker 317:19

booster 319:11

border 163:5

born 60:12

borrow 18:12

borrower 135:16 138:6

179:17 183:9 202:12

202:12 203:3 216:8

217:6 219:17 222:14

224:16

borrower's 126:11

217:13

borrowers 105:16,19

106:15 107:4,11

108:7 165:13 179:14

179:20 180:1,3,7,9

182:17,20 187:18

207:2,11,13,18,20

208:8 209:3,5 212:10

212:14 213:1,3,7,22

215:15 216:5,6

218:14,16 244:11

246:21 247:9 248:11

borrowing 187:1

Bostic 1:14 5:16 20:21

148:10 168:11 232:11

234:16,19 236:2,15

236:19 237:5,22

238:3,9 240:16,19

241:4 242:5 243:3

Boston 8:19 12:21

51:12 52:16,17,21,22

54:8 55:14 60:9

bottom 106:7 165:4

181:16 182:22 205:12

301:19

bought 207:3

box 17:11 113:18 114:5

129:12 167:17 181:1

301:19 302:10,13,21

boy 276:5

brain 80:15 276:7

277:12

branch 26:20 27:7,11

281:13

branches 27:15 37:21

163:18 293:22

branchless 77:1 79:9

79:10,20

brand 38:3

brand-new 18:19 38:14

branding 274:17,21

Brazilian 279:18

bread 301:2

break 89:17 163:15

196:15 231:7 244:14

249:4 258:3

breakdown 256:13

309:21

breakdowns 236:9

breaking 238:10

breakout 244:2 254:12

255:10

breaks 80:5

breathe 292:9

brick 27:14

bridge 76:21 203:14

brief 228:1 297:10

briefly 107:18 131:21

bring 5:11 7:3 26:10

27:22 29:12 30:16

44:6 45:7 62:6,12

65:1 68:20 113:1

118:21 130:8 160:22

192:10

bringing 13:1 34:9 44:1

83:10 136:14 171:10

285:10

brings 33:18

broad 91:13 100:5

228:8 317:12 329:22

broader 127:22 129:14

129:17,21 153:20

156:13 182:10

broadly 256:14

broke 70:18

broken 239:7

brought 63:10 83:6

175:2

Brown 2:3 175:22 189:2

227:9,14 228:10

236:14,18 237:3,20

238:2,8,15,20 252:11

253:9,11,14,17 257:3

257:6

Bruce 6:3,6 8:20 37:15

Bryant 20:11

bubble 248:3,18,22

250:7

bucket 212:12 215:9,10

234:9

budget 290:8 320:2

build 13:3,4 42:15

46:15 53:9,13 54:16

58:15 59:8,22 60:1,2

64:11 81:5 96:6

114:21 293:10 295:2

316:10,15

Builders 17:22

building 62:18,20 73:4

75:12 135:11 256:11

263:4 300:10 327:13

builds 322:13,15

built 12:15 77:12 96:16

247:5

bumpiness 221:15

bunch 168:13

bunching 221:3

bundle 49:20

burden 206:2 208:2

235:18 240:21

bureau 91:22 111:11

113:5,7,14,21 114:11

114:13,17 115:5,9

117:20,22 152:4

160:22 227:11

bureaus 111:12 112:14

115:19 159:5

burned 298:6

business 6:11,14 9:1

15:21 19:5,8,12 20:9

31:18 82:22 83:8

98:12 99:4 100:2,3

103:6 108:10 118:5

146:20,21 155:11

162:20 168:18 171:16

205:6 252:16 300:2

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52:10,14 53:9,13,15

110:2

busy 328:19

butter 301:3

buy 19:16 63:11,18

324:19

buyers 121:15 179:8

203:5

buying 208:10

buzzwords 110:2

C

C 1:22

C-O-N-T-E-N-T-S 3:5

cable 115:6

calculate 118:22

calendars 9:14

California 7:8 199:12

199:13,14 319:20

call 45:22 71:13 96:8

219:7 265:13 266:4

267:6 275:9 285:18

286:20

called 11:11 58:10 62:1

116:20 145:2 184:10

286:5 317:21

calling 298:6

calls 330:15,17

camp 76:3

campaign 60:6 265:20

267:17 272:7 274:14

275:8 307:3,3 323:17

campaigns 262:7

264:16 265:6 274:15

307:14

cancel 237:3

cap 285:19

capabilities 78:22

capability 11:13 60:16

62:10,13 322:4

capacities 330:22

capacity 53:20 330:21

capital 16:13 19:4

22:17 23:8,14 24:9,19

54:15 224:9 226:1

Capitalism 162:17

caps 38:7

capture 281:4 284:10

captured 284:6

captures 276:15

car 39:8 119:13,13

card 38:2,3,5,19 72:15

91:11 95:19 96:21

98:22 109:14 115:11

115:12 121:14 126:5

129:6 137:10,18,21

138:3 143:18 160:3

160:19 172:16 222:20

239:22 301:1 305:18

cards 65:17 109:16,16

138:4

care 51:1,3 151:9 165:4

165:5

career 5:18 67:9 235:9

carries 316:8

carry 138:2 316:16

321:6

Carter 81:4

cartoon 276:4

case 50:22 116:2

118:12 154:20 171:10

205:13 208:21 211:22

219:5 272:3 325:3

cases 102:14 167:16

199:10 328:20

cash 14:10,12 18:9 27:8

27:15,20 33:7,10,17

34:8 38:4,6 58:2 59:3

63:18 77:3,4 79:3

84:1 91:17 98:5,18

99:8 101:22 102:11

102:19 105:22 106:18

108:6 122:18 123:1

125:10 133:16 135:1

136:8 138:11 141:1

143:2 144:21 145:6

145:20 206:9 244:6

245:6,10,11,15,16

246:5 254:18 255:7

255:18 256:2 287:9

290:8 315:4,5,9

Cashback 38:2

casher 41:1,5 63:17

cashers 37:19 64:3,7

cashier 63:12

cashing 25:1 37:18

83:20

casual 316:17

catch 246:6,7 305:15

catch-22 123:19

catches 88:3

categories 38:7 113:9

299:2 303:7

category 244:18 303:4

causal 216:15 217:3

218:5

causality 328:14

caused 69:21

causes 248:3

causing 72:12 107:17

cautionary 45:17

cautious 169:1,16

caveat 183:3 216:2,3

caveats 289:4

CDBG-DR 82:19

CDFIs 15:15 17:17 18:7

58:22 59:9 100:4

202:15 330:1

cease 16:4

celebrating 15:19 111:7

celebration 122:10

cell 115:6 171:6

cells 107:4

center 2:19 29:19 32:1

48:1 92:4 122:7,11

233:13

centered 7:2 74:14,15

74:15

centers 74:9 202:12,13

318:16

central 305:1

CEO 2:12 6:17 7:1,12

62:1 68:15 70:10

91:11

ceremonies 252:3

certain 73:14,15 105:3

135:3 172:8,11

219:16 222:3 259:9

310:8

certainly 47:10 48:12

84:2 86:7 95:12

104:16 107:16 108:17

123:8,11 128:14

156:15 158:7 160:20

167:18 168:17 170:3

170:7 193:13 251:8

260:15,20 310:9

321:21

certification 79:11

certified 38:1 77:18

78:2,3 202:16 298:13

certify 77:10

cetera 93:15 131:18

133:2 171:16 265:16

267:9 274:17 294:15

CFE 76:19 77:7 79:11

263:10 293:19

CFPB 94:17 187:2

234:8

chain 271:5

chair 5:22 6:4 89:6

chairman 1:11,12 4:16

4:17,18 9:9 36:4

42:17,20,21 44:19

47:17 80:11 93:4

110:10,14,19 148:3

184:6,11,14 328:5,6

chairmanships 34:15

chairwoman 20:1

challenge 21:8 41:17

42:1 280:9 324:2

challenged 28:4 40:16

challenges 10:20 21:4

24:3,5 40:15 43:5

48:6 49:11 65:2,4

70:7,14,16 81:9,18

82:6 85:11 90:16

152:14 170:7 261:6

challenging 27:1 64:3

champions 260:8

chance 44:4,17 45:14

115:2 129:17 130:20

130:22 135:2 251:21

259:5 260:7,14 261:1

change 17:15 22:6,15

30:4 66:12 71:6 73:16

83:2 266:2 267:15

279:11 290:1 297:15

308:18

changed 142:21 148:14

changes 23:4 43:9 44:4

45:11 60:20 73:13

74:14,16 75:10 87:10

110:12 144:19 183:6

241:9 298:12 318:10

changing 67:6 137:4

297:22 301:18 307:9

channel 285:9

characteristics 33:3

179:14,17 183:10

195:4 208:9 216:8

charge 13:22,22 142:12

143:12

charge-offs 194:1

charged 14:18 138:18

charitable 262:18

charity 25:1

Charles 100:16

chart 34:18 131:2,7

148:13 177:6,17,22

178:19 181:17,17

182:15 183:15 186:4

187:2 189:18 194:9

195:9,21 197:18

210:12 211:1 215:1

215:12 216:13,16

217:3,15 220:8

284:15

charter 172:22 173:4,11

chartered 70:5

charters 34:15

charts 164:12 180:11

180:13 182:13 217:16

Chase 2:2 77:22 176:4

204:21 205:4,18

207:7,8,8 209:10

212:1 219:2,4 244:10

cheaper 25:2

check 14:16 25:1 28:7

37:18,19 41:1,5 63:12

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63:17 64:2,7 66:2

269:6 283:8,11,11,15

283:19 284:1,5,7,17

284:20 285:13 286:5

286:6,13 287:18

304:18

checking 37:22 39:19

40:10,21,22 65:17

116:22 117:12 209:17

210:2,5,15 219:1

221:8 256:3

checks 77:4 83:20

285:6

ChexSystems 296:5

Chicago 12:22 281:2

child 27:5 51:3 60:11

313:5,17,21 316:6

318:10,19 319:4

321:17 322:18

childhood 10:6 312:5,8

313:11

children 21:22 60:4,8

66:1 67:18 317:11,17

320:3 322:1

children's 25:22 26:15

26:16 318:15

Choi 192:8

choice 39:2 261:12

choices 67:8

choose 129:7,8 160:3

choosing 133:19

chose 125:16

chronic 141:3

Cincinnati 238:6

CIP 66:12

circled 190:7,17

circles 321:7

circumstances 64:18

Cisneros 1:15 25:8,9

319:13 320:1,7,10

322:11 327:6,8

Citi 1:13 38:11

CitiBank 26:2,18

cities 1:20 2:6 7:12

12:21 13:3,20 64:21

71:11 199:21 263:9

293:7 296:7 319:15

citizens 83:12

city 1:15 7:21 11:9 20:5

25:16 26:20 64:19

73:12

civil 1:17 42:11,13

43:15 45:6

clamor 312:19

clarifying 228:17

clarity 235:4

class 10:8 33:6 54:5

159:18

classes 75:17

classic 111:9 112:11

133:6 276:12

classification 142:20

classified 185:12

classroom 276:6

classroom-based

279:5

clear 28:18 166:10

235:16 283:20 306:1

325:18

clearly 53:3 123:1

149:5 159:13 161:19

177:18 217:22 248:16

Cleveland 238:6

click 172:4,6

clicking 129:11

client 38:5,6 202:22

clients 13:19 14:7

36:16 38:17 39:3,6

202:18 328:8

clients' 39:19

climate 88:16

close 8:12 33:15 53:1

71:4 106:2 185:1

231:1 326:11 328:4

closed 209:18 298:3

closely 152:9 295:16

303:21

closer 110:16 153:3,3

190:9 240:2

closing 3:18 56:21

210:16 211:7 255:19

263:6

closures 74:9

cluster 105:19

co-founder 2:12 91:11

coaching 11:13 12:16

25:19 49:21 68:2,5

287:5 294:14

coalition 7:9 42:13 45:1

coalitions 7:15 77:9

294:6 295:8 300:3

303:21

Cochran 2:4 91:15

97:19,20 105:14

138:19 145:22 146:4

146:9,16 147:2,20

155:2 173:14

coefficients 241:1

cofounders 160:17

cogent 131:6

coherent 321:4

cohort 207:2

coin 29:5

coincide 309:9

cold 320:15

collaborate 97:16

collaboration 59:14

collaborations 56:20

collaborative 43:4

collapse 248:3 250:10

colleague 83:19

colleagues 44:22 83:7

84:9 86:3,9 88:19

89:12 227:4 246:18

314:1 316:2

collect 78:1 127:3

collecting 99:14

collective 22:7

college 26:22 283:5

314:13 318:11,20,22

319:1,2 321:18,19

322:12,18,20,21,22

328:13,14,15

college-going 314:21

colleges 48:7 263:3

color 30:16 31:19 33:9

44:1 47:13 63:3 69:3

107:2 145:12 190:5

246:22 322:1

Colorado 9:13

Columbia 58:5

Columbus 238:6

column 164:14

combine 100:13 101:3

102:16

combining 197:19

come 13:12 18:12

26:10 52:6 55:13

63:18 78:4 111:21

112:5 133:8 135:22

152:18 159:4,5

166:15 176:12 191:1

191:11 193:10 204:14

208:6 259:18 261:4

283:4 288:13 294:10

321:8

comes 6:18 10:1 17:13

81:17 127:12,13

167:14 233:11 242:7

266:1 271:3 277:5

285:19 291:18 308:4

comfort 282:9

comfortable 152:22

234:6

coming 4:10 8:12 10:17

61:5 71:18 75:4 79:11

123:5,5 140:2 159:17

275:19 277:2 330:19

comment 45:21 56:21

83:18 148:19 238:16

247:8

commenting 253:22

comments 70:18

148:10 174:21 175:1

175:17 259:5 308:12

310:21

Commerce 6:21

commission 11:21

69:15

Commissioner 7:20

commissioners 13:2

commit 45:11

commitment 37:6

164:18 261:9 265:22

committee 1:3,9 3:9 4:7

4:12,14 5:7,14 6:4,8

6:10 7:5,11,18 8:11

21:3 26:10 27:22

29:12 35:10 42:17

43:8 44:14,18 45:15

47:9 48:8,11,17 55:1

59:19 61:17 62:5

83:11 84:8 85:15

88:13 90:8 92:9,11

135:22 163:9 173:20

223:12 261:5 274:10

314:3 327:13 330:22

common 157:4 272:12

289:8 323:9

communicating 267:22

294:21 326:1

communication 264:10

266:6 268:15 269:21

271:9,22 282:17

295:21

communications 262:5

263:13 268:22 271:4

271:6,16

communities 10:19,21

23:9 24:13,15 31:19

36:17 40:5 44:1,10

47:6,12 49:11,17,22

51:18 54:2,3 59:11

60:1 61:4 69:3,5

71:18 72:1,2 73:15

74:19 75:8 169:4

204:11 246:22 273:16

289:14,15 291:4

312:22 316:9 330:5

community 6:14 9:12

9:20 13:16 14:6 17:4

17:22 24:12 37:10

41:18 43:15,19 45:1,4

49:14 53:5 57:4 63:21

64:2 68:17,22 69:7,13

69:20,22 70:4,17 71:2

75:18 79:18 80:19

108:3 143:8,14

169:12 178:9 204:2

258:17 259:15 260:3

265:18 274:19 275:3

275:11 316:13 319:1

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329:18 330:1

community- 50:18

community-based

23:15 50:13

companies 34:16 53:19

99:20 100:5 101:6

102:2,20 108:4,5

124:21

company 91:12 100:13

104:3 125:14 139:1

150:21

company's 91:21

compare 28:12 104:7

150:1 181:7 185:9

222:7

compared 26:21 138:1

186:11,22 195:19

199:17 200:8 225:15

comparing 208:13

comparison 102:13

179:21 228:19 229:7

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compelling 101:20

169:11 304:2,19

compensating 247:6

compete 37:19 51:15

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competition 133:1

competitors 24:22

compilation 244:21

complement 91:18

complementary 103:4

complete 155:13 295:3

completed 121:17

completely 171:3 200:1

complex 11:14 13:6

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288:7

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compliant 79:12 154:5

complicated 105:11

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component 50:21 51:5

52:11 53:3 232:7

components 33:20

composition 201:21

compound 280:5

compounding 242:2

comprehensive 133:20

concentrate 74:13

114:4

concentrated 198:4

concept 18:6,19 209:19

210:8 213:10 214:9

concepts 280:5

concern 92:20 167:4

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concerned 159:12

290:6 291:13 326:18

concerns 28:8 92:5

291:10

conclude 92:2 119:3

207:21 247:10

concluded 246:20

248:6

concluding 250:2

conclusion 30:22 121:4

concrete 287:8

conditional 315:7

conducted 279:18

296:6

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conference 1:17 9:13

42:10,12 43:19 48:11

329:18,19 330:14

confidence 5:3 53:20

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confirm 96:2

confirmed 31:16

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conforming 181:20

confused 41:5

confusion 114:9

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congratulate 69:10

Congress 70:5 104:20

connect 25:15 77:16

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connected 23:10 66:5

82:12 275:1 325:7

connecting 325:20

connection 59:21,22

172:7 231:1 241:19

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connections 81:21

294:7

conscious 268:14

271:3,10,15

consent 129:13

consequences 45:12

consider 42:4 91:8

191:14 261:17 263:16

263:19 277:14 325:8

considerable 91:7

considerably 207:13,19

209:5 213:3

considerations 108:22

131:14 253:2,3

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considered 114:10

consistency 29:8

consistent 23:9 99:17

103:20 104:17 139:4

139:9 213:14 221:13

244:8 263:22

consistently 148:22

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consolidation 287:17

constant 43:9 242:3

constraint 149:4,6

construction 74:21

consulting 83:8 158:19

consumer 2:9,19 7:20

11:21 28:10,21 45:19

47:19 90:3,4 92:4

96:1,6 98:15,21 100:3

108:14 114:14 115:10

117:2,3,13 118:12,14

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129:1 130:11 131:17

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134:1 135:3,7 138:7

138:12 141:18 143:20

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context 85:17 90:9

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239:16 277:13 281:17

289:11 292:11

contexts 289:12

continual 314:18

continually 14:1

continue 6:9 16:17

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continuing 4:17 34:1

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continuum 56:9

contract 158:20

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contribute 195:5

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contribution 202:3

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166:20 196:15 208:13

222:2,8 224:15

229:17 284:13 285:1

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303:13,17 304:5,6,20

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240:1

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convening 61:12

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conventional 188:17

conventions 223:3

convergence 109:19

conversation 81:22

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convince 295:10

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core 4:22 5:5 7:5

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Corporate 6:5

Corporation 1:1,10,18

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correct 28:22 29:1 42:3

127:10 129:5 153:18

284:10 308:19

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74:21 75:2 96:20

97:10 108:12 137:9

138:6,7 163:17

171:10 283:6 302:4

305:20,22 318:22

319:2 325:19 326:18

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countervailing 219:12

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18:7 28:13 36:22 44:8

51:22 54:18 61:14

64:22 69:4 77:9 85:21

159:4 274:16 294:7

313:2 321:7

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21:12 23:1 26:9 31:16

41:15 52:19 55:21

86:11 93:9 97:18

105:22 107:19 137:8

151:17 155:22 162:13

190:22 231:5 234:21

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62:7 63:15 80:4 94:17

96:19 97:13 123:7

125:19 129:19 130:1

158:7 172:10 179:5

181:13 182:4 183:11

194:2 195:14 197:12

202:19 204:12 228:4

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140:6 156:2 258:3

286:11 327:18

coverage 71:22

covering 176:18

coveted 293:2

CRA 23:12 37:8,9

329:19

crack 160:13

craft 277:22

crap 166:10

create 18:10 23:16

30:16 53:4 69:21

133:20 214:17,19

250:9 265:11 266:2

292:16

created 84:19 145:2

220:18 262:14 284:6

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creates 221:14 249:2,3

creating 17:21 18:1,20

262:3 265:3 268:4

312:14

creation 59:6

creative 23:13 24:21

credential 50:8 118:19

credibility 50:16 301:21

302:16

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creditworthiness 90:22

96:3

crime 109:22

crimes 109:20

crisis 33:12 43:2,10

47:1 71:13,14,17

124:5 177:14 185:15

186:2,14 187:1,12

188:2 189:5,7 190:8

193:8 254:8

criteria 118:13 119:1

120:5,10

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43:8 45:3 61:13

127:21 172:17 188:12

criticism 82:15

crop 12:20

crops 85:2

cross 56:19 59:13 95:2

213:9,19 219:17

crowd 85:13

cup 278:10

curiosity 14:11

current 214:13 252:13

currently 8:3 82:11

293:17,21 313:13,17

curriculum 22:5

cushion 292:17

custodial 66:6

custom 307:6

customer 112:4 134:22

137:6 160:21 166:8

customers 16:2 17:16

138:10 140:8 169:12

169:22 170:4 207:7

209:10 212:1 219:3

customize 132:9

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cycle 140:20 168:19

cycles 170:19

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daily 56:17

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damage 46:22

dangers 166:14

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109:6 122:3 139:21

144:6 147:16

dark 177:21 213:21

214:5 215:16,19

218:17

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database 115:4 274:21

date 26:18 112:2

265:10

daughter 328:11

Davis 2:4 262:16,17

263:4 276:3,15

280:17 310:1,14

311:1,6 314:8,11,17

day 19:16 46:19 57:20

80:5 95:16 140:17,17

161:22 171:15 259:6

259:9,10 261:1

275:22 292:2,8,8

293:3 317:11 324:19

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days 12:19 142:8,14

246:5 295:18

DCP 2:5,11,13,14,17

DDA 118:8,15 119:21

152:15 167:13

de 14:21 20:2,6

dead 86:5

deal 326:20

dealing 252:17

dealt 165:19

death 246:8

debate 31:14 45:21

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debates 323:5

debit 38:19 65:17

109:16 222:20 301:1

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debt 39:2 47:14 72:15

123:14 193:22 195:14

227:8,10 228:2,6

235:3 239:20,22,22

241:14 247:2,7,16,18

256:12 267:12,16

285:21 286:10,12,17

287:16,17 288:4,5

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decade 31:11 177:4

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decades 81:2 118:5,7

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decay 280:10,12

December 279:14

decide 230:2

decided 55:19 193:3,10

198:9

decision 95:18 154:10

167:22 168:1 172:9

decision-making 205:8

decisions 21:15 68:4,7

110:3 134:5,6 162:2

278:17 286:15

decline 74:10 88:1

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declined 186:11

declining 190:19

decompose 191:20

decomposing 195:22

decrease 215:22 217:7

decreases 239:13

decreasing 196:22

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dedicate 224:4

dedicated 5:18 319:21

deep 41:9 58:20 248:17

286:8

deepens 144:16

deeper 12:16 40:12,19

40:20 72:16,16

141:20 236:4 263:6

289:21 313:10 323:13

deeply 113:4 185:2,14

default 106:2,9 139:13

164:4 187:12 206:3

206:13 207:1,4 208:1

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210:13,19 211:3,15

211:19 212:7,13,17

213:8,17 215:21

216:19,20 217:1,14

217:17,18 218:7

219:13,19 221:22

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209:4 213:2

defaults 211:8,12

212:21 249:3 254:8

define 209:8 245:9

defined 183:21 209:14

defining 245:18

definitely 52:7 124:15

147:11 153:20 253:18

284:20

definition 234:11

degree 50:1 171:8

deidentified 207:7

delayed 4:4

deliberately 125:16

delighted 258:20

delinquencies 72:12

141:4 142:6,9 194:1

194:21

delinquency 137:16

139:13 140:15 142:13

143:11 246:2

delinquent 227:20

deliver 25:12 27:15

delivered 28:13 290:2

delivering 315:9

delve 113:4

demand 52:10 108:10

113:16 116:21 183:7

192:19 195:10 249:3

314:18

demographic 104:5,13

105:3,6 196:10 238:5

306:6 309:21

demographically 95:1

demographics 196:19

196:20 201:10

demonstrate 198:16

demonstration 318:15

Denial 182:20

denied 128:19 129:16

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density 10:21

Denver 329:21

departing 69:15 80:14

depended 139:14

depending 112:10

depends 161:11,12

depleted 245:16

deploy 91:12

deployment 23:14

deposit 1:1,10 20:10

33:22 34:4,6 79:2

83:4 98:21 113:16

116:21 163:13 174:9

207:8 209:11 212:2

269:7 270:8,11,16,20

272:11 273:1 281:4

281:13,20 282:6,13

282:21 283:12,14

284:2,15,22 301:2

304:17 305:19 311:8

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285:7,7 287:22

depositing 281:9,22

Depositor 90:4

depositories 34:3

depositors 309:22

depository 15:3

deposits 20:14 26:20

27:5,9,10,15 77:3

79:3 156:3 230:14,16

272:11 284:16 285:4

285:10 287:21 308:18

322:13

depression 84:22

depth 296:8 317:8

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276:17

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design 50:17 56:14

136:16 262:22 263:7

277:21,22 289:20

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designated 298:5

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designation 274:1,8

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282:17

designers 278:17

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276:18 278:4 289:14

designs 226:11

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248:7 318:3

destructive 122:16

detail 205:13 207:10

293:15 297:11 303:15

detailed 107:22 193:21

details 35:9 139:10

detection 109:14

deteriorate 188:15

249:19 251:7

deteriorated 12:10

deterioration 70:20

74:6

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228:3

determinants 59:18

192:21 199:7 206:2

determination 16:8

determinations 167:5

determine 104:5 110:8

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deterrent 143:10 291:1

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111:19 112:3 310:17

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6:14 24:10 49:18

56:14 108:3 262:21

263:11 294:13

developments 3:11

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Diaries 158:14

differ 179:12,15

differed 178:17

difference 24:2 52:3

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198:5 201:13 218:2,9

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87:11 98:19,20 99:14

99:21 100:6,7 101:16

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105:3,19 107:2 108:1

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128:20 131:14 133:4

139:6 150:5 156:22

160:2,6 164:5,8,11

167:1,11 170:21

179:16 184:9 186:22

192:13 197:9,21

198:17 199:10,20

200:1 201:1 202:3

213:20 216:7,7

218:15 223:8 226:11

240:4 242:21 277:15

277:16 292:12 294:10

296:10 314:22 315:8

320:2,11,13,13

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differentiate 106:19

160:8

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105:16,21

differentiation 106:14

differently 23:22 59:12

126:4

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108:11

difficulty 165:11

dig 35:9 129:20 303:11

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digital 95:22 96:10

132:17 137:5 161:14

166:16 296:10 310:16

dignity 36:22 37:1,5

dipped 284:20

dipping 159:22

dire 190:18

direct 33:22 34:4,6 79:2

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301:2 304:17 305:18

directed 315:22

directing 268:3

direction 32:12

directions 71:19

directly 17:13 34:2

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90:3 91:16 97:20

122:6 174:9 258:16

259:15 262:2,16

263:9 326:6,13,16

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disabilities 11:15,16,20

12:2,7,11,12,18 13:2

disability 11:18

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182:10

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330:12

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128:11 165:15

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247:13,21

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288:22

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196:19 197:2 201:18

distributions 181:9,14

district 24:21 26:3 58:5

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ditto 49:3

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divergence 190:8

diverse 22:10 44:9 68:4

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Division 90:4 174:9

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doable 17:11 50:11,12

doctor 149:22

documentation 187:7

291:12,17

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documents 204:6,6,8

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149:16 248:15 249:22

doing 11:8 15:15 18:17

22:13 39:9 41:3 48:13

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78:3 87:20 104:14

106:1 111:2,16 118:6

131:7 135:9 140:22

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189:16 202:5,7 213:5

226:10 227:2 251:21

264:6,15 274:13

287:2 288:14,15

313:21 317:7 320:17

324:1,7 329:4,13,14

dollar 18:5,11 33:20,21

38:20 39:12

dollars 13:4 14:3 32:7,9

75:14,20 76:5 287:22

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192:3

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doomsday 166:19

doors 20:9

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301:8,8

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188:5 201:14 320:13

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163:17 192:19 295:6

328:1

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drivers 195:10

drives 117:22

driving 183:11 192:12

198:5 238:12 277:7

drop 211:3 231:4

241:21 242:4

drove 324:16

DTI 182:5 194:2 208:20

227:18 246:13,13,16

252:14,18 253:1,6

254:3

DTIs 251:1 253:22

due 189:8 201:6 245:18

245:21

dues 210:7

dug 296:16

dumb 230:1 232:12

dynamics 235:3

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e-commerce 99:7

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31:6 35:8,19,22 36:9

45:20 162:13 168:3,5

172:13,19,21 173:2,6

173:9 243:19 249:16

250:9,17 251:13,17

253:5,10,12,15 255:8

earlier 77:2 98:6 175:2

226:3 239:1 256:10

269:13 286:19 290:18

294:1,11 303:9

early 21:21 28:7 30:12

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235:9,10 246:1 294:2

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earned 51:3 63:10

earners 76:6

earning 66:3

easier 44:6 278:3

283:15

easiest 226:13

easily 147:3

easing 225:14 290:19

East 14:13

easy 37:20 80:20 117:7

118:10 174:16 296:17

303:9 304:16,18

eat 19:15

eating 277:6

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echoes 302:17

economic 1:3 5:19 6:11

21:8 51:14 55:10

62:21 64:18 86:1 87:9

188:18 205:5 262:21

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economically 27:16

67:14,19 285:11

economics 224:9

262:11 264:22 276:13

economist 2:11 85:16

183:2 216:3

economy 32:4 43:6

47:13,20 70:6,11,12

82:4 87:21

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education 191:14

195:15 279:2,6,16

307:19 308:3 312:13

315:11 316:11 319:10

effect 34:17 67:1

106:21 225:20 239:11

239:19,21 240:5

254:6 255:17 280:12

282:4 284:19 288:9

effective 96:20 108:12

138:5 163:19 254:4

261:15 264:11 295:5

effectively 51:16

263:15 310:12

effectiveness 208:17

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effects 101:14 200:16

217:21 236:22

efficacy 313:11 316:6

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77:15,16 91:21 114:7

152:16 153:2 157:7

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eight 71:10

eight-hour 280:13

eighth 22:6 50:4

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163:13 170:20 183:17

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181:21 203:22

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305:11

keeps 58:2

Keith 2:5 90:2,5 93:2

95:9 295:17

Kelly 2:4 91:15,20

97:19 109:5 131:3

134:12 138:14 144:12

Kelvin 8:19

kept 87:19 224:2

key 38:2,11 49:9 57:18

61:3,8 66:7 122:20

198:15 261:6 272:6

293:13 295:1 296:15

303:2

KeyBank 1:16 6:5,6

36:5 37:17,22 38:2

39:20

KeyBank's 36:14

kick 169:17

kid 322:10

Kid's 60:7

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127:8 249:21 282:10

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323:11

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256:16 280:17 283:1

287:15 292:11

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320:15

knowledgeable 11:22

known 6:18 93:16

99:20 278:18

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L

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labeled 161:19 287:7

315:10

labeling 161:21 287:6

315:12

labor 144:18 145:19

148:21 149:3 168:20

lack 10:22 11:12 74:7

94:19 228:9 291:5

301:20

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lacking 29:11

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lagging 189:13

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landlords 150:14

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166:1 271:16 288:19

288:21

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104:11 108:2 121:12

121:13 179:1 180:8

181:8 197:16 201:19

218:1 273:11 283:1,5

288:4

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116:11 125:7 143:14

149:13 185:3 216:1

217:21 218:1 239:19

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179:4 181:11

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189:1 246:12

Lariece's 226:21

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Latino 17:22 18:1 30:14

33:8 34:9 175:4

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laudable 312:12

Laughter 8:9 10:5,9

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92:13 105:13 110:13

131:4 147:19 148:5

238:19 254:1 276:14

280:16 302:7,9

310:22 311:5 314:5

321:10

launch 53:4

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Lauren 2:18 92:3,8

109:1 122:4,6 131:12

153:22 164:15

Lauren's 139:6

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92:4 122:7,11

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293:10

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34:2 42:10,12 43:18

48:11 69:11 73:7

263:10

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109:21 191:6

leads 37:4 50:9 325:6

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280:19 301:5

learned 10:13 13:13

19:3 81:13 84:10

246:1

learning 77:14 88:21

93:14 167:18 298:19

learnings 97:14

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187:15 226:5 317:2

329:11

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89:13 119:4 316:1

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241:8 262:22

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89:11 123:6 127:5,13

139:22 140:19 141:15

158:3 177:6 182:15

182:22 191:21 206:17

269:1,19 298:15,16

301:19 302:11

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left-most 212:11

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112:5,18 117:8 120:2

125:17,21 127:2

128:13 141:19 147:4

150:15 152:21 167:9

169:13 177:5,10

180:6 181:16 187:21

202:20,21 204:9

217:11 232:9 234:5

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33:22 34:2 46:5,6

47:15 48:1 96:20

98:12 101:14 103:10

104:1,2 115:1 122:20

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233:4 235:11

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lens 292:12

lesser 85:6 195:16

lesson 27:12

let's 119:2 186:8 210:20

215:4 220:8

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47:5 51:19 60:6

100:12 101:21 144:15

145:7,13 173:3

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206:2 208:2 237:19

237:19 238:21,22

255:18 264:14,16

273:8 274:13 310:3,9

levels 12:8,9 75:9

145:14 179:9 185:20

215:21 218:9 235:14

leverage 18:12 53:8

56:20 57:16 96:7 97:1

116:21

leveraged 56:17

leverages 111:10 115:4

leveraging 113:20

118:15 303:3

Levere 1:19 8:15 54:22

55:16 144:5 146:3,8

146:15 147:1,15

148:6 226:4 227:12

228:7,11 275:22

317:6 318:3,6 320:6

321:9,11 323:1,4

325:15 326:12,15

327:5

liabilities 134:1,17

liability 133:14

Libby 1:19 7:1 61:21,22

68:1,10 256:8 257:2,4

257:7

Liberty 1:20 68:16

lie 328:9

life 36:21 37:5 67:2

75:21 132:11 211:17

212:11 242:6,20

243:9 246:8 280:3,8

313:4

lift 102:17 103:5 271:1

278:7

LiftFund 1:14 15:19

17:10,13 18:16 19:7

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215:19 218:18 284:5

lighter 190:4 198:22

200:17

lightning 252:5

likelihood 133:9 162:7

239:12,13

limit 254:6 312:15

limitation 313:6

limited 90:21 103:10

181:18 292:5

limits 38:7 128:12

Lin 191:2,10 192:16

Lindsay 2:8 262:1,3

264:2 276:10 285:22

308:13 312:3

line 117:8 126:15

137:20 160:2,11

161:10,17 177:9,21

177:21 178:7 180:15

180:18 184:2,20

186:9 187:8 209:20

213:20,21 214:2,6

215:16,19,19 216:6

217:1,2,10 218:15,17

218:18 295:9

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lines 113:14 124:10

126:20 147:12 159:20

181:3 214:6 218:5,9

219:8

lingering 46:22

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204:1

linked 39:20 58:16

linking 134:20

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liquid 96:13 145:2,11

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206:1,9,11,18 207:12

207:15,17,18,20,22

208:12,15,22 209:4,7

209:19,22 210:8

211:2,6,8,13 212:4,6

212:12,15,17 213:2,4

213:15,21 214:3,9,18

214:19 215:8,22

216:20 217:17 218:7

219:1,4,7,13,18 221:4

221:9,22 223:20

228:18 229:6 231:19

235:17 240:21 243:12

254:14,21 255:2,5

290:5

LISC 1:18 6:18

list 257:11

listen 263:18

listening 86:3 87:20

164:1 244:15 259:21

289:16

literacy 21:18 40:6,8,17

49:21 50:3 71:5,7

279:6

literally 303:5 324:13

little 29:8,9 36:8 59:4

68:4 88:14,15 103:10

105:11 108:15 111:2

119:3 121:5 124:1

136:11 142:21 143:17

156:22 163:8 171:4

175:20,22 176:4

181:3 189:17 193:2

196:13,16 198:10

201:5 206:18 207:12

207:18 209:4 213:2

216:10 244:10 258:9

265:8 275:15 284:6

285:13 293:5 297:1

298:15 299:2

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70:7 196:9 226:18

242:7 286:21 317:15

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21:21 37:1 44:7 45:7

54:12 56:17 64:18

67:8 280:11 289:17

292:16

living 21:15 37:1,5

199:20 201:15

Liz 81:22,22

LLC 18:18

LMI 179:20,22 180:2

182:16,17

loaded 214:15

loan 16:12 17:2 31:12

33:13 39:9,10 47:14

96:4 100:12 101:21

119:14 122:22 123:14

126:16 128:9 133:10

135:17 180:21 183:19

184:4 186:21 187:10

187:22 188:14 195:14

224:7 225:22 227:10

228:2,6 235:1,2 239:4

239:6,11,20 247:14

247:19,22 249:11

253:18

loan-loss 53:17

loans 18:10 30:18,19

31:8,9,17,18 32:13

33:22 34:6 53:16

82:22 88:10 96:15

108:7 126:2 163:14

165:19 171:16 178:1

178:5,22 179:2,3,6,7

179:7,16,19,22 180:1

180:7,20 181:20

182:16,19 183:21

184:7,7,9,10,22 185:2

185:4,6,12 187:3,6,8

187:12,14,18 188:17

227:20,21 233:16

244:13 247:1,13

250:21 251:6 253:7,8

257:12,22

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26:2,20 47:1 51:19

63:11 200:14,15

262:7 264:16,16

274:13,14,15 275:7

293:9,10,12

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logistics 110:5

London 159:17

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54:17 68:13 86:6,7,7

100:9 131:20 134:9

166:2 174:14 248:20

293:3

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longer 21:16 47:16 67:4

longer-term 324:21

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100:9

looked 56:8 95:8 98:12

98:21 103:18 113:6

116:10 119:17 138:20

139:1,18 185:15

186:2,22 213:6

225:11 241:12,12

254:3 279:4

looking 10:14 20:13

48:13 55:10 56:9

60:14 61:1 72:18

73:11 75:7,7,22 76:2

87:10 101:5 102:4

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351

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130:14 140:1 141:3

142:8,9 143:1 145:18

171:9 189:4,10 191:1

192:5 194:10 197:9

197:10 201:9 213:7,9

227:7 230:2 238:18

242:12 256:15 281:18

295:7,18 299:19

300:1

looks 22:9 117:6

133:20 185:18 188:16

191:3 199:11 200:21

221:12 264:11 293:18

loose 248:4 249:1,2

loosening 188:7 250:20

Los 151:1 275:6

lose 85:2 242:8,8

loses 222:14

losing 30:21 304:6

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224:7 225:22 231:2

231:15,19 241:19

246:9

lost 56:2 86:12,21 87:2

159:15 169:3 268:20

lot 18:7 22:13,21 23:12

26:10 31:1,14 46:21

50:17 52:16 62:22

63:22 65:21 66:8 72:4

74:9 75:14 84:10 87:4

87:5,18 92:16 106:5,6

107:10 108:10 116:12

124:6 125:20 130:2

130:11,15 137:4

140:6 142:18 155:17

155:19 157:21 158:21

161:4,7 163:10 164:2

170:21 178:12 185:1

189:6 198:5,12 202:2

221:2 223:1,9 233:10

239:15 240:3 245:20

256:22 259:5,22

268:11 274:19 278:1

289:18,22 290:4

313:20 315:5 316:8

319:10,14 323:17

324:9 327:13 329:9

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Louis 75:3 77:20

Louisiana 82:8

love 26:12 65:11,15

66:12,17 125:20

130:7 165:17 166:13

227:5 245:3 246:11

323:12

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27:15 37:19 39:5,5

41:12 46:8,18 47:12

56:3 57:16 59:2 61:8

63:3 95:5 107:14

122:12 149:1 158:17

159:2 163:10 179:20

187:19 203:2,5

244:12,12 246:4,20

293:5 302:4 325:13

326:22

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112:20 179:9,11

181:10 183:15 186:16

201:5 203:10 216:18

217:8 220:20 224:19

224:22 231:13 245:12

248:14

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241:16

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213:12,14,18 214:14

215:17,21 217:7,18

220:13,21 230:6

244:12,12

LTVs 251:1

lucky 317:10

lumped 197:13

lunch 173:17,20 260:14

M

Mac 2:3 175:22 202:7

202:19 204:3,5 253:6

Mac's 204:7

machine 14:19 167:18

machines 15:6

macro 197:22

macroeconomic 196:8

Mae 204:5

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mail 281:13 283:3

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199:6 264:22 265:5

267:18

mainstream 5:5 45:8

83:13 90:12 92:19

94:15 134:3 291:6

300:21

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296:17 303:10,13,17

304:5,20

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98:4 192:21 228:3

317:19

majority 90:19 95:14

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327:19

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making 21:15 30:18

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88:10 122:21 167:5

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221:1,1 271:2,10,15

278:17 285:11 286:17

287:8 294:18 306:12

312:21 315:7 322:13

325:3

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malpractice 163:6

man 35:2 63:8

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141:21 156:8 157:17

277:7 292:8

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119:21 262:4,16

263:8 290:7

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manual 96:18 141:1

171:14

Manuel 8:20

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164:7

march 9:13 101:17

329:20

Margaret 1:19 7:1,6

61:20,22 227:6

234:22 243:17 252:8

256:6

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235:2

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Mark 2:17 174:8 176:10

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72:5,8 79:8 88:7 94:7

97:15 98:14,15 100:3

107:20 108:10,11

117:1 137:21 138:1,4

147:9 154:2 155:17

160:15 168:21 174:8

175:8 176:22 178:21

181:19,19 182:11

184:4 185:22 188:11

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264:21 323:16

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95:13 97:11 122:14

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133:1 140:7 150:10

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100:6 101:8 116:8

144:19 145:19 149:7

150:18,19 174:19,20

176:14 177:4 178:18

179:13 181:15 198:4

198:15 200:13 223:22

237:8

marred 107:12

Martha 319:5

Martin 1:15,16 29:16,18

35:21 45:20 59:3

148:9 158:4 162:10

169:6,10 234:14,15

234:16 243:16 256:13

260:13

Martin's 39:12 168:16

252:7

Marty 36:4,12 55:1

61:10 77:11 84:7

89:15

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Massachusetts 60:12

master 252:3

mat 284:7

match 56:3 164:9

310:19

material 12:2 254:8

Mattapan 53:14 54:7

matter 89:20 141:11

174:4 195:15 196:17

196:17 200:6 203:18

228:2,6 240:5 258:11

279:21 321:20 331:5

mattering 199:15

matters 196:11 201:22

228:2,5 240:3

mattress 290:11

Maurice 1:18 6:16

48:21 306:20

Maxine 47:17

Mayor's 11:20

mayors 293:9

MBA 204:13

McCoy 8:19

McDonald 1:20 68:14

68:15 137:2,15

138:14 139:21 142:15

McKinsey 159:16

McWilliams 1:11,12

42:18 44:19 80:12

93:4 110:10,14,19

148:3 184:6,11,14

328:6

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36:18 52:15,22

UNOFFICIAL/UNREVIEWED
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352

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317:7 325:13

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means 33:11 46:15

90:15 98:19 179:13

214:17 263:7 272:3

277:12,21 292:7

325:20

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282:1

measurable 279:9

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191:12 193:9,15,16

194:14 208:22 212:4

212:6 219:12 239:17

247:20 254:4

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96:11 185:10

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145:3,5 182:7

measuring 94:4 139:15

211:19 245:6

mechanical 255:14

256:1

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208:21 214:12

media 171:5 267:21

268:5,8

median 33:10 73:20

74:3 106:3 165:12,12

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186:9,12 244:7

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118:13 154:16 203:2

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15:13 42:16 59:20

88:20 264:5

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8:11 16:22 49:13,16

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282:5,18 283:22

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287:15 288:5,21

314:3 328:8 330:8,20

330:22

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memo 161:17

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318:11

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41:16 77:2,11 83:19

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131:21

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323:20

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283:7 284:14 289:20

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279:13

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105:17,20,22 106:8

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Miller 2:14 9:2,8 13:9

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35:17,21 36:2 42:7

48:21 54:21 61:19

67:22 68:12 76:9 80:8

84:4 89:16 90:1 174:7

174:13 258:8,14

327:11

million 13:4,19 14:22

16:12 18:5,11 30:19

31:9 72:6 78:8 94:21

112:7,9,9,11,13,15

113:2 114:18,18

193:18 274:6 319:21

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218:21 225:9 233:11

282:12 292:5 301:18

305:21

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248:13 326:9

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252:22

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296:20

minutes 9:5,22 13:8,11

15:10,14 49:6 77:5

89:19 113:17 158:3

176:17 223:14 252:10

288:12

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241:22 292:7

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36:14,14 264:22

Mississippi 200:1,2,4

200:11 236:16

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misunderstood 45:6

MIT 160:18

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ML 110:1

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281:20 282:6,13,20

283:11,13 284:2,15

284:16,22 285:4,7

310:5,10 311:8

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167:6,12 168:9

169:20 172:1 247:5

315:6 316:13

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78:5 252:9 280:20

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269:18 270:11 272:9

290:7,11,12,16 291:6

299:16 304:6,18

312:12 314:15 323:9

324:18,19 325:13

326:17 328:13

monitoring 188:11

month 39:17 46:17

127:6,8 209:18

210:16 211:13 245:15

245:16 255:19 269:7

269:16 270:9,20

290:22 311:16

monthly 79:4 96:14

214:15,20 225:1,6,19

287:10

months 57:22 73:12

85:18 90:13 115:20

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231:6 240:14 241:22

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months' 145:6

Morduch 158:15

morning 4:3,4 9:3

20:22 25:9 29:17 42:9

44:20 48:19,22 61:21

80:10 90:7 91:6

174:17,21 260:12

261:12 277:3

mortar 27:14

mortgage 3:14 39:7

58:22 59:8 147:8

165:19 174:8,20,22

175:8 176:14,22

177:1,4,8 180:7

182:10 183:7 186:7

188:6,11,13 194:7,11

194:14,17 195:1,7,11

195:19 196:1 203:16

204:7,15 206:3,10,20

207:8,14 208:1 209:6

209:11,15 210:4,10

211:18 212:1,11

213:12 214:15,20

219:15 220:19 222:6

223:9,10 224:18,19

225:13 226:15 231:2

231:5,16 233:14

234:9,10 239:12

241:13,19,22 243:8

246:15 251:19 255:16

256:4 257:13 258:6

mortgage-ready 198:3

mortgages 35:1 88:10

171:16 209:12 212:3

220:11,13 232:1,4,14

232:15,15,17

mother 30:2 36:6,7

260:15

mothers 30:20 31:7

260:15

motivate 262:11 265:1

272:7 301:22 302:1

302:12

motivated 259:19

302:13

motivating 300:8 302:6

302:19 303:1

motivation 303:20

motivational 296:1

motivations 60:20

mouth 140:18 278:8

move 8:22 22:10 58:3

110:21 132:21 210:22

217:7 263:15

moved 148:14 159:16

295:21

movement 132:1,1

232:1

moving 32:11 47:11,13

48:14 74:2 129:2

134:9 135:6 176:15

MPE 210:19 212:12

213:22

MPEs 210:11,14 212:14

214:2

MSA 192:11 237:19

238:21

MSAs 192:13

multifamily 150:21

multiple 286:3

mumble 229:4

municipal 77:17

municipalities 313:21

Murphy 6:3,7 8:20

37:15

mute 330:15

mutual 136:16

MyPath 1:19 7:2 62:1

myth 299:19 300:14

myth-busting 299:18

N

N.W 1:10

NALCAB 17:21,22

18:19

name 29:17 45:7 61:22

68:15 97:19 261:3

322:17,18

named 206:10

names 121:10

naming 223:3

nascent 170:10

nation's 42:13 261:7

national 2:18 7:13,15

9:12,20 12:2 17:21

18:1,10 30:17 34:10

42:14 44:22 77:10

92:3 122:7,11 150:20

199:1,3,12,18 200:8

200:18,22 201:11

264:14 273:7 281:3

295:14 296:10 329:17

nationally 12:8,20 62:2

Nationals 147:18

natural 202:6 219:20

naturally 292:6

nature 102:22 183:4

212:18 223:4

navigate 75:19

navigating 11:16 13:6

NCUA 17:9

near 248:7 250:3

nearly 26:18 178:6

312:18

necessarily 41:19 63:2

91:2 107:3 126:16,22

127:4 154:22 155:12

164:10 205:17 261:20

277:8

need 16:6 19:5 26:8,13

27:12 34:21 43:3 45:9

45:10 46:11 47:3,5,18

53:3 64:17 68:3 69:6

74:13 75:9 77:1 90:15

123:19 164:1 167:19

167:20 173:22 203:15

265:8 290:7,12,17

323:6

needed 69:12 74:4

needing 300:8

needs 12:17 51:1 203:2

242:13

NegAm 249:20

negative 28:5 70:13

102:6 117:18 120:6

124:11 136:15,19

140:7 143:4 160:1

187:9 237:1 239:18

248:20 297:18 298:9

negotiate 293:20

neighborhood 75:4

149:2

neighborhood-based

50:18

neighborhoods 74:7,8

74:11 75:6 149:1

neither 302:5

nervous 130:12

Nestel 2:15 91:20 109:6

109:6 110:14 111:1

141:6 147:22 151:16

169:10

net 33:4 52:20,21

318:22

network 13:14 14:2,8

15:5 34:22 57:4,7,11

202:13 268:5 274:14

275:6

networks 14:20 23:11

never 33:15 34:11,12

81:12 147:2 234:17

319:4 328:16

new 4:7 5:13 7:21 11:9

11:19 12:15,21 20:4,7

22:19 26:4 38:3,17

39:18 42:4 43:11

44:13 46:9 60:6 61:18

68:16 70:1 73:6 78:14

84:19 93:7 97:1,21

109:18 113:1 114:16

114:21 124:8 138:1

140:2,3 165:9 168:14

169:16 186:5 194:17

194:22 195:1,6,11

196:2 197:11 199:12

199:16 201:4,7,11

203:4 206:6 209:16

226:8 236:16 262:6

268:8 281:4,8 300:2

316:1 319:20 331:1

newborn 312:10

newborns 312:8

newcomers 69:17

news 277:20

nexus 83:5

nicely 270:10

nicer 301:9

nickel 34:21

nineties 140:16

ninth 37:8

No-doc 249:20

no-fee 138:4

no-score 123:18

nobody's 248:11

noise 221:14 270:1

noisy 221:12

non- 66:5

non-bank 177:5,10,18

178:17 179:1 180:6

180:20,21 188:4

257:13

non-banks 176:22

177:11 178:20 179:12

179:19 180:4 181:5,7

181:12 182:9

non-credit 154:20

non-custodial 65:10,13

non-enthusiastic 61:8

non-financial 113:19

non-Hispanic 190:3

195:20

non-mobile 309:22

non-mortgage 194:18

non-profit 97:21 202:13

202:14 205:6 262:8

275:7 330:2

non-profits 15:15

202:15 203:13 263:4

noon 148:8

normal 289:1

normalize 210:3

normalized 210:8

normalizing 288:21

normally 143:10 208:14

norms 282:20

North 54:6

Northeastern 53:12,12

Northern 54:8

Northwest 286:4

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note 7:19 43:21 45:18

126:6 128:16 143:16

152:12 183:5 184:2

254:13 255:3,16

296:21

noted 174:22

notice 106:5 120:5

210:17,22

noticeable 297:3

noticing 27:21 71:15

190:7

noting 184:21

November 122:10

novo 20:2,6

NSF 126:19

nuanced 40:8

nucleus 265:19

nudges 266:5 269:15

number 4:7,9 8:11

25:12,16 28:8,9,21,22

58:22 64:21 71:19,19

94:18 98:9 100:4

108:1 125:9,16

131:12 144:7 152:17

157:2,6,12 162:4

165:2 167:1 184:18

210:10,14 217:10,11

233:8 236:15,22

285:6 287:21 294:10

306:12 309:10 319:15

319:18,19 323:18,21

numbers 31:16 107:3

200:4 225:10 323:6

numeracy 50:3

numerical 206:15

O

objective 139:17

281:10

objectives 7:5 268:11

268:13,15 269:1

307:9

objects 278:5

obligation 133:10 162:7

obligations 154:13,16

154:22

observation 213:13,15

observations 91:3

107:1 130:19

observe 189:12 192:4

192:22 194:13 327:12

observed 194:13 197:5

201:13,16 209:13,16

observing 198:6 213:11

obtain 37:20 132:21

261:17

obtained 118:18

obvious 188:10

obviously 93:6 123:18

124:5 125:11 127:16

128:12 223:21 225:14

226:19 231:11 232:16

233:7 269:15 273:8

297:8 309:14 313:1,5

316:7 320:21

OCC 9:18

occurred 87:11 93:8

177:19 178:13

occurring 93:8

occurs 39:21

OCTOBER 1:7

odd 54:1

odds 117:9

offer 38:12 41:4 96:21

179:10 203:5 204:4

205:15 313:3,10

327:17

offered 79:7

offering 39:18

offers 203:10

office 11:20 25:11

60:10

officials 174:18

offs 143:12

offset 75:2 143:4

offsets 236:20

oftentimes 27:8 35:5

40:6 161:18 286:9

Ohio 238:5

old 119:12 156:4

older 197:6 310:19

olds 65:8

On-certified 295:11

on-ramp 115:15

once 41:10 115:16

166:16 211:3 212:9

240:12 281:19,20,22

282:3 298:11

one- 30:12

one-half 38:6 195:18

one-third 27:8

ones 61:18 106:16

118:7 122:16 130:10

154:4 168:20 269:20

288:5 297:12 304:3

ongoing 45:21 80:18

102:9 262:6 266:4

267:7 322:14

online 80:2 118:20

287:2

Ons 302:18

onset 43:1 86:11

open 35:5 40:22 65:8

130:18 131:1 223:12

297:22 302:1 306:18

opened 13:14 20:8

26:16,18 78:9,10

317:17

opening 3:7 28:5,16

66:3 78:13 92:2

263:16 296:1 300:12

301:18

operate 68:17

operates 68:18

operating 97:15 245:8

operation 17:10 99:10

operations 25:1 150:10

opinion 234:1

opinionated 30:3

opportunities 5:19

23:19 27:20 36:20

49:10 81:21 92:17

275:14 329:7 330:4

opportunity 9:4 14:20

26:21 37:4 51:17,22

57:7 76:16 83:16

162:11 173:20 258:20

261:4 302:15

opposed 136:18 155:19

230:3 289:15

opposite 28:14

opposition 46:7

opt 135:7 150:11 166:3

166:15

opt-in 165:18,20 171:21

172:2

opt-out 166:13 171:21

172:6

optimization 110:8

148:1

option 39:13 118:14

203:6,11,11

options 42:4 133:1

203:14

orange-ish 190:5

order 5:16 51:1 175:15

213:19 219:17 227:16

237:13 241:1 302:8

orders 9:21

Oregon 226:9

organization 48:4 62:1

84:12 86:6 93:12,16

93:20 97:21 98:6

204:22 273:11 313:9

organizations 42:14

50:13,19 84:12 88:14

108:3 265:14 273:13

275:8,11 329:14

orient 298:14 301:13

original 222:8 299:21

315:19

originated 187:6

209:12 212:3

origination 186:7 206:6

209:14 211:10,13,20

213:12 214:11,21

220:1,12,15 221:3,11

230:7

originations 177:8,10

177:12 180:18 182:14

186:7 257:13

Orleans 68:16 73:6

Orozco 8:20

ought 9:6 233:2

outcome 277:17

outcomes 260:5 261:20

290:3

outflows 102:5

outlays 225:2,6,19

outlined 108:22

output 105:16

outreach 40:17 262:4

275:14

outset 4:13 214:8 222:4

outside 113:4,7 115:5

283:8 288:6 318:16

outsized 290:2

outstanding 37:8,9

outstripping 72:11

outweigh 326:19

over-represented

253:1

overall 97:7 98:16

101:20 103:3 109:2

134:4 137:21 138:5

157:5 162:4 180:5

182:10 189:22 299:1

299:4,8 303:7

overcome 72:20 289:19

overdraft 34:4,7 39:13

39:19,21 40:1,2

126:19 160:10,11

161:10,18,19 304:7

304:11,21

overdrafts 159:19

160:6

overhaul 75:10

overlap 123:9

overlay 247:4 252:15

override 172:19 173:2

oversampling 296:11

oversees 262:17

oversight 39:14

overstating 218:10,13

overview 95:10 209:2

264:8

overwhelming 46:7

owed 155:13

owned 20:5,5,12,12

68:17

owner 173:7

ownership 151:2 175:3

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190:1,3,8,12,18,20

191:5,5 192:12 193:4

193:11 195:7,19

196:1 197:7,10

199:22 202:16 236:12

300:2

P

P-R-O-C-E-E-D-I-N-G-S

4:1

P&I 72:12

p.m 174:5,6 258:12,13

331:6

Pacific 286:4

package 283:20 284:13

325:11

packages 283:5

packed 277:1

packet 9:10

packets 297:9

page 101:19 116:15

211:16 213:1 221:18

pages 162:21,21 208:4

paid 119:13 155:12

269:17

paired 318:8

Pam 1:21 7:22 80:9

84:4 243:18 252:8

257:10

panel 35:13 90:1,9

97:14 149:13 151:15

174:8,16 186:5

258:18,21 259:4

260:22 261:2,6,14

327:12,21 328:3

panelist 162:14

panelists 91:7 97:17

173:21 258:5,10

261:21 313:16 323:12

panels 260:1

paper 99:3 191:3 192:7

198:13,14 235:1

238:17 246:18 256:21

283:14

papers 98:9 189:7

paranoid 250:4,18

parent 65:9 307:3

parental 191:17

parents 65:22 66:1

318:19 322:2

Park 20:11

parochial 136:12

part 5:6 17:10 20:2 21:2

36:21 44:12 46:12

55:22 62:5 71:12 80:1

86:7 128:10 130:13

131:19 132:9 140:13

144:1,18 167:13

171:22 183:4 188:5

201:19 215:1 220:19

227:10,14,17,21

242:22 243:6 248:16

264:4 271:9 275:5,16

309:13

partial 99:8

participant 103:21

106:3 139:1 257:16

302:8

participants 44:3 63:9

99:2 103:14 105:15

139:10,12 146:5

participate 150:15,16

261:2 275:21

participates 150:22

participating 116:18

273:11,13 292:14,15

participation 151:4

174:1 261:7 329:22

particular 24:11 41:18

45:18 52:13 63:3 74:8

76:22 93:21 94:1 95:5

98:13 101:7 106:3

108:8 123:17 126:15

137:22 149:2 162:3

163:2 176:20 190:11

191:3 193:16 197:8

199:8 200:19 215:15

220:21 252:19 321:3

particularly 10:19

11:14 24:14 29:20

31:18 40:16 41:12

55:5,22 59:11 84:16

84:18 107:9,20

126:11 136:9 180:8

188:17 224:14 254:4

260:12 295:17 304:10

328:2

partition 235:5

partner 18:17 32:14

60:1 77:21 117:21

151:19 152:13,13,17

159:17

partnered 64:18 77:19

204:3

partnering 16:21 34:16

202:12

partners 48:12 52:4

58:8 76:13 77:21

117:20 204:13 265:14

293:10 294:2,16

partners' 77:16

partnership 26:2,17

53:11 55:4 61:10

262:5 286:3

parts 40:12,14

party 172:17

pass 92:22 138:15,17

292:20

passed 161:13

passing 228:1

passionate 81:15

passport 153:5

Pat 8:19

patch 246:16

Patenaude 1:21 7:22

80:10 257:11,18

path 33:6 64:4

pathetic 57:15

pathway 50:8,17

patience 293:4

pattern 212:9 231:6,8

patterns 182:6

pause 127:15 183:1

292:20

pay 14:7 39:1,6,10

51:20 96:12 126:10

126:18 140:18 319:4

328:14

paycheck 25:19 63:10

270:12

paychecks 64:8

payday 33:22 34:2,6

46:5,6 58:1 126:1

payday-like 46:9

paying 59:7 305:6

payment 33:13,15

34:11 38:18 99:6

109:14 126:1,2

150:12,13 179:11

203:6,10,10,12,15

204:1 206:2,8 207:14

208:2,12,14 209:6

210:4,10 214:19

215:6 217:12,22

218:1 221:1,2 225:7

226:5 231:5,16

235:18 240:21 244:3

244:13 245:11,22

254:15,22 255:1,7,16

256:4

payments 39:9 109:19

115:7,18,19 127:7

156:9 187:10 209:15

214:16,20 242:1

244:18 245:9,13

pays 50:9

peak 32:16

peaks 220:17

Pearce 2:17 174:8,11

174:14 175:18 188:22

223:7 226:2 228:15

229:1 234:7,17 236:1

240:6,9 243:16 252:2

256:6,22 257:9 258:2

peer 168:6,9

pegging 296:12

pejorative 160:9

penalized 58:13

penny 31:10

people's 21:21 22:16

22:16 36:19 54:12

80:6 125:2 163:1

165:3 279:9 292:16

perceive 289:16

perceived 292:14

percent 12:6,11,12

14:10,15 16:3,9 19:6

33:9 38:4,6 53:21

57:21 64:15 73:2,20

73:22 74:2 78:12 79:2

82:19 86:20,21 87:1

97:7 114:14 120:11

120:15,17,19,22

137:19,20 145:10,11

156:2 165:2,4 177:13

178:3,4 179:3,19,21

183:18,18,22 184:19

185:5,6,11 187:6,8

190:1 192:17 194:10

194:12,21 196:7,14

197:15,17 200:5,7

206:16,17,19,19

211:10 214:18 220:16

221:1,2,7,8 222:4,5,5

222:9 224:2 225:7,13

225:17 232:15 239:14

246:16,20 247:1

248:11 251:17,19

254:6 266:16,19

267:3,4 285:4 299:6,8

309:21

percentage 82:10 140:9

190:12,13 214:7,13

215:5,6,18 225:12

288:4

percentages 194:16

percentile 181:2,5

186:17

perception 291:21

perceptions 282:6

297:3

perfect 143:13

perform 138:11

performance 55:10

138:8 188:15,16

251:6

performed 102:12,14

performing 136:18

performs 117:10

period 32:15,22 33:1

53:5 87:6 173:17

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185:9 186:19 188:19

191:2 192:1 193:12

194:8 195:2 197:8

233:20 244:12 246:3

248:20 250:5 254:7

periods 164:5

permanent 191:11,12

permission 30:5 117:4

118:17 119:22 125:7

129:2,6 131:17 153:7

252:2

permitting 34:15

persist 255:17

persistent 282:4 284:21

persistently 280:11

282:1

persists 211:17 212:9

231:6,8

person 19:15 62:7 64:3

82:1 122:21 127:9

232:3 282:21 284:4

322:16,21

person's 67:2 126:18

personal 43:21 94:10

94:15 130:2 132:12

156:7 169:21 270:1

271:7 287:6

personalized 282:18,22

personally 329:3

perspective 62:7 66:17

67:20 79:22 95:12

126:9,11 234:8 263:5

277:21

persuadable 306:3

perturbations 168:20

Petal 2:12 91:11 93:17

125:22 126:5 129:6

Peter 246:17 252:16

Pew 67:11

phase 170:3

PhD 160:18

phenomenon 114:16

philanthropies 316:14

Phillip 8:16

phone 115:6 171:6

286:19 288:12 296:8

297:16 311:10 330:15

phones 116:16 310:6

photo 66:9

phrase 71:7

physical 27:14

picked 184:18

picking 148:16 159:12

picture 97:2 104:21

134:7 135:14 146:6

197:20 198:20 199:12

199:18 200:11,18

201:3,11 227:22

305:7

piece 51:6 52:5,7 54:19

71:20 73:9 75:11

79:15 140:1,2 142:16

193:12 200:21 201:6

205:21 226:21 239:4

239:7 247:7 253:18

273:2 306:22

pieces 71:1

Piermont 20:4,10

pillars 24:13 122:20

pilot 13:15 16:17 77:13

116:18 121:7,16

170:2 208:7,16

221:22 286:19,20

piloting 58:17,22 226:6

pilots 169:15

pin 141:14

pioneered 109:13

pipeline 17:16 22:9

PITIA 214:16

pivotal 114:5

place 22:20 37:4 62:18

74:18 90:8 129:3

132:21,22 139:20

155:19 159:13 189:9

259:6 268:19 284:7

placed 277:15

places 22:22 24:3,3,6

49:16 101:2 103:16

103:17 106:7 236:22

237:8 248:5 277:16

plan 33:19 265:12,22

266:2 287:9 292:10

308:7,9

planes 110:6

planning 21:6

plans 262:14

platform 109:18 268:8

272:22 309:6

platforms 56:16,16

play 45:3 47:2 69:2

79:20 131:15 195:16

234:4 237:16 239:16

244:19 265:15

played 266:21

player 178:21

players 150:20

playing 47:5

please 61:17 92:10

297:13

pleased 6:9 7:6 84:18

176:12 261:4

pleasure 21:1 259:1,4

pledge 262:15 265:13

265:21 266:12,14,16

266:19

plenty 95:15

plot 209:20 212:7

plotting 210:13,14

plug 265:15 275:18

plus 37:17 210:6,6,6,7

pockets 38:4 199:20

point 5:9 23:18 39:12

53:19 61:15 66:20

92:4 127:20 137:16

139:6 141:12,12

142:2,5,13 144:9,22

145:2,3,10,17 153:4

156:2 159:11 160:15

161:12 168:16 178:16

188:10 202:11 214:13

215:15 217:20 218:15

225:12,12 226:16

233:7,19 243:14

245:5 250:15 252:14

254:11 256:4 260:21

289:14 295:13 324:17

pointed 94:18 134:12

327:16

pointing 128:17 186:20

points 120:18,20 121:3

141:7 142:18 162:13

181:12 186:15,17

190:13,13 214:7

215:5,6,16,18 231:10

294:9 306:13

policy 5:21 6:2,15 44:4

44:5 73:16 74:14,16

75:1,10 85:11 98:15

108:21 142:17 200:14

205:6 232:7 233:13

263:12 313:14 321:6

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78:16 86:20 87:1,12

95:2 116:3,11 119:7,8

119:18 120:11,16,18

120:19 121:1 139:2

143:9 179:9 235:6

242:19 307:10 313:1

327:20

populations 123:11,14

128:6,9

port 51:10,12

portal 118:20

portfolio 54:13 72:6

portfolios 262:17

portion 191:22 201:5

position 64:17 67:17

72:21 171:4 259:15

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117:17 120:7 237:1

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288:9 299:1 305:11

314:13,21 315:14

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possibly 30:21 252:19

post 190:8 191:7 193:7

post-closing 175:6

176:5 206:18 207:12

207:15 209:4,7,19,22

221:5

post-crisis 191:2 192:1

193:12

posted 76:7

potential 66:5 67:10

82:14 92:5,20 135:15

150:11 191:19 194:8

221:21 252:15 290:1

potentially 123:16

135:19 155:7 199:22

224:19 225:15

poverty 145:2,4,7,11

power 53:9,13 134:14

154:21 245:1

powerful 130:3 255:22

practical 100:15 156:17

practice 45:22

practices 157:17

187:20 189:9

practitioner 311:19

pre-crisis 177:16 185:8

188:8 191:6 254:7

prearranged 160:2

precedent 170:20

precedes 231:4

predecessor 6:6

predict 166:19 278:19

predictability 164:21

predicted 100:20

predicting 106:8 165:6

243:10

predictions 113:10

predictive 102:1,11

104:10,15 112:1

128:4 134:14 139:5

predictiveness 100:20

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134:18

predictor 140:10

217:14 311:8,11

preemption 173:5,11

preemptive 34:17

prefer 229:4

preferences 218:16,17

218:19

preliminary 119:4

prelude 163:20

premium 165:20 223:20

prep 57:4,15

preparation 202:17

prepared 49:14 51:15

presence 243:12

present 1:12 2:1 98:7

175:12 208:3 263:5

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93:11 137:7 162:15

223:18 249:17 250:14

293:2 297:10 309:20

presentations 61:9

88:22 223:8 243:20

263:19 308:14 330:11

presented 118:14 141:8

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presenters 329:5

presenting 321:4

preserving 5:2

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presumably 232:8

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previously 5:20 6:12,20

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138:20 139:3 153:18

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principal 210:6

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priority 47:9,11

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158:9

private 6:12 26:6 58:7

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privilege 4:5

probability 224:22

probably 53:22 72:21

126:4 129:20 130:10

173:16,21 189:19

218:10,18 222:19

223:5 235:21 237:5

259:3 281:6 288:4

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problems 21:18 93:21

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procedures 143:3

process 4:11 21:7

63:13 87:7 96:18 98:8

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procurement 53:8,13

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produced 229:10

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96:21 97:4,15 100:6

135:20 137:22 138:7

139:3,7,14 140:3

161:8 203:1,22

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95:17 96:7 98:3 99:21

122:15 129:7 130:8

132:9 138:2,20

161:20 168:15 169:16

186:21 187:22 202:20

203:1,16 232:5

248:15 249:11,20,21

273:18 278:12 289:21

290:1 295:11 298:13

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program 12:15 25:14

26:1,4,15,17 27:2

28:1 32:19 38:16 39:1

60:11 64:1,7 163:12

208:7,16,18 222:1

227:3 272:19,21

274:2 279:18 286:5

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programmatic 294:7,9

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programs 25:13,16

56:3 65:1,3 82:21

125:3 248:10 257:21

258:1 263:11 272:16

272:17,19,20 273:3

279:17,22 292:14

293:13 294:12,13,14

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projection 230:15

projects 121:16

promise 116:12

promising 25:6 92:21

122:19 123:16

promote 23:13 44:4

265:16 273:22

promoting 327:14

prompt 280:18

proof 19:11

proper 69:7,8 82:3

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property 73:10 84:19

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proposal 255:8 317:19

proprietary 97:6 125:18

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Prosperity 1:19 55:7,9

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261:13 263:3

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prudential 9:17 58:17

psychology 277:18

public 6:1,1,12 11:6

18:3 26:2 94:3 115:8

189:20 205:1 262:20

311:14 313:13 319:2

public's 5:2

public-private 42:5

publish 301:10

published 205:11

297:11

publishing 98:8 168:6

Puerto 80:19 81:15,16

82:3,9,16,18,20 83:3

83:12,17

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pugnacious 30:22

pull 155:7 308:2

pulled 157:16 179:2

267:12 303:7 304:16

punch 271:20

punt 313:16,22

purchase 38:19 117:14

181:20 182:14 183:17

purchasing 324:20

purpose 9:19 93:19

313:7 315:11,13,15

purposes 156:11

229:12 233:3

pursuing 199:22

pushing 38:11

put 15:5 36:16 37:17

58:14 70:2 71:3 74:21

80:3 122:8 141:13

147:22 148:4 162:11

169:11 184:16 192:7

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221:6,7 222:4,8

239:16 268:9 272:10

283:7,19 284:7

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puts 46:6 67:17 149:4,5

putting 72:16 93:12

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266:17 284:4

pyramid 113:13

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qualified 234:9,10

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qualify 72:5,8 94:14

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quality 217:7 260:1

Quantitative 2:3

quarter 17:7 86:12

quarters 212:20

quartile 231:9,12,14,18

241:16,17

quartiles 231:7

quashed 37:3

question 101:5,13

123:2,4,7,22 126:17

127:17,19 128:3,15

128:21 130:20 134:19

139:22 140:13 141:8

146:1,10 147:13,16

150:8,9 151:11 155:4

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161:9 162:11 164:15

164:16 165:18 166:3

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248:17 251:9 252:9

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129:14,18 130:15,19

131:8 148:11 149:12

156:19 157:2,6,13

175:11,12 193:13

223:12 252:7 256:12

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quick 63:7 121:9

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238:16 252:6,7,9,12

264:8 280:22 308:11

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quickly 9:15 41:14 50:6

131:15 270:22 290:16

293:16 295:12

quintile 145:14

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quote 34:3 35:5 290:9

quoted 144:9

R

race 30:9,11 53:1 54:4

104:6,13 128:5 244:2

244:19 254:12 255:3

256:14,17 310:2

Rachel 158:15

racial 45:13 46:13

55:11 58:21 61:2

195:3 201:17,19,20

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234:14,14 244:20

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raise 92:8 121:8 143:6

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range 42:4 73:2 99:21

100:5,7 117:7 137:19

138:19 147:4 156:7

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ranged 102:3

ranges 103:17 181:5

rank 241:1

ranked 106:16 237:13

ranking 55:11 241:9

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20:20 41:16 136:3

rapidly 129:2 146:20

rate 16:3,9 34:10

122:21 137:10 138:17

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126:2 138:3,22 175:3

176:1 182:20 190:8

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218:7 219:14 221:22

222:7 223:22 231:11

231:13 248:20 250:7

261:16 285:3

rating 37:8,9 303:18

ratio 143:11 247:2

raw 183:8

RCT 226:10

reach 18:8 69:5 101:9

103:14 287:10

reached 32:16 60:6

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reaching 41:20

react 242:5 277:12,19

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59:13 70:21 71:20,22

72:2 73:8,9,14 74:15

120:3 226:18 248:19

249:14 250:6 282:21

283:7 287:12 290:17

291:17 297:2 321:6

321:16 328:21

real-time 109:19

reality 27:14 169:2

300:5

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reason 38:9 72:9 85:4

169:15 217:5 218:22

220:5 243:7 323:8

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233:20

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146:11,19 152:6

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receive 31:11 115:21

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receiving 116:9 132:19

267:6 280:2

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recession 85:19,20,21

86:12 168:13 188:20

191:7,7 248:18 251:5

251:9

recognition 286:7

recognizing 43:2

recommend 235:7

recommendations

132:10

recommending 65:7

reconcile 54:3

reconciling 54:2

reconvene 258:8

record 28:20 89:21

132:4,12 133:17

134:15 148:7 149:19

167:15 174:5 258:12

331:6

records 28:6 29:7 98:21

99:7,7 115:8,8 132:2

135:18 149:17,17,21

recovered 107:12

recovering 124:7

recovery 86:1 189:11

189:12,13,14 193:14

194:8

recruited 319:5

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179:18 180:19 190:5

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reduce 225:22 292:18

reduced 28:2

reduces 241:7

reducing 47:2 206:7

215:5 222:22 254:17

reduction 285:22

286:12

reductions 286:17

reemphasized 158:12

reentering 79:6

reexamined 279:14

refer 111:9,17 210:9

reference 70:22 153:4

185:11

referenced 290:18

294:1

referencing 114:6

referred 71:5 94:5

288:6

referring 44:3 96:10

refine 306:9

reflect 146:16 183:6

reflections 306:16

reflects 205:17 224:21

reform 23:12

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refuge 35:6 165:21

refund 57:19,21

Reg 183:20,20 184:14

regard 23:11 37:6 79:22

regarding 161:9 171:21

218:20

regards 260:11 324:1

registered 57:9

regular 79:3 140:15

266:18

regularly 266:15

regulated 46:2 82:12

152:8,9 172:17

regulation 166:11,20

regulations 68:18

172:20

regulators 9:18 46:12

159:11

regulatory 17:8,11

150:2 152:6 156:17

172:12,13 234:8

rehabilitation 88:11

rehabs 83:1

reimagine 133:5

reimbursement 83:1

reinforcement 22:1

reinforcing 304:17

reinvestment 7:8 9:12

9:20 37:10 41:18 45:1

45:5 329:18

reiterated 324:9

reiterating 323:20

rejoining 298:11

relate 16:5 52:5

related 91:9 93:18

262:18

relation 171:4

relations 2:16 109:7

141:9

relationship 94:13

117:9 162:6 211:17

217:17 256:2 269:9

269:10 296:18 300:7

relationships 23:17

43:17 46:1

relative 207:1 218:17

239:11,17 253:14

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relatively 100:8 107:14

124:8 187:18 203:4

release 121:10

relevant 51:8 64:12

159:13 253:2 280:3

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reliability 164:10,19

reliable 310:2

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remained 33:3

remaining 131:9 293:1

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remarks 3:7,18 92:3

259:7 260:21

remember 142:19

214:22 244:9 280:10

280:13

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remind 9:10 22:14

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287:14

reminders 269:16

reminding 267:8

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remote 27:1 281:4

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renovation 88:11

rent 114:16 150:12

155:4

rent-a-bank 45:22

rental 114:19 150:18

151:2 176:1,2

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repay 91:2 96:3 122:22

123:14 128:8 133:10

135:17 162:7 234:9

repayment 16:3,9

126:14 267:13,16

repeat 27:10 249:7

replace 84:21

replied 270:9

report 25:10 98:12,14

104:20 107:21 124:21

125:15 126:7 133:13

135:11 140:2 153:19

154:17 155:9,15

156:16 165:14 205:10

299:17 303:14 306:11

324:13

reported 90:11 115:19

reporting 125:8 151:22

152:5,9 153:10,11,12

153:15 154:5 156:20

reports 28:14 107:19

123:8 124:22 146:13

309:1

represent 42:10 135:16

reproduce 216:13

request 320:5

required 83:4 134:20

requirement 72:11

151:5 225:22 255:7

requirements 66:8

172:12 219:14

requires 54:19 172:2

research 2:2 5:22 55:5

56:12 58:20 66:21

67:11 70:22 90:3

91:17 97:21 98:4

100:17 112:19 114:7

138:16 144:14 147:7

147:8 169:11 175:13

189:15 191:1 193:13

202:5 204:13,19,22

205:5,7,9,19 206:4

229:10,12 254:5,10

261:22 263:12,14

291:8 292:3 295:1

296:2,12,13 297:5,10

297:12 306:7 313:8

313:17 317:10 320:14

321:16

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researched 313:19

researcher 175:19

researchers 233:9

317:7,13 318:8

reserve 1:14 5:17 53:17

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104:18 206:11,21

219:15 222:6 224:18

225:18,22 243:8,13

254:18

reserves 33:8,17 34:8

103:3 146:12 147:9

210:11 224:7 226:3

245:21 246:5

reside 115:9

resided 113:4

resident 293:11

residential 177:8

180:17 181:20 182:14

183:16

resides 113:7 115:5

residual 123:3 170:17

191:21,22 192:2,15

206:9,20 222:5

254:17

resilience 21:8

resolutions 152:7

resonant 304:12

resonate 64:12 303:22

307:10

resonated 300:22 301:6

resonates 263:20

resource 4:20 203:13

270:13 292:6 313:3

resources 13:4 204:1,9

265:15 268:3 274:17

292:5 316:11

respect 49:9 154:17

218:6,16 252:14

254:11 255:13

respectable 85:12

respectively 195:20

respond 47:19 131:16

134:19 151:15 166:22

respondents 297:1

303:1 304:1

response 160:13 252:6

269:8 326:8

responses 270:6

responsibilities 152:8

Responsibility 6:5

responsible 29:19 48:1

98:3 122:20 124:16

262:3

rest 97:2,2 134:17

135:14

restrict 28:16

restricted 315:15

restricting 312:11

restrictions 203:7,7,9,9

222:11,13 290:19

result 90:13 94:7 199:2

199:3 259:20

results 90:10 91:17

98:7,10 100:12,12

101:15,20 103:21

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170:13 216:11 219:22

220:4,6 226:17,18

236:7,8 260:4 279:19

287:19

resumed 89:21 174:5

258:12

retained 127:22

rethink 145:21

rethinking 296:18

retirement 58:12,16

75:14,19

return 112:17 188:8

returned 115:12

reverse 175:15

reversion 84:20

reviewed 168:7,9

274:10

rhetoric 248:7

rich 35:2 86:18 92:15

100:10 230:3

riches 86:19

Rico 80:19 81:15,16

82:4,9,17,18,20 83:3

83:13,17

rightfully 291:5

rights 1:17 42:11,13

43:15 45:6 153:16

ring 327:4

ringing 116:16

ripped 163:1

rise 77:1 269:22

risk 15:17 16:10 42:1

52:13 53:18 66:17

92:1 97:12 105:19

106:19 113:1,10

119:10 126:7,8 128:4

143:12 165:7 168:14

168:17 182:3,5

183:19 184:17 185:22

187:11,19 217:14

242:19 244:18 251:2

251:3,14 252:18

risk-based 137:18

riskier 232:5,15,16

riskiness 233:16

risking 125:2

risks 53:10 124:13

129:22 150:6 291:17

risky 106:16 187:3

188:1 234:12 249:12

rival 76:13

River 100:16

roadmap 119:5

ROBERT 1:13

robust 174:1 196:3

246:13 268:2 309:4

330:13

robustly 252:19

role 31:15 45:3 47:1

58:20 61:13 79:19

124:15 176:21 195:16

198:9 206:1 234:4

252:13

rolled 281:3

Ronald 8:5

room 1:10,11 76:15

81:22 89:13 130:1

223:11 233:8 275:20

278:22 281:7 309:11

rosy 250:14,17

rotating 38:7

Roth 58:14

roughly 112:12 137:20

213:6 214:7

round 252:5

rounds 296:10

route 53:7

routed 287:16

row 10:7 106:12,12,15

164:13

rows 105:18 106:22

Roxbury 53:14

rule 34:1 73:22 142:20

234:9

rules 47:1 68:18 172:10

run 14:20 46:2 84:13

169:9 186:14 188:2

329:11

runner 110:20

running 25:13 125:7

288:20

runs 77:7

rural 24:3,5,7 41:16,20

257:15,20 296:9

Ryan 2:11 8:15 175:19

176:8 216:3 228:17

231:21 234:12 247:22

257:11

S

sad 80:14

safe 7:16 25:15 56:5

65:12 77:13 79:7 84:2

98:3 122:14 153:8

163:13 232:7 272:10

272:14 273:4,17

274:1 293:20 327:2

327:13,14,17

safeguards 166:18

safely 27:16

safety 72:20 233:3

234:4

Safeway 63:11

sale 181:21

salute 45:20 47:22

sample 193:17 207:7

209:9,21 211:6,10

212:20 213:5 220:1,2

220:3,6 221:11 231:7

241:14 296:12,13

samples 220:11

San 1:15 12:22 25:9,11

25:14 26:5 28:2 54:8

62:2 64:6 319:16

sandwich 63:11,18

sat 89:5

satisfying 162:8

Saunders 2:18 92:3

122:5,6 153:9

save 27:4 38:18 39:4

57:9,21 84:2 101:18

109:2 217:13 262:12

265:1,8,9 267:8,19

270:10 272:8,21

290:10 318:11 322:21

328:13

saved 258:15

saver 266:4,11,13

267:6 271:4,12

saver's 322:19

savers 265:7 266:12

267:1,19,22 269:9

270:2,21 271:11

272:15 322:15 324:5

saves 2:9 262:3,4,7,14

264:6,8,19 265:12,21

268:19 271:13 272:16

273:8,9 274:6,15,16

275:5,18 306:21

307:3,5 308:17,19

309:2 322:16

saving 27:20 56:18

62:17 64:13,15 67:13

209:17 262:13 265:10

265:17 266:1,8,15,17

267:8 271:12 273:22

274:18 275:12 280:6

308:6 318:11 323:18

324:4

savings 5:2 7:4 26:1,15

26:16,22 27:6 38:21

38:21 39:20,22 56:3,4

56:5,7 57:20 58:9,10

58:12,15 59:3,4,7

60:2,4 65:13 67:2

116:22 210:2,5,15

218:20 219:1 221:8

226:15 262:14 265:3

265:12,21 266:3,7

267:11,14,17 269:6,7

269:18 270:4,8,11,19

273:16 274:2 285:21

286:12,16 287:7,19

287:21 290:20 291:2

300:1,10 305:6

307:12 308:4,7,18

309:5 312:5,8 313:11

313:17,22 314:13

316:6 317:12,17

318:15 320:3 321:19

322:18 323:21

saw 93:21 120:11

198:22 215:1 243:17

281:21 287:21

saying 17:9 35:3 48:12

163:20 165:22 166:1

169:6 204:18 226:12

242:7 245:14 276:7

326:22

says 16:10 17:4 28:12

164:10 179:18 291:7

SBA 42:5

scalable 288:15

scale 18:8 64:19 163:12

scaled 239:17

scaling 58:18

scanning 136:2

scarcity 292:2,3,11

scenario 166:19 200:2

schedule 110:9 147:17

147:18 286:14 330:8

scheduled 89:17 210:3

Schneider 158:16

school 6:1 26:3 50:1

52:1 60:18 66:10

159:7 162:20 319:8

322:4

schools 279:18

science 54:15 263:1

278:18 289:5

scientists 141:8

score 16:1,1,10 90:14

92:1 94:20,21 95:11

97:6 103:17 111:8,9

111:10,10,12,21

112:1,2,3,6,11,17,21

114:22,22 115:2,11

115:13,14,17,21

116:9,11,20 117:2,6,6

117:7,9,9,10,22

118:22 119:8,18

120:4,12,15,16 121:1

121:19 124:11 129:17

133:6,6,7,20 138:12

140:3 142:3 152:2

161:2 165:12,13

180:16,20,21 181:9

181:11,14 182:3

186:6,13,17 195:12

230:11 241:6 244:21

UNOFFICIAL/UNREVIEWED
(202) 234-4433 Washington DC www.nealrgross.com

Neal R. Gross and Co., Inc.

361

249:18 250:22 300:11

301:15

Scorecard 55:7

scored 302:4 304:6

scores 16:7 25:21

28:13 29:11 73:1

101:1 102:21 103:7

104:19 107:14 114:9

120:21 123:8,11

124:2,3 145:16 165:3

180:10 186:10 194:2

217:9 228:5 229:12

229:21

scoring 3:11 109:11

111:4,15 112:12,16

134:8 136:21 165:9

scoundrel 166:1

scoundrels 35:6 56:1

64:9 165:22

scourge 46:5

screen 268:9 274:5

296:16 330:14

scrutinize 169:18,22

170:5

season 57:6 309:15

seat 10:7

second 17:6 23:8 44:19

50:21 52:5 63:12 67:3

71:12,20 81:15 95:6

98:11 101:5 107:20

114:5 115:2 129:16

130:18 135:1 140:13

150:9 178:16 195:9

213:1 218:22 257:12

259:2 265:13 276:21

280:9 286:20 296:16

300:14 320:5 326:7

326:14

secondary 72:5,8

secondly 163:8 207:16

208:18 291:3

seconds 204:20

Secretaries 8:2

Secretary 5:21 6:13,20

6:21 8:1

section 44:5,12 95:2

132:14 213:10,20

sector 56:19 58:8 59:13

163:1,12 166:11

262:8

sectors 6:12 94:3

262:20

secure 272:10,14 273:5

273:18 274:1 299:16

Secured 38:2

security 5:1 129:22

151:17 153:1 282:9

seed 77:21

seeds 21:20

seeing 22:22 23:21

64:15 107:19 108:16

136:19 183:11 184:5

189:11 197:6 247:11

247:18 330:21

seeking 79:11 119:14

265:9 269:8

seen 15:18 26:12,14

52:18 72:2,14 88:1

93:14 94:2 120:4

138:10 142:1 148:22

180:13 182:11 189:6

189:19 246:19 249:17

267:16 276:5,11

segment 295:19

segmenting 295:18

segments 179:8

segregate 241:14

segregation 199:19

selected 81:8 259:16

selection 89:3 120:1

151:7,7 220:7

Self-Help 1:15 29:18

30:6 166:5

self-regulatory 157:7

selfish 275:18

sell 130:6

send 39:1 269:15 283:1

322:4

sending 41:10 268:10

270:3

Senior 2:11,15

sense 15:6 43:8 63:2

67:9 99:12 101:8

102:8,22 103:3

108:14 148:15 228:8

241:5 292:9 301:11

312:7 326:20

sensitive 132:12

sent 16:5 281:5

sentences 271:21

separate 54:18 106:18

227:15 235:1 240:1

separately 100:13

104:8

separating 242:22

series 190:2 325:21

serious 142:13

serve 5:10 18:13 43:20

44:11 47:6 48:17

49:11,17,22 51:18

71:12 72:1 78:16

80:12 108:11 132:5

171:11 179:13 180:9

served 5:20 6:12,20

300:17

serves 8:3 137:22

service 4:11 6:9 8:17

39:22 133:19 261:13

servicers 204:9

services 10:22 22:17

34:22 40:13 43:12

50:15 65:1 98:1 99:22

108:5 132:6,20

156:22 157:4 202:17

servicing 211:21

219:22 242:14

serving 7:19 8:1 100:5

101:7 163:10 181:16

session 85:10 287:5

sessions 286:18

set 67:2 91:13 96:5

99:10,19 100:16

112:10 115:7 125:5

133:11 208:8 222:2

226:13 255:1 272:10

272:11 279:15 287:13

287:13 320:12 327:22

sets 50:3 113:4 115:14

171:3,9 224:16

setting 93:20 206:9

215:7 223:3 286:16

287:7

settings 62:14

settled 21:11

settlement 290:15

setup 208:12

seven 94:3 174:22

321:18 322:9,11

severed 172:7

shame 288:18

share 9:5 13:7 15:7,14

17:18 19:22 53:9

62:22 63:7 70:3 80:16

80:20 91:16 133:18

135:10,18 138:9

142:15 149:22 150:16

153:6 170:2 177:9,14

177:16,18,19 178:1,5

178:10,17 182:16,19

183:16 184:16,22

188:4 194:18 211:6,8

220:11,13 257:13

259:17,21 262:10

263:13 264:5,7,9

266:10 267:10 268:17

269:2,11 273:1

274:17 280:22

shared 10:13 166:4,6

166:17 250:22 260:11

260:13 269:12

shares 178:20

sharing 135:8 166:12

268:16 295:2

sharply 178:2

She'd 30:4

sheet 9:11 133:14

Sheila 4:15

Sheridan 317:10

shift 182:8

shirt 30:21

shoes 277:1

shop 71:6

shopping 74:9,9

short 60:2 87:6 100:8

256:11,11

short-term 299:15

305:5,5,16 324:17

325:4,6,22

shortening 271:19

shorter-term 300:13

shortfalls 40:3

Shoshana 162:16

shot 166:2

shout 55:3

show 99:9 148:7 158:22

160:20 180:11 181:4

198:16 211:6,7 214:1

216:15 231:1 307:19

317:3

showcase 261:15

showcasing 264:15

showed 43:1 52:20

197:19 219:21 236:8

236:9,16 237:10

303:19

showing 28:19 103:1

106:1 178:8 182:2

184:19 190:2 213:18

213:20 216:14 220:7

220:10,11

shown 43:22 67:5

107:8 149:5 287:19

shows 37:7 113:9

141:19 142:7 177:9

177:17 178:19 179:15

181:1,17 182:16

183:16 186:6 187:3,5

268:11 276:5 284:16

301:14,16

shrink 110:20 219:8

235:14

side 14:13 29:5 103:2,2

104:1 108:9,14,16

133:14 147:7 155:14

157:9 168:4 210:18

314:7

sidecar 58:10

sign 105:8 108:13

signed 39:21 46:19

224:2 282:21

significance 288:2

significant 142:1,11

UNOFFICIAL/UNREVIEWED
(202) 234-4433 Washington DC www.nealrgross.com

Neal R. Gross and Co., Inc.

362

149:9 214:5 238:5

299:3,6

significantly 28:1

201:21 216:1

silos 56:22

similar 95:9 104:17

105:19 132:11 149:19

182:6 192:9,15

199:11 200:21 201:10

207:2,3,4 208:9,10

230:7 254:10 287:4

319:16

similarly 180:1 237:9

239:16

simple 79:5 172:4,6

265:21

simply 35:6 74:12 80:3

112:20 164:6 247:14

248:19 285:17

Simultaneous 110:18

232:21

sincere 261:19

single 30:20 31:10

37:13 46:16 95:16

103:21 107:4 126:2

162:19 164:13,13

165:20 167:7 171:15

177:7 180:17 181:19

182:13 183:16 242:3

site 318:15

sites 57:5

sitting 205:4 276:6

situation 25:22 58:4

103:8 192:2 194:7

195:17 196:5 197:22

situations 198:22

285:21 287:16 288:7

six 57:22 73:12 81:7

94:3 99:20 212:13

245:20 290:21

sixth 50:4

sixties 140:16

Sixty 14:10

size 16:12 94:6 217:12

283:3 289:6

sized 208:14

skepticism 171:8

skill 50:3

skills 50:9 51:8

skip 221:17 271:8

slack 292:16

slaves 70:6

slice 211:21

slide 100:18 108:22

111:18 117:19 141:18

142:7 199:2 221:17

257:12 299:20

slides 93:11 176:11,16

183:3 227:13,16

297:8

sliding 215:16,18 217:1

slightly 102:20 208:11

219:8 254:17 255:1

277:15 292:12 306:5

320:13

sliver 196:10

slop 217:2

slope 215:1 218:4

slower 87:7 108:15

slowest 86:1

small 6:11,13 14:5,14

15:3,21 16:14 18:2,7

19:4,7,12 20:3 23:14

31:18 33:20,21 52:9

82:22 98:12 99:4

100:2,3 103:6 108:2,9

146:20,21 155:11

163:16 171:16 178:9

206:8 211:2 235:14

236:15,22 239:15

240:4 253:10 303:5

small-dollar 92:18

95:19 96:20 163:14

smaller 24:12 108:9

178:9 208:11 214:19

239:21 255:1 258:1

smart 289:19

snapshot 211:21

219:22

social 171:5 262:10

264:21 267:21 268:4

268:8 277:18 282:19

323:16

socioeconomic 196:10

soft 51:7

softened 31:4

software 99:6

solely 34:17

solicitations 281:6

solid 184:2

solution 30:15 109:14

315:17

solutions 61:5 109:21

109:21 110:8 288:15

289:7

solve 93:21 157:8

somebody 62:15 166:6

265:22

someone's 28:20 133:9

162:7

somewhat 114:16

244:8 255:14

soon 9:15 56:7 163:22

290:18 311:15,19,21

sophisticated 99:17

sophistication 21:19

22:8

sorry 128:17 173:1

229:3 284:2

sort 76:16 94:2,15

131:19 140:20 151:6

161:9,17 172:4

180:13 183:2 185:3

224:3 230:1 233:1,14

233:15,16 235:5,16

236:2 238:9 241:8

244:2 249:8,14 250:4

276:12 287:14 294:11

296:12 299:17,18

300:1 301:4,12

302:15,19 303:5

304:14 310:6 311:10

312:7 315:22 316:18

324:21

sorts 95:17 102:7 130:7

154:18 156:9 161:20

171:9

sound 112:17 187:21

233:3 239:14 240:4

soundness 72:21 233:3

234:4

sounds 123:21

source 23:3 31:12

100:10 102:17 145:8

155:14

sources 99:3,5,7

100:14 101:3,12

102:16 112:22 155:20

South 23:19 30:8,11

54:7,9

Southeast 22:20

sown 21:21

space 22:12 105:9

108:6 109:15,22

110:1 111:3 115:1

143:15 147:4 150:5

151:20 156:18,22

157:22 329:8,14

Spanish 204:4 296:7,11

297:21 299:10 304:11

306:5

sparse 113:22

speak 97:14 131:10

143:16 172:1 252:13

speakers 89:3 296:11

299:10 304:11,13

306:5

speaking 55:13 60:3

110:18 232:21 297:21

special 24:5 248:10

267:13

specializes 288:17

specific 133:22 134:19

141:7 155:4 196:17

206:5 274:18 286:10

300:20 305:17 307:7

315:7,10

specifically 44:3 96:22

97:22 111:3 176:20

267:20 272:3 295:19

297:6 313:18 321:22

specifics 131:16

specified 314:14

spectrum 51:14 222:19

speed 290:15 293:4

spelled 324:14

spend 24:4 53:21,21

189:3 204:20 288:12

329:1

spending 127:9

spent 22:21 50:17

127:14 189:6 292:1

327:12

spike 220:16

spikes 221:9

spinning 80:16

spiral 58:1

split 216:17 270:10,15

272:11,21 273:1

320:2

spoke 13:14 148:20

spoken 59:17 89:12

sponsored 9:17 158:15

sport 52:2

sports 110:7

spot 120:14 127:3

232:5

spread 15:1 107:17

183:17,19,22 184:19

185:6

spreading 146:19 156:1

spreads 185:3

squares 298:16

squint 221:13

St 75:3 77:20

stability 21:13 262:9

272:18 293:11

stable 107:13

staff 35:15 55:4,5 69:11

75:12 88:22 263:11

294:19 306:13 330:7

stage 128:22

staircase 277:14

stairs 276:22

stake 330:3

stakeholders 9:16

136:13 295:9 330:1

stand 106:1 313:13

standalone 247:4

standard 77:11 139:17

152:18 233:22 284:14

290:20

UNOFFICIAL/UNREVIEWED
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Neal R. Gross and Co., Inc.

363

standardized 95:22

standards 66:16 79:14

79:22 112:20 136:17

187:19 188:7,18

327:14

standpoint 5:11

stands 257:14

stark 201:12

starkest 199:17

start 10:2 12:21 18:5

20:4 36:13 52:8 60:12

64:13 85:19 98:18

106:18 116:19 124:13

136:1 151:16 175:16

177:6 189:16 193:7

202:10 204:18 205:15

241:22 264:1 318:16

323:15

started 4:15 11:19

23:16 30:6,18 31:8

64:6 75:12,17 81:20

85:16 88:2 93:16,20

97:22 113:3 114:20

116:16 245:12,20

269:20 281:18,22

282:5,11 298:17

318:8

starter 283:11,19 284:1

284:17,19 285:13

starting 5:9 9:9 56:10

61:4 94:5 295:13

297:18 300:2 325:20

starts 106:13 143:18

startup 19:9

startups 263:2

state 6:19 46:3,5 55:15

84:16,20 85:8 172:21

173:3,10,11 188:10

199:20 200:7,9,19

201:15 237:18 238:22

292:4 319:20 320:2

stated 36:14

statement 133:16

161:14 235:17,19

256:10 271:8

statements 96:10

133:12 134:13 270:6

states 46:4 55:11 71:10

86:20 87:1 94:12

202:4 236:9 237:13

237:15 293:22 319:18

319:19 327:17

Statewide 226:9

static 193:5

statistic 266:22 267:1

269:12

statistical 162:2 167:5

229:16 288:1

statistically 299:3,6

statistics 19:10 69:8

266:9

status 104:13 191:15

192:20 283:16

stay 35:10

staying 261:1

steadily 182:21

steaming 278:10

steep 215:1

step 44:11,13 65:14

131:15 283:20,21

292:9 300:13 317:13

319:7 325:5,21

Step-by-Step 204:7

steps 60:11 169:9

170:10 287:3 305:6

stick 105:11

stigma 288:18

stigmatized 289:1

stop 74:18 188:21

223:6

stories 177:3 291:19

story 63:7,9 81:13

114:6 190:17 200:2

200:22 211:5

straight 37:8 167:14

221:17

stranger 7:10

strategic 2:8 21:6 262:2

strategies 7:2 204:14

260:4 261:15 262:5

264:10 266:6 268:16

269:22 271:22

strategy 271:9 295:21

stream 62:19 164:5

streamed 11:6

street 1:10 50:15 63:16

116:15

strength 24:10

strengths 56:20

stress 304:19

stressed 24:14

strict 9:21

strikes 139:8

strong 119:9 235:19

stronger 217:16

strongly 235:7 281:15

301:17

struck 24:2 144:12

structural 291:16 296:3

structure 41:8 59:10

139:3,14 299:19

306:1 325:19

structured 289:22

299:17

structures 100:6

138:20 139:7

struggling 24:8 76:18

316:10

student 39:10 47:14,15

160:17 195:14 227:7

227:10,20,21 228:2,6

235:1,2 239:4,6,10,20

247:7,16,18 253:17

256:12

students 48:6

studied 32:19

studies 124:17 191:1

241:13 279:4,15,19

310:15

study 19:6 52:19 99:2

100:11 103:11,13

107:6,15 123:22

126:13 128:16,18

144:1,8 146:9 147:12

247:17 253:5 278:21

288:17 311:1,6

316:12 317:3

studying 104:19

stuff 16:5 235:10

238:14 257:8 305:8

329:6

subconscious 278:15

subject 80:7 141:10

172:18 173:5

submit 172:8

subprime 35:1 138:3

165:19 251:20

Subsequently 319:5

subset 281:22

subsidies 292:19

substantial 182:1

substantially 178:17

181:10 186:13 196:13

216:6,7

success 11:21

successful 27:3 76:2

261:20 266:7 279:16

279:22 299:14

successfully 79:6

278:7

sufficient 166:2

suggest 42:3 140:21

150:4 309:10 328:7

suggested 254:3

suggesting 147:9 285:8

316:12

suggestion 89:2 243:5

suggestions 240:12

suggestive 183:13

suggests 180:5 217:16

231:17 242:1 254:5

254:10

summarize 201:17

218:4 227:16

summary 304:15

summer 25:17 76:2

98:11 294:12

Suncoast 16:20

super 136:6 216:9

231:21 238:1 243:11

243:13 255:4

supplied 154:7

supply 47:2 183:7

supplying 69:6

support 1:18 6:17 7:14

12:1 36:19 43:22

64:17 66:13 73:4

118:2 128:11 186:3

269:5 275:8 286:21

287:12

supporting 77:9 294:6

supports 62:17 64:1

67:15 68:7 263:6

266:5 267:6 286:8,10

292:18

supposed 41:3 176:15

surface 284:5

surprise 242:10 298:10

surprised 197:16

199:13 310:14

surprising 111:21

220:17 297:19 298:4

305:10 310:18

surprisingly 113:12

281:7 299:11

Surveillance 162:16

survey 90:10 266:11

295:14 296:17 302:22

304:9 324:10

surveyed 297:22

300:22

surveys 266:10 296:10

296:11,22 297:17

survivor 83:4

survivors 82:7 83:21

suspect 109:9 126:3,12

310:7,8 330:5

suspected 28:18

sustain 7:14 33:11

282:2

sustainable 3:16 47:8

51:2 176:6 258:21

285:12

Swagel 8:16

sweet 120:14 127:2

232:5

sweetheart 63:16

switch 285:8

switching 283:17

system 5:3,5 7:4,14

11:17 17:8,8 27:17

28:16 44:2 47:4,16

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52:1 78:17 79:6 82:12

83:5,22 84:20,21 85:4

94:16 134:3 135:7

138:21 141:1 150:1,3

156:13 157:4 161:6

167:14 171:12 261:8

296:4 298:8,11

300:15,16 325:5

systems 28:7,7,9 29:4

40:11 66:2

T

TA 19:4

table 10:2 38:16 57:3

68:20 74:21 79:8

131:2 162:12 169:3

173:19 294:2 314:7

tackle 285:21 324:2

tag 162:3

tailwind 88:15

takeaway 187:15

279:20

takeaways 223:1

taken 179:7 189:9

256:5 266:12,14

takes 161:5 226:18

259:6

talk 27:12 29:13 45:2

55:21 63:1 68:2 73:19

77:5 78:5 83:19

107:18 109:1 111:2

113:17 118:6 119:2

121:21 165:18 166:14

174:19 175:20,22

176:4,13 178:14

191:9 195:8 196:16

205:11,22 207:10

209:8 216:9,18,19

219:9 244:3 260:9,14

260:16 264:7,12

268:11,13 272:1

275:12,12 312:20

320:20 324:3

talked 11:11 60:15 68:6

86:10 142:6,18

163:10 174:22 189:7

218:14 220:2 221:19

286:1 291:9

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60:17 75:5 78:15

88:20 93:17 106:14

106:21 119:3 133:21

143:9 149:7 165:9

167:4,17 175:5 184:7

185:19 189:3 217:8

226:3 227:6 231:22

232:2,13,18 234:12

254:13 260:9,17

282:5 292:2 306:13

311:3 322:9

talks 119:6 125:15

162:22

tangible 278:4 300:20

305:17 328:21

tank 84:13 85:9 205:1

tap 222:15

target 223:3 306:22

targeted 23:13 204:14

265:17 307:4 313:2

targets 272:20,21 307:5

task 69:18 174:16

taught 261:18

tax 51:4 57:4,6,8,14

58:3 73:8,9 84:19

309:15

taxes 71:22 72:2 73:14

210:6 225:20

Taxpayer 57:7

tea 278:10

teach 19:15 76:3,4,4

280:4

teaching 75:13

teacup 278:9

team 52:2 54:16 94:10

94:11 275:9 295:17

teapot 278:9

tease 228:12

teaser 143:19

tech 76:22 78:20

technical 19:2 53:16

262:6 263:12 274:20

techniques 137:4

167:18 171:14

technological 78:22

technologies 91:5

92:19

technology 96:6,17

98:1 157:9 282:9

300:17 310:20

Ted 8:18

telco 114:8,10,15 115:4

tell 10:1 121:10 140:8

144:10 155:10,12

164:4,9 192:6 198:13

211:5 328:9 330:16

330:18

teller 27:7

telling 102:19,21

270:18

tells 211:12 277:4

temptation 313:4

ten 4:15 18:5,11 20:7

31:9 32:2 71:10 87:15

87:16 121:2

tend 198:3 323:18

tends 217:7 241:21

tens 94:21

tenth 30:13 50:6

tenuous 149:3

term 8:11 21:16 67:4

100:8 131:20 134:9

266:13

terminology 82:3

terms 4:9 8:20 29:6

40:17 101:19 103:9

104:6 116:3 121:11

138:8 142:9 144:21

145:14 149:16 155:21

156:19 157:16 166:8

208:17 209:8 211:14

218:19 223:3 225:13

226:12,19 231:17

241:18 248:9 249:18

250:15 285:9 288:1

298:17 301:15

terrific 9:16 89:18

terrifying 163:3

Terwilliger 8:5

test 50:2 104:17 164:20

196:2 222:1 230:11

tested 286:17,19

301:12 303:6 324:14

testing 59:9 226:11

288:16

tests 240:18 288:20

Texas 201:12,16 238:4

text 267:2,21 268:1,10

303:5

texting 277:7

thanking 4:11 5:9 55:1

thanks 15:11 25:8

29:14,15 36:4 38:15

42:8 79:7 80:11 93:2

122:2 174:15 176:12

258:5 292:22 308:13

theme 113:15 185:16

272:5,12 302:18

303:16 307:21

themes 23:1 289:8

295:5 301:12 303:14

304:15

they'd 283:16

thicker 124:2 138:12

thin 90:20 94:6,22 97:8

107:10 120:13 123:18

138:10 142:7,7 159:6

159:6 194:3

things 4:6 6:22 11:5

16:7,18 21:11 23:20

23:21 25:5 26:9,11

34:19 38:14 39:15

49:20 50:22 53:6

55:22 56:15 59:1 65:6

67:16 69:4 71:3 73:3

74:20 77:15 87:6

89:13 96:14 98:19

99:5,12 102:4,7

104:21 105:22 107:5

107:8 127:15 137:8

151:17 154:18 155:22

157:13 158:14 166:22

179:6 185:15 186:1

188:13,19 190:6,9

191:8 192:18,19

196:20 223:4 224:8

225:1 235:13 240:4

246:1,7 249:19 259:9

259:11 271:2 276:9

276:16 277:6,22

280:10 282:8 283:4

284:4 286:11 294:11

296:15 300:21,22

302:16 304:7 305:13

306:13 318:1 320:16

320:17 324:22 329:9

thinks 159:3

third 45:15 51:5 59:13

79:1 98:14 113:18

170:22 172:17 212:20

231:4 247:11 283:22

thirdly 219:11

thought 32:3 80:6

82:13 90:8,9 116:12

134:10 149:15 150:6

223:19 224:10 233:10

243:17 246:13 264:1

282:7 283:17 314:9

319:3 326:7 330:10

thoughtful 130:14

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173:15 306:16 311:20

thousand 247:10

284:16

thousands 82:2 136:14

152:13

threat 45:18 47:12

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three 9:17 19:8,9 20:3

34:5 39:8 49:12 70:19

72:22 78:7 87:2 88:2

100:17 120:6,8 121:9

145:6 161:3 162:19

175:9,10 178:21

179:18 207:14 209:6

209:13,15 210:21

211:4,20 212:14,20

214:1,2 215:2,9

218:12 223:7 236:13

240:22 241:21 244:3

244:17 245:9,13,17

245:18,19 255:17,20

298:17 303:8 323:12

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248:9 290:8,8

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63:22 81:7 210:19

212:13 259:3 321:18

322:9,11 330:16

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today 16:3 25:3 35:2

46:18 54:17 70:7,8

73:6,19 90:2 92:20

93:5 95:17 96:21 97:8

98:11 109:15 110:9

112:7,12 114:12

116:9 124:14 129:4

129:20 141:11 161:1

171:15 174:19 176:13

198:15 205:11 229:11

259:14,18 261:6

264:1 280:7 292:2

293:17 294:11 300:10

308:15 309:11 330:10

330:11,20

today's 223:22 264:5

toehold 316:10

told 63:9 232:12 266:17

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tone 305:11

tool 38:17 115:17

127:21 128:3 152:22

203:20,20 289:10

tools 54:13 265:15

274:17 296:17 304:16

304:17 306:12 311:18

311:19 327:22

top 106:6 113:12

179:18 180:15 190:2

194:9 213:21 267:11

267:17 282:12 302:10

302:21 305:21 307:12

308:4,5

topic 93:6 134:11 136:7

147:14 175:6 252:13

topics 167:2 176:19

263:5 299:15

total 120:10 208:20

241:14 253:22 254:3

285:6 287:22

touch 41:14 80:19

81:14 330:7

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91:22 95:11 97:4

101:1,1,11 102:12,21

103:6,17 104:19

105:17,20 106:17

107:14 115:16,17

134:15 135:4 138:13

146:13 155:14 161:6

171:2 279:5 300:16

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training 12:16 22:5

316:11

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105:2,5 155:6,8,10

158:22 159:20 161:18

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66:18

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163:13 290:15

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270:11 287:13 315:4

315:5

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194:15,19,22 195:1,6

195:14,17,19 196:1

198:7,21 199:7

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197:4,7 239:12

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translate 151:8 261:20

279:11 280:7

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322:20

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164:17 165:10,16

168:2

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167:20 305:22 325:19

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trapping 34:6

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traveling 22:22

Treasurer 25:10

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treat 128:7 154:6

treatment 206:7 208:10

222:2 224:14 284:12

284:22

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66:4 69:6 81:18 82:6

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trends 174:8 178:13

183:6,8 250:22 289:9

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trial 287:20 315:2

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triple 72:2,4 126:2

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124:12

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235:21 298:2 299:9

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228:13 271:15

truncated 244:10

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291:3,5,11 312:10

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truth 79:1,5,13 183:20

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26:6 49:2 58:10 70:15

120:2 130:7 157:8

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248:10 252:8 256:10

258:9 260:4 269:22

276:9 281:8,11 282:3

283:11 293:4

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27:16 30:15 36:19

38:15 42:2 51:11

52:12 63:6 71:3,6

72:19 75:3 76:21

78:16 79:13 134:21

141:17 149:9 158:11

165:11 242:13 246:14

248:12 249:4 250:15

252:17 260:16 271:7

281:17 308:1 320:22

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TUESDAY 1:7

tunnels 292:6

turn 9:1 88:2 176:7

193:2 326:4 330:15

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Twelve 85:18

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Twitter 268:7

two 7:5 8:2 13:11 21:7

22:21 26:11 28:15,22

38:4 49:12 55:9 56:15

61:15,16 74:20 79:15

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100:2,4 101:3 102:16

103:11 106:8 111:18

114:13 116:7 117:20

118:5,7 120:5,10

148:10,11 162:19

164:8 197:22 207:9

214:1,1 215:4,6,16

217:11 225:1 245:17

249:4 253:7 265:5

270:5 279:15 280:13

289:5 296:10 299:14

314:12,22 320:2

329:20

two-thirds 78:19

195:18

type 50:9 75:20 95:15

104:3 143:7 208:16

208:18 224:13,17

271:4 305:19

types 37:16 38:12 41:4

41:11 102:3 108:1

182:6 263:15 279:8

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typical 26:22 229:17

typically 161:5

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U.S 77:22 94:13 95:2

122:1 156:3 279:17

296:8

U.S.-based 161:4

ultimate 157:19

ultimately 162:6 246:2

UltraFICO 116:20

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295:15,19,20,20

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305:1 317:1 323:7,8

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317:1 323:7

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295:15

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40:10 63:5 94:5 112:6

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149:10 167:20,21,22

180:14 193:6 236:3

280:19 281:17

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40:20 43:16 69:1

93:13 144:16 236:5

238:13 295:3 308:16

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97:3 135:5,12 137:6

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18:18 91:5,13,18

95:16 99:13 108:19

118:13 122:18,19

123:1,3 125:11 136:9

139:19 143:1 144:20

145:21 154:10,16

156:6,14 158:1

170:18 171:13 172:9

185:20 187:20 188:7

229:17 235:22 248:5

248:6,9 249:1,2,18

250:10,20 251:14

underwritten 138:10,13

undocumented 66:1

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unemployment 195:10

198:1 200:3,5

unequal 199:9 200:3

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Unfortunately 141:10

279:10 283:6

uniform 152:18,21

204:6

uniformly 153:13

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16:19,20 17:12 29:19

121:12 166:5 281:1,2

282:22 285:12 286:15

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16:15,19 29:21

169:12 274:11 286:4

297:2,4

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United 86:20 87:1 94:12

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University 288:16

317:9

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115:1

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Update 3:9,14

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199:20 233:12

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34:17 40:21 49:10

56:17 59:2 67:16 76:5

76:17 82:3 91:21

92:17 95:16 96:2

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104:4 110:1,4,7

113:22 118:11 124:13

124:18 125:10,18

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139:16 140:11 141:13

154:20 157:3 159:9

160:10 169:21 172:9

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207:6 214:11 215:12

217:12 220:1,3,5

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253:5 261:18 265:14

266:13 267:18 274:16

278:7,9 282:16

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313:4 315:7,18

320:14 323:17

useful 105:8 146:14

148:18 155:6,15

205:7 242:15 243:3

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usually 138:2 146:18

155:22 165:4

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utilities 125:4 155:5

utility 114:8,10,15

124:20,21 125:3

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utilization 124:9

utilize 29:7 173:6

utilizing 141:1

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vaguely 282:12

valid 247:4

valuable 4:20 329:2

value 5:11 74:8,10

103:7 104:15 147:6

222:14 269:3,4 280:5

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191:19 198:1 229:14

229:18 244:22 245:2

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variation 139:8 200:12

238:5

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103:19 196:7 275:10

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327:19

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108:18 156:10

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232:4

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161:10 171:21 175:4

182:9 191:21 222:7

222:11 241:18 266:18

video 271:18

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244:5

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146:18 158:13,21

209:1

volume 330:16

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226:16

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265:2

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192:16

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48:20 85:15 312:1

317:22 319:14 321:11

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260:10

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288:17 293:22

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330:13

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78:21 84:2 86:14

108:6,12 109:9

124:20 125:6 134:21

135:5 136:1 143:13

143:20 144:6,16

145:13 149:9 152:2

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215:10 224:12 226:13

229:16,16 230:1

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247:20 255:6,6 256:9

270:15 273:6 276:19

277:11,22,22 278:14

279:2,9 287:7 289:21

290:1,2 294:21,22

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309:12 312:14 314:21

316:15 317:14 319:8

321:4 325:2

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66:13 72:19 88:3,17

98:2,20 125:18

155:13 156:14 280:18

292:15 294:10 315:9

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31:20,22 32:2,20 33:5

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274:7 275:18 308:19

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Weicher 1:22 8:16 84:6

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306:8,17 312:4 331:1

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137:7 165:3,5 168:1

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166:12 304:22

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52:20 87:12 165:15

180:18 190:3 196:12

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197:18 198:20 201:22

white/Hispanic 195:6

197:15,17 202:1

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195:20 197:2,3 200:7

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180:9 261:17

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58:11 290:22

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wonderfully 328:10

wondering 89:11 160:7

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word 49:10 169:14

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232:3 295:22 303:21

304:2,3 314:12

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22:13 26:11 29:3,18

40:11,11 42:18 43:7

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52:11,15,16 54:1,3,4

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58:19 59:14 60:4

61:11 62:8,11 63:1

65:11 69:2,22 75:9,21

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123:15 124:17 147:14

151:12 154:2 172:15

191:9 193:2 225:11

237:8 256:22 258:6

272:13 284:3 288:14

291:12 293:8,9 294:3

295:13,18 302:16

304:9 313:18 315:5

328:19 329:6,13

330:9

worked 7:7 57:19 58:7

77:20 81:3,10,11

101:6 154:4 281:1

289:10 295:16 317:11

317:20

worker 51:2 159:18,18

workers 7:3 26:7,7

158:20 272:19 307:5

307:17 308:3

workforce 23:10 24:10

49:18 51:6 62:14

294:13

working 10:12,19 12:13

22:4 25:4 42:14 47:20

50:13 53:7 58:4,9

60:21 62:2 64:10 77:8

81:1 83:7,16 86:4,8

100:15 111:5,14,20

122:13 140:17 147:5

157:8 203:12 204:10

204:12 262:20 264:17

271:22 272:3 273:3,7

293:19 294:4,16

workplace 7:3

works 15:2 45:7 89:9

91:12 115:3 118:11

152:2 157:14 224:15

328:9

workshop 280:13

workshops 279:5,8

world 54:1 85:10,22

87:22 88:1 99:4

113:20 133:11,17

137:5 144:7 170:22

276:13 283:7 310:17

315:4

worlds 54:18

worried 83:9

worry 63:16,19 127:16

250:13

worse 31:21 32:6 71:16

worst 85:20,20

worth 52:20,21 129:8

131:21 145:6 184:21

185:21 186:20

worthiness 197:12

worthwhile 326:17

would've 81:12

wouldn't 85:2 302:1

322:3

Wow 319:22

wrap 172:4 173:22

188:3 264:15

wrestle 77:5

wrestling 80:4

write 259:7 266:2

wrong 32:11 127:11

129:5 166:10 168:15

168:16 169:4 232:4

318:21 319:8

wrote 138:15 149:18

X

X 210:14 213:19 301:16

Y

Y 210:12 213:17 301:14

year 8:21 12:14 15:19

UNOFFICIAL/UNREVIEWED
(202) 234-4433 Washington DC www.nealrgross.com

Neal R. Gross and Co., Inc.

368

17:7 37:7 39:4,7,10

55:10 58:3,4 60:7

65:8 73:1 78:9,10

85:2 86:17 98:6 111:6

121:17 122:9 192:8

273:12 274:2 317:16

year's 320:1

yearly 309:8

years 4:15 15:19 16:12

19:3,8,10 20:3,7

22:21 30:6 31:4,9

32:3,7 42:22 43:10

48:18 52:19 55:2 56:2

57:3,12 67:12 69:19

70:9 71:15 72:3,7

79:15 84:8 85:16,17

85:18,18 86:11 87:3

87:15,16,21 88:3,13

89:14 93:9,17 94:4

96:6,16 97:18 100:9

104:19 111:7,20

119:12 137:6 142:19

151:20 156:4 159:7

162:19 163:9 164:3

170:18 182:12 186:11

190:21 209:14 211:20

245:19 246:6 250:6

255:18,20 262:19

280:14 302:19 312:6

312:19 329:20

yellow 187:8 302:13

York 7:21 11:9,19 12:15

12:21 20:5,7 186:5

199:13,16 201:4

236:16

young 7:3 62:7,9 63:5,8

64:3,5,10,15 65:18,21

67:1,15 68:2 95:3

118:3 120:13 121:14

142:7,7 158:17,18

272:19 306:22 307:5

307:15,16 308:3,6

311:4

younger 299:10 306:5

329:7

youth 7:7 25:17,18 62:6

62:9,13,13 63:3 65:5

76:3 272:20 294:12

Z

Z 183:20 184:14

zero 32:21 33:10 39:12

148:15 236:21 240:2

244:7 248:20 250:6

Zorn 246:17 252:16

Zuboff's 162:16

0

06 185:12 187:6

1

1,200 162:21

1.3 239:13

1.5 183:17,22

1.8 114:17

1:23 174:6

10 71:15 176:17 179:3

250:6 251:17

10,000 253:8

10:27 89:21

10:45 89:22

100 30:19 31:9 72:6

73:2 74:2 148:14

275:7 321:20

100,000 32:9 267:22

1033 132:15

109 3:12

10th 181:4 186:16

11 86:21 186:12

110 74:2

12 42:22 43:10 48:17

55:2 72:3 85:16,17,18

90:13 163:9 186:12

246:4

12.5 225:11,12

12.9 143:18

12:11 174:5

12th 329:21

13 72:3 178:4 262:19

14 62:15 299:8 328:11

147 3:14

15 53:21 65:8 71:15

89:19 137:19 159:7

187:8

15-minute 89:17

15,000 268:6

150 75:6

150,000 32:9

15th 269:16

16 63:10 65:8 307:6

17 65:8 192:17 299:6

17th 1:10

18 12:6 65:19 68:3

274:3,7 307:4 312:11

1870 70:5

1893 84:22

19 44:5,12

1930s 86:2

1989 111:8 114:9

1990s 109:13

1st 60:11

2

2 206:19 215:17 222:5

2,000 273:12

2,300 13:21

2.0 66:15

2.25 225:13,17

2.5 183:18 184:19 185:6

2.6 109:15

2:41 258:12

2:50 258:9

2:51 258:13

20 32:7 57:21 82:19

86:19,22 120:17,20

121:2 151:20 156:4

187:6 211:10 221:1,7

223:14 248:11 254:16

200 33:4,10 42:14 57:5

112:7 141:14 148:14

244:7 279:4

2002 233:19

2003 186:8 233:19

2005 32:14 185:12

186:18 187:6 249:19

251:18

2006 186:18

2007 86:16

2008 177:15 246:3

250:3

2009 177:15 212:4

2010 86:17 177:15

178:3 186:12

2012 32:14,16 33:4

194:6,12 209:12

2013 212:5 213:13,16

220:14

2014 211:22 212:8

213:8 219:22 279:4

2016 114:22 193:20

209:12

2017 78:6 192:17 193:1

266:11 273:9

2018 78:5 97:22 116:13

175:21 177:12 178:4

179:4 180:22 185:6

186:18 194:6,14,17

194:18 200:6 279:14

2019 1:7 111:6,13

189:22 190:15 274:7

2020 9:11 121:18

275:19 329:21 330:8

204 14:12

21 81:4 221:1,7 312:10

22 1:7 179:21

22,000 293:21

24 62:15 68:3 307:6

246,000 274:5

247,000 52:21

25 13:15 15:19 16:12

41:4 112:12 185:11

251:19

25,000 12:14 16:13

268:6

250 140:10

258 3:16

25th 181:1

26 137:19 190:13

28 112:15

29 266:19

3

3 206:18 222:5 254:16

318:20

3.5 214:7

30 32:3 64:15 111:7,14

111:20 137:6 142:8

142:14 165:4 190:12

204:20 246:6 288:12

30-day 140:15 141:3

30-minute 286:19

30-year 39:6

300 16:12 39:4 79:4

117:7 141:14 148:14

300,000 209:10

31 179:19 267:4

328 3:18

331 3:20

35-plus 30:6

36 293:21

37 12:10 178:3

4

4 3:7

4,000 57:6

4:00 331:6

40 14:15 57:11 120:17

120:18 145:10 181:12

285:3

40,000 26:19 32:21,21

140:8

400 120:7 140:5 141:7

141:14,16,22 142:3

144:6,11 148:12

162:21 267:1,3

269:13 273:13

401(k)s 75:15

43 241:15 246:16 247:1

253:6 254:6

45 94:20 112:9

47 70:9

49 197:17

5

5 206:16,17 213:1 222:4

222:8 225:7 254:16

271:8 283:10 285:12

323:20

5.6 239:18

5.8 193:17 194:20

50 33:9 186:15 253:7

293:22 319:21 327:17

UNOFFICIAL/UNREVIEWED
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Neal R. Gross and Co., Inc.

369

50,000 32:13 268:1

500 265:4 274:6 312:9

321:21

500,000 212:1 213:7

50th 122:9

51 145:11

529 26:22

53 112:9,11 113:2

550 1:10 73:1

56 177:13

590 16:2

6

6 55:2

60 142:8,14 197:14

266:16

600,000 78:9

6010 1:11

620 248:11

636 181:6

64 190:1

66 86:16 196:6

660 142:20

670 181:2

680 116:10,11 120:22

248:14

69 267:3

7

7 254:16

70 12:12 97:7 120:11

165:2 186:17 274:15

700 117:10,10

700,000 60:7

710,000 262:12 265:11

717 180:20

72 78:12

755 181:11

75th 181:2

765 181:2

77 19:6 120:22

78 120:15

79 196:13

792 181:6

8

8 31:17 52:22,22

8.6 200:7

8.9 193:18

8:45 1:10

8:50 4:2

80 73:20,22 77:8 114:18

220:16 221:9

81 119:12

850 117:7

87 79:2

88 86:15

9

9 3:9 112:1 221:18

9,000 109:16

9.8 200:5

90 82:15 220:17 246:5

294:6 309:21

90-day 142:9

90th 181:5

95 156:2 214:14 220:17

246:20

96 16:2,9

99 220:18

9th 329:20

UNOFFICIAL/UNREVIEWED
NEAL R. GROSS

COURT REPORTERS AND TRANSCRIBERS 

1323 RHODE ISLAND AVE., N.W. 

(202) 234-4433 WASHINGTON, D.C. 20005-3701 www.nealrgross.com 

C E R T I F I C A T E

This is to certify that the foregoing transcript 

In the matter of: 

Before: 

Date: 

Place: 

was duly recorded and accurately transcribed under 

my direction; further, that said transcript is a 

true and accurate record of the proceedings. 

 

 - 

Court Reporter 

370

Economic Inclusion Adv. Comm.

FDIC

10-22-19

Washington, DC

UNOFFICIAL/UNREVIEWED