[House Hearing, 116 Congress]
[From the U.S. Government Publishing Office]
EXAMINING THE USE OF ALTERNATIVE
DATA IN UNDERWRITING AND CREDIT
SCORING TO EXPAND ACCESS TO CREDIT
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HEARING
BEFORE THE
TASK FORCE ON FINANCIAL TECHNOLOGY
OF THE
COMMITTEE ON FINANCIAL SERVICES
U.S. HOUSE OF REPRESENTATIVES
ONE HUNDRED SIXTEENTH CONGRESS
FIRST SESSION
__________
JULY 25, 2019
__________
Printed for the use of the Committee on Financial Services
Serial No. 116-42
[GRAPHIC IS NOT AVAILABLE IN TIFF FORMAT]
__________
U.S. GOVERNMENT PUBLISHING OFFICE
40-160 PDF WASHINGTON : 2020
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HOUSE COMMITTEE ON FINANCIAL SERVICES
MAXINE WATERS, California, Chairwoman
CAROLYN B. MALONEY, New York PATRICK McHENRY, North Carolina,
NYDIA M. VELAZQUEZ, New York Ranking Member
BRAD SHERMAN, California PETER T. KING, New York
GREGORY W. MEEKS, New York FRANK D. LUCAS, Oklahoma
WM. LACY CLAY, Missouri BILL POSEY, Florida
DAVID SCOTT, Georgia BLAINE LUETKEMEYER, Missouri
AL GREEN, Texas BILL HUIZENGA, Michigan
EMANUEL CLEAVER, Missouri SEAN P. DUFFY, Wisconsin
ED PERLMUTTER, Colorado STEVE STIVERS, Ohio
JIM A. HIMES, Connecticut ANN WAGNER, Missouri
BILL FOSTER, Illinois ANDY BARR, Kentucky
JOYCE BEATTY, Ohio SCOTT TIPTON, Colorado
DENNY HECK, Washington ROGER WILLIAMS, Texas
JUAN VARGAS, California FRENCH HILL, Arkansas
JOSH GOTTHEIMER, New Jersey TOM EMMER, Minnesota
VICENTE GONZALEZ, Texas LEE M. ZELDIN, New York
AL LAWSON, Florida BARRY LOUDERMILK, Georgia
MICHAEL SAN NICOLAS, Guam ALEXANDER X. MOONEY, West Virginia
RASHIDA TLAIB, Michigan WARREN DAVIDSON, Ohio
KATIE PORTER, California TED BUDD, North Carolina
CINDY AXNE, Iowa DAVID KUSTOFF, Tennessee
SEAN CASTEN, Illinois TREY HOLLINGSWORTH, Indiana
AYANNA PRESSLEY, Massachusetts ANTHONY GONZALEZ, Ohio
BEN McADAMS, Utah JOHN ROSE, Tennessee
ALEXANDRIA OCASIO-CORTEZ, New York BRYAN STEIL, Wisconsin
JENNIFER WEXTON, Virginia LANCE GOODEN, Texas
STEPHEN F. LYNCH, Massachusetts DENVER RIGGLEMAN, Virginia
TULSI GABBARD, Hawaii
ALMA ADAMS, North Carolina
MADELEINE DEAN, Pennsylvania
JESUS ``CHUY'' GARCIA, Illinois
SYLVIA GARCIA, Texas
DEAN PHILLIPS, Minnesota
Charla Ouertatani, Staff Director
TASK FORCE ON FINANCIAL TECHNOLOGY
STEPHEN F. LYNCH, Massachusetts, Chairman
DAVID SCOTT, Georgia FRENCH HILL, Arkansas, Ranking
JOSH GOTTHEIMER, New Jersey Member
AL LAWSON, Florida BLAINE LUETKEMEYER, Missouri
CINDY AXNE, Iowa TOM EMMER, Minnesota
BEN McADAMS, Utah WARREN DAVIDSON, Ohio
JENNIFER WEXTON, Virginia BRYAN STEIL, Wisconsin
C O N T E N T S
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Page
Hearing held on:
July 25, 2019................................................ 1
Appendix:
July 25, 2019................................................ 41
WITNESSES
Thursday, July 25, 2019
Evans, Lawrance L., Managing Director, Financial Markets and
Community Investment, U.S. Government Accountability Office
(GAO).......................................................... 10
Girouard, Dave, CEO and Co-Founder, Upstart Network, Inc......... 11
Johnson, Kristin N., McGlinchey Stafford Professor of Law, Tulane
University Law School.......................................... 8
Rieke, Aaron, Managing Director, Upturn.......................... 6
Wu, Chi Chi, Staff Attorney, National Consumer Law Center (NCLC). 5
APPENDIX
Prepared statements:
Evans, Lawrance L............................................ 42
Girouard, Dave,.............................................. 54
Johnson, Kristin N........................................... 57
Rieke, Aaron................................................. 74
Wu, Chi Chi.................................................. 80
Additional Material Submitted for the Record
Lynch, Hon. Stephen F.:
Written statement of the Cato Institute's Center for Monetary
and Financial Alternatives................................. 96
Domino: A Blog About Student Debt............................ 99
Written statement of the Financial Data and Technology
Association of North America (FDATA North America)......... 102
Written statement of FICO.................................... 105
Written statement of VantageScore............................ 107
McHenry, Hon. Patrick:
Written statement of the Cato Institute's Center for Monetary
and Financial Alternatives................................. 111
EXAMINING THE USE OF ALTERNATIVE
DATA IN UNDERWRITING AND CREDIT
SCORING TO EXPAND ACCESS TO CREDIT
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Thursday, July 25, 2019
U.S. House of Representatives,
Task Force on Financial Technology,
Committee on Financial Services,
Washington, D.C.
The task force met, pursuant to notice, at 10:02 a.m., in
room 2128, Rayburn House Office Building, Hon. Stephen F. Lynch
[chairman of the task force] presiding.
Members present: Representatives Lynch, Scott, Gottheimer,
Lawson, Axne, McAdams, Wexton; Hill, Luetkemeyer, Emmer,
Davidson, and Steil.
Ex officio present: Representative McHenry.
Also present: Representatives Green, Himes, Porter;
Gonzalez of Ohio, and Hollingsworth.
Chairman Lynch. Good morning. The Task Force on Financial
Technology will come to order.
Without objection, the Chair is authorized to declare a
recess of the task force at any time.
Also, without objection, members of the full Financial
Services Committee who are not members of this task force are
authorized to participate in today's hearing consistent with
the committee's practice.
Today's hearing is entitled, ``Examining the Use of
Alternative Data in Underwriting and Credit Scoring to Expand
Access to Credit.'' I now recognize myself for 4 minutes to
give an opening statement.
I want to thank everyone for being here at our second
Financial Technology Task Force hearing. Today's hearing will
focus on the use of alternative data, the financial and
nonfinancial data that is not traditionally used by national
consumer reporting agencies in credit underwriting. With an
estimated 26 million consumers lacking in any credit history,
and another 19 million with an outdated or short credit
history, lenders have looked to other means of assessing the
creditworthiness of applicants.
As a result, alternative data has become a hot topic. It
has the potential to expand credit access but also raises
concerns over the nature and sources of its data points. There
is also significant regulatory uncertainty surrounding its use.
Today, we will hear testimony and discuss questions on all of
these issues. The promise of fintech lending has been to lower
costs and bring new consumers into the market. This promise has
been fueled by data points outside of the traditional factors
used by underwriters like payment history and credit
utilization. Today, lenders use an array of financial and
nonfinancial data in their decision-making. Some factors, such
as utility bill or rent payments, resemble traditional factors.
Others, such as living in public housing, who your friends are,
and what their credit scores are, your ZIP Code, your reading
choices, educational attainment, educational institutions, and
driving habits or online shopping habits, are a significant
departure from traditional factors.
We know that Facebook has up to 52,000 data points on each
of its 2.7 billion daily users, and they sell access to that
data to its advertisers. Use of this and other data can
potentially help 45 million Americans who might have trouble
accessing credit with traditional factors alone. Take for
instance, a 28-year-old woman in a modest-paying job, or maybe
with 2 very modest paying jobs, who has never had a credit card
or taken out a personal loan or mortgage loan. She might find
herself denied access to credit based on traditional factors,
even though she is working 12 hours a day. But a lender using
alternative data might take into account that she went to a
reputable school, had a job with a reputable employer, and
always pays her rent and utility bills on time. In that case,
they might approve her application for credit. It is very
likely we have one or two staffers working here on Capitol Hill
who fit that exact description.
However, it is not hard to imagine a similar scenario with
much different results. Say, a young man with a decent but
short credit history might be right on the cusp of being deemed
creditworthy by traditional factors. However, a lender using
alternative data sees in his rental history that he moves
frequently, moves around a lot. In the last few years, he has
had several domiciles. They may also see he doesn't have a
college degree and that his Facebook friends have below average
credit scores. So, they deny him access to credit.
Unfortunately, this probably describes a number of our military
personnel as they repeatedly move domiciles as a result of
multiple redeployments during their careers.
Without question, there are instances when using
alternative data in credit underwriting has potential positive
impacts. However, right now, oversight of its use is either
highly fragmented or completely nonexistent, leading to
uncertainty for lenders and potential harm for consumers. That
is why we are here today, to better understand how to harness
the benefits and mitigate the harms of using alternative data.
I look forward to the testimony of our witnesses and the
discussion of our Members. With that, I now recognize the
ranking member of the task force, the gentleman from Arkansas,
Mr. Hill, for 5 minutes for an opening statement.
Mr. Hill. I thank the chairman. I appreciate you convening
this hearing and I appreciate our witnesses appearing today. We
are grateful for your advice and counsel today. Analyzing the
use of alternative data in the marketplace lending industry is
an important sector within our broader study of the fintech
ecosystem. I am pleased that we were able to bring everybody
together and do a deeper dive on this topic.
Marketplace or fintech lenders are categorized through
their digital or online focus and have recently emerged and
grown quite a bit over the last decade. According to S&P
Global, marketplace lending grew by 30 percent in 2017. They
provide unsecured credit to individuals and working capital to
small businesses. They have unique funding models with
financing provided by investors, credit facilities,
securitization, and, of course, balance sheet cash.
Typically, these lenders currently lend through two primary
models: a bank partnership model, in which a bank originates
the loan, which is generally sourced and served by the
marketplace lender; or a direct lender model, in which a
marketplace lender acquires the applicable regulatory licensing
in all of the States of our country.
To help determine a borrower's creditworthiness,
marketplace lenders often use some form of alternative data,
hence the topic today. Traditional lenders typically have used
FICO scores, 3 years of tax returns, payment history for credit
cards, mortgages, or student loans in order to establish a risk
profile for their borrowers.
However, marketplace lenders robustly combine FICO scores
with alternative data points to better gauge a borrower's
character and economic situation. Examples of these data points
include education level, employment status, utility and rent
payments, et cetera. Analyzing these data points has the
potential to widen the universe of borrowers and provide
greater access to affordable credit.
Importantly, a report by TransUnion outlined that lenders
that utilized alternative data were able to lend to an
additional 66 percent of borrowers in current markets and 56
percent in new markets.
Today, we will explore concerns about how alternative data
can best comply with critical fair lending requirements, which
will be discussed in more depth. However, I do want to remind
my colleagues that we don't want overregulation to stifle
innovation and prevent the American consumer from now being
able to access affordable credit through this new methodology.
As to compliance obligations, obviously, I want to
highlight some of the ongoing issues that have been evolving
within the marketplace lending industry. The Treasury report--
which I regularly reference in these hearings--released a year
ago now, provides a comprehensive review of the fintech sector.
It has a robust analysis of this industry. It generally favors
innovation, but identified certain important policies that need
to be highlighted and discussed today, such as codify the
valid-when-made doctrine, codify the role of the bank as the
true lender of a loan that is made, allowing the testing of new
credit models and data sources for financial institutions, and
think through this issue of special charters or harmonization
of this process across our States.
The report also mentioned the third-party lender guidance.
I know the FDIC and the OCC have been focused on this due to
the rise of marketplace lenders and strong bank partnerships.
As a former community banker, I well understand the compliance
responsibilities around vendor partner, due diligence,
onboarding of new partnerships, and board of director reviews.
Also, as a result, as a banker, I understand the importance
of banks maintaining a robust level of safety and soundness and
constantly facing changing technology but assuring a vigor in
compliance on both data security and privacy.
I look forward to hearing the thoughts of the panel today,
and over the years, I think this is going to be a fascinating
way for Mr. Lynch and I to make recommendations to our full
Financial Services Committee on how we can broaden marketplace
lending. So, with that, I would like to yield the balance of my
time to my friend, the ranking member of the Full Committee,
Mr. McHenry of North Carolina.
Mr. McHenry. Thank you. And, look, technology is creating
new pathways for more consumers to access the financial system.
That is a good thing. We are talking about people who are
otherwise credit invisible or lack enough credit history to
finance things like a mortgage, credit cards, or other loans.
Alternative data draws on nontraditional sources of financial
history, including bill payment history in areas like mobile
phones, that are now essential ingredients, with 81 percent of
Americans owning a smartphone at this point or using a
smartphone, and rent. And by harvesting this type of data about
the consumer, lenders have a more holistic picture about the
consumer to whom they are lending.
Yet, this new era is not without its challenges. We need to
ensure that alternative data remains nondiscriminatory and that
consumer data and privacy are protected. So, it is our job to
ensure responsible innovation continues to be a driving force
of the American economy, but in particular, in financial
services. I yield back.
Chairman Lynch. The gentleman yields back.
The Chair now recognizes the gentleman from Georgia, Mr.
Scott, for 1 minute for an opening statement.
Mr. Scott. Thank you very much, Chairman Lynch, and let me
commend you and Mr. Hill for providing this excellent
bipartisan leadership on what I refer to as the thrilling new
frontier. We are in a situation now where technology is moving
at such a rapid pace, and where we need to look at where we
need to adjust the sails and make sure everybody has an
opportunity to be able to participate in this. And, of course,
as we look at this scoring technology, we look at how it is
impacting our financial system. There is no group that needs
our help more than the 26 million Americans who have no credit
history. There are also 19 million Americans who have a very
limited credit history. And when you put the totality of the
unbanked and the underbanked in there, we can see that we must
not leave these parts of our population behind.
So, I am looking forward to this, to making sure that we
determine effectively how data is used in lending decisions and
credit scoring, so all the American people can participate in
this glorious new frontier.
Thank you, Mr. Chairman.
Chairman Lynch. I thank the gentleman. On behalf of this
bipartisan task force, I want to welcome our distinguished
panel. I would like to welcome the testimony of: Chi Chi Wu, a
staff attorney with the National Consumer Law Center, based in
Boston, my hometown; Aaron Rieke, managing director at Upturn,
which is a nonprofit focused on promoting equity and digital
technology through research and advocacy; Kristin Johnson,
McGlinchey Stafford Professor of Law at Tulane University Law
School; Lawrance Evans, Managing Director of Financial Markets
and Community Investment at the Government Accountability
Office; and Dave Girouard, founder and CEO at Upstart, which is
a fintech lender focused on direct-to-consumer loans.
Witnesses are reminded that your oral testimony will be
limited to 5 minutes. And without objection, your written
statements will be made a part of the record.
Ms. Wu, you are now recognized for 5 minutes to give an
oral presentation of your testimony.
STATEMENT OF CHI CHI WU, STAFF ATTORNEY, NATIONAL CONSUMER LAW
CENTER (NCLC)
Ms. Wu. Mr. Chairman, Ranking Member Hill, and members of
the task force, thank you for inviting me to testify today. I
am testifying on behalf of the low-income clients of the
National Consumer Law Center. We have heard several times today
that there are tens of millions of consumers who are credit
invisible. The topic of this hearing, alternative data, is
often promoted as the solution. The thing is, alternative data
includes lots of different types of data used in lots of
different ways. Some types of data and uses can be helpful;
others can hurt. As we say, the devil is in the details.
The number one consideration for alternative data should be
consumer choice. That should be the touchstone for all data
collection. Now, we have heard with respect to the Equifax data
breach a repeated complaint: Hey, none of us gave Equifax
permission to collect our data.
Let's get this right with respect to alternative data.
Let's make sure it is the consumer's choice, that consumers
make knowing and affirmative decisions to allow the use of this
data, and the data is only used in the ways that consumers give
permission for and expect. Another consideration for
alternative data is whether it is used to create second-chance
scores for just credit-invisible consumers or whether it is
dumped wholesale into traditional credit reports where it might
damage the records of consumers who already have a score. We
want to give credit-invisible consumers a chance to be seen
without hurting any of the nearly 200 million consumers who are
already visible.
As for types of data, bank account transaction data has
shown a lot of promise, but it is also a juicy target. Debt
collectors would love to get ahold of it. And bank account data
can include sensitive information, such as where a consumer
shops. There should be appropriate guardrails for sharing bank
account data.
Rent payment information is another type of data looks
promising, specifically when no additional late payments are
reported. But we don't want to penalize tenants who invoke
their rights to withhold rent over poor conditions.
Payday loan information, in contrast, is probably harmful.
It is designed to lead to a cycle of debt, and just reporting
it can hurt a consumer. And it is probably not necessary
because most payday borrowers actually have credit records.
Gas and electric utility data can be potentially harmful if
added in the wrong way. If reported monthly without giving
consumers a choice, it has the potential to hurt tens of
millions of low-income consumers by adding new reports of 30-
or 60-day late payments. In contrast, efforts to include
utility data on a voluntary basis could be useful, and new
voluntary products show there is no need for utility credit
reporting where the consumer has no choice.
And then, of course, there is Big Data--things like social
media profiles, web browsing history, and behavioral data.
There are a lot of unanswered questions about the
predictiveness and the accuracy of Big Data. Some of it is also
troubling because it strongly reinforces inequality. For
example, education, that is, what kind of degree a consumer
has, is highly correlated with the income and education of
one's parents. And using social media profiles, particularly
friend networks, raises concerns about racial disparities,
given who most people's friends and families are likely to be.
Speaking of racial disparities, we know there are
tremendous racial disparities with respect to traditional
credit scores. It is the result of centuries of slavery and
discrimination which led to the huge racial wealth gap.
Alternative financial data is also likely to have racial
disparities for the same reasons. The critical question is
whether the alternative data or algorithms lessen or increase
racial disparities and whether it is more predictive or less
than traditional models.
These two questions are closely tied to the test for
disparate impact under the Equal Credit Opportunity Act. If the
alternative data is less predictive, there is less of a
business justification for it, under the disparate impact test.
On the other hand, if it creates less of a racial disparity, it
could be a less discriminatory alternative than traditional
scoring.
In terms of regulation, all third-party alternative data
used for credit should be considered a consumer report under
the Fair Credit Reporting Act (FCRA). Unfortunately, several
courts of appeals haven't respected the plain language of the
FCRA and its broad coverage. We urge Congress to reaffirm this
broad coverage, because the FCRA has critical protections. One
of the key issues with alternative data is accuracy, the FCRA
addresses accuracy, and it gives consumers the right to dispute
errors.
The FCRA, as well as the ECOA, also requires notices for
the purpose of transparency, requiring lenders to disclose the
source and type of information so consumers aren't left in the
dark as to the reasons for credit decisions. Having black boxes
to evaluate creditworthiness should be a thing of the past. I
thank you for the opportunity to testify and I look forward to
your questions.
[The prepared statement of Ms. Wu can be found on page 80
of the appendix.]
Chairman Lynch. Very good, thank you.
Mr. Rieke, you are now recognized for 5 minutes.
STATEMENT OF AARON RIEKE, MANAGING DIRECTOR, UPTURN
Mr. Rieke. Chairman Lynch, Ranking Member Hill, and
distinguished members of the task force, thank you for the
opportunity to testify today. We are here because approximately
45 million Americans do not have access to credit because there
is a lack of quality data with which to underwrite them.
Alternative data can certainly help. I want to echo Ms. Wu and
say that the devil is in the details, and to suggest that we
are really talking about two categories of data here:
conventional data; and fringe data.
Conventional data consists of things like various payment
histories, bank account balances, information about an
individual person's financial capacity. Fringe data consists of
things like social media data, information that may be
correlated with this financial capacity but is much further
removed.
Conventional data is promising; fringe data raises
concerns. To understand why, think about traditional FICO
credit scores. These credit scores are not conceptually
complex. Most of their predictive value comes from people's
payment histories. That is really the number one factor in the
recipe of FICO scores. The logic is simple. If a consumer is
keeping up with their current financial obligations, it is
reasonable to predict that they can take on new financial
obligations. As it turns out, the same basic logic applies to
many kinds of conventional data. The best available evidence
suggests that bill payment histories are similarly predictive
and can help otherwise unscoreable consumers access credit.
Another example, cash-flow data obtained from a consumer's
bank account with their express permission, can provide an
immediate high-quality picture of that person's ability to
repay a loan, even without a credit bureau being involved at
all. That is conventional data.
The story gets murkier when we talk about fringe data.
Expansive data sets about people's social connections, the
kinds of websites they visit, where they shop, and how they
talk do not have the same simple, intuitive connection to each
individual's ability to repay a loan. These can yield blunt
stereotypes that might be predictive, but for the wrong
reasons.
Let me offer you an analog analogy. Imagine I offered to
build you a credit-scoring model that relied on a person's ZIP
Code. That should feel intuitively wrong. I want to unpack why.
First, we know that geography reflects deep-seated social
inequities. The result would almost certainly be textbook
disparate impact.
Second, judging from ZIP Codes would paint with too broad a
brush. It would do little to help many of the unscoreable
consumers we seek to help most who already live in low-income
neighborhoods. Latching on to traditional markers of wealth and
privilege aren't going to get us to where we want to be. My
point is that thousands of behavioral data points thrown into a
complicated, machine-learning, artificial intelligence model
can actually act and behave just like a ZIP Code. In the
absence of rigorous public scrutiny, we should be skeptical of
fringe data.
I want to note, because Facebook was brought up in opening
remarks, that Facebook has for a number of years had a policy
that prohibits third parties from using Facebook users' data
for any kind of eligibility purpose. So, if you see a start-up
company touting their use of Facebook data, ask them why they
are violating Facebook's policies. That may not be the case
forever, but I think today that indicates that we are not ready
to embrace this new data set. In short, this task force should
focus its efforts on encouraging the use of alternative data
that is closely related to loan performance, has an
understandable relationship with an individual applicant's
creditworthiness, and has been evaluated for compliance with
anti-discrimination laws.
Fortunately, this is all doable. More collection and use of
alternative data makes the Fair Credit Reporting Act and the
Equal Credit Opportunity Act more important than ever before. I
would urge Congress to ensure that new kinds of alternative
data are only used for credit underwriting, where we have
researched and understood their role, and not for things like
employment and insurance.
Finally, as you are all aware, thanks to the advocacy of
Ms. Wu and her colleagues, any new policies around alternative
data must respect important State and local consumer
protections. Thank you again for the opportunity to testify,
and I welcome your questions.
[The prepared statement of Mr. Rieke can be found on page
74 of the appendix.]
Chairman Lynch. I thank the gentleman.
Ms. Johnson, you are now recognized for 5 minutes for a
summary of your testimony.
STATEMENT OF KRISTIN N. JOHNSON, MCGLINCHEY STAFFORD PROFESSOR
OF LAW, TULANE UNIVERSITY LAW SCHOOL
Ms. Johnson. Good morning, Chairman Lynch, Ranking Member
Hill, Ranking Member McHenry, members of the committee, and
members of the task force. Thank you for inviting me to
participate in this hearing to discuss the use of alternative
data in credit underwriting and credit scoring. I am a
professor of law and associate dean of faculty research at
Tulane University Law School, but I have previously worn other
hats. I was an analyst at Goldman Sachs, a vice president and
associate general counsel at JPMorgan, and an associate at a
New York law firm with a globally recognized transactional
practice. During my tenure in financial services and as an
academic, I have learned a few things about financial markets,
including the lesson that credit is a critical resource.
Individuals and families increasingly rely on credit to
finance household purchases and overcome significant
unanticipated expenses. Without access to credit on fair and
reasonable terms, it can be extraordinarily expensive to be
poor. For families with fragile financial circumstances, credit
may serve as a lifeline, enabling consumers to meet short-term
debt obligations and to pay for education, transportation,
housing, medicine, childcare, and even food.
Two critical developments create promise for the 26 million
Americans referenced earlier as credit invisible, those without
credit histories, and the 19 million Americans who have thin,
impaired, or stale credit histories described as unscoreable.
First, the birth of Big Data. The collection, storage, and
analysis of vast volumes of consumer data fuels artificial
intelligence or automated decision-making platforms. Similar to
the proliferation of AI in health care, employment, criminal
law, surveillance, and communications, the rise of AI in
finance monetizes consumer data. Consumers' web browsing,
click-stream data, and social media networking, which we could
describe as consumers' digital interface, is matched with or
paired with consumers' financial transactions, checking and
saving account cash flows, and credit and debit card
transactions, fueling data mining and engendering a new set of
behavioral criteria we can describe as alternative data.
While fintech firms integrating alternative data offer
great promise, it is very much worth noting that this new
species of financial market intermediaries also presents great
concerns. In my limited time this morning, I note three
challenges that arise when we integrate and endeavor to
regulate alternative data.
First, alternative data may, as mentioned earlier,
disadvantage vulnerable, marginalized consumers, particularly
those who are members of legally protected classes. Under the
behavioral scoring model, your friends on Facebook, the people
in the pictures you post on Instagram, and those you chat with
on WhatsApp--I am happy to deconstruct that later for those
unfamiliar--may signal more than whether or not you have street
cred. These connections may determine the interest rate on your
next mortgage.
It is not yet clear how these new sources of data will
impact credit invisibles and unscoreables, groups often
disproportionately comprised of women and people of color.
Unsavory lending practices, detestable marketing tactics, and
usurious interest rates have too often plagued these
marginalized consumers.
Second, learning algorithms evaluate facially neutral,
alternative data, yet may result in variables that function as
proxies for protected traits or result in decisions that may
have a disparate impact on members of legally protected
classes.
Consider, for example, Amazon's recent experiment with an
algorithm tasked with reviewing resumes for a software
programmer position. Armed with the resumes of previous hires
and general instructions regarding qualifications, the
algorithm went rogue. Because previous hires were predominantly
men, the algorithm began to discount references to women,
including references to women's chess club captain or all-
women's colleges. Unknowingly, the algorithm replicated
historic discriminatory hiring biases. In credit decisions,
these results may be actional, as noted earlier, under the
Equal Credit Opportunity Act and fair lending and fair housing
regulations.
Finally, alternative data raises concerns regarding
consumer privacy and cybersecurity concerns. Beyond Equifax's
settlement this week, there is more breaking cybersecurity
news. A 20-year-old computer programmer successfully launched a
cyber attack against another nation's national revenue agency,
signaling that it is imperative to ensure that any entities
that collect, store, and transfer consumer data have developed
sufficient security mechanisms.
CRAs may also struggle with respect to the obligation to
describe and explain adverse credit decisions. Because of the
inscrutable nature of learning algorithms, they are non-
intuitive, opaque, and their operations are not often easily
explained.
Finally, in my written testimony I note as well that there
is an even newer class of emerging financial intermediaries
within the fintech ecosphere, or ecosystem--blockchain-based
CRAs. I reference in my written testimony Bloom, one example of
a blockchain-based credit reporting agency or an entity that
will operate in a manner similar to a credit reporting agency,
that is also presumably to rely on alternative data.
For these reasons, I encourage and urge Congress to think
carefully about comprehensive legislation outlining the
appropriate uses for alternative data and data governance,
storage, transfer, and cybersecurity protections, as well as
enforcement of antidiscrimination norms.
[The prepared statement of Ms. Johnson can be found on page
57 of the appendix.]
Chairman Lynch. Mr. Evans, you are now recognized to give
us a 5-minute summary of your testimony.
STATEMENT OF LAWRANCE L. EVANS, MANAGING DIRECTOR, FINANCIAL
MARKETS AND COMMUNITY INVESTMENT, U.S. GOVERNMENT
ACCOUNTABILITY OFFICE (GAO)
Mr. Evans. Thank you very much, Chairman Lynch. I am
pleased to appear before you, Ranking Member Hill, Ranking
Member McHenry, and the members of the task force to discuss
the use of alternative data in underwriting. My testimony is
largely based on our December 2018 report, which covered
several fintech lending issues. The problem with the current
credit-granting ecosystem has been well-articulated, namely its
limits in its ability to reach certain borrowers. We know that
alternative data provides an opportunity to improve the status
quo by expanding access to credit, improving prices, speeding
up decision-making, and preventing fraud, but it is also
important to know that some of what we refer to as alternative
data is not new.
However, the types of alternative data available have
expanded significantly due to the ability to secure large
volumes of consumer and behavioral information, including data
on consumer spending and shopping habits, internet browsing
history, online social media networks, educational
affiliations, and other factors that may not have a clear nexus
with creditworthiness.
In combination with analytic techniques like machine-
learning, these factors provide predictive power for fintech
companies looking to enhance their ability to determine who is
eligible for credit. But alternative data is not a panacea.
Depending on the specifics of these data and the analytical
techniques used to extract information from them, these
innovative approaches can bring significant risk. One of the
major concerns is that usage of that data may produce lending
outcomes that result in disparate impacts or violations of fair
lending laws, unintentionally in some cases.
For example, according to a Federal Reserve newsletter, it
has been reported that some lenders consider whether a person's
online social network includes people with poor credit
histories, which can raise concerns about discrimination
against those living in disadvantaged areas.
Another concern is that there may be a lack of transparency
about what alternative data are being used and how they
ultimately factor into credit decisions. This potential opacity
could raise issues, not only for consumers, but for fintech
firms themselves looking to comply with fair lending
requirements. It may also be unclear whether a borrower has the
ability to dispute the accuracy of the information used.
The great challenge ahead is to manage the risk-reward
balance of innovation and ensure our experience with
alternative data does not mimic our experience with alternative
mortgage products leading up to the financial crisis. To better
ensure the risks are managed without stifling innovation, which
is extremely important, policymakers and regulators will need
to sort through a number of different tradeoffs and
considerations.
In the meantime, implementing key recommendations that GAO
has offered to regulators would assist them in addressing some
important deficiencies as we see them. Fintech lenders and
their banking partners we spoke to indicated they face
challenges due to regulatory uncertainty about the appropriate
use of alternative data. Representatives of one bank said that
a fintech partner's use of alternative data may be attractive
from an innovation and business perspective, but the bank would
likely hesitate to use this data due to regulatory uncertainty.
While Federal agencies monitor the use of alternative data,
they have not provided firms with the types of communication
that they need to really think through the appropriate use of
this data in the underwriting process.
We believe coordinated guidance from the regulators may
better position fintech lenders and their bank partners to
responsibly use alternative data. In our prior work, we have
also recommended that agencies formally evaluate the
feasibility and benefits of adopting knowledge-building
initiatives. We believe these initiatives will help firms
understand the applicable regulations, improve regulators'
knowledge of fintech products, and facilitate interactions
between all parties.
Chairman Lynch, Ranking Member Hill, Ranking Member
McHenry, and members of the task force, this concludes my
opening statement. I look forward to any questions you may
have.
[The prepared statement of Mr. Evans can be found on page
42 of the appendix.]
Chairman Lynch. Thank you, Mr. Evans.
Mr. Girouard, you are now recognized for 5 minutes.
Welcome.
STATEMENT OF DAVE GIROUARD, CEO AND CO-FOUNDER, UPSTART
NETWORK, INC.
Mr. Girouard. Chairman Lynch, Ranking Member Hill, Ranking
Member McHenry, and members of the Task Force on Financial
Technology, thank you for the opportunity to participate in
today's conversation. My name is Dave Girouard, and I am co-
founder and CEO of Upstart, which is a leading artificial
intelligence lending platform. I founded Upstart more than 7
years ago, in order to improve access to affordable credit. In
the last 5 years, almost $4 billion in bank quality consumer
loans have been originated on our platform, using a model that
combines alternative data with AI and machine-learning
algorithms to determine a borrower's creditworthiness.
Concerns about fairness in algorithmic lending,
particularly in the use of alternative data, are well-founded.
As a company focused entirely on reducing the price of credit
for the American consumer, fairness is an issue we care about
deeply. In the early days at Upstart, we conducted a
retroactive study with a large credit bureau, and we uncovered
a jarring pair of statistics: Just 45 percent of Americans have
access to bank quality credit, yet 83 percent of Americans have
never actually defaulted on a loan. This is not what we would
call fair lending.
The FICO score was introduced in 1989 and has since become
the default way that banks judge a loan applicant, but in
reality, FICO is extremely limited in its ability to predict
credit performance because it is narrow in scope and inherently
backward-looking.
At Upstart, we decided to use modern technology and data
science to find more ways to prove that consumers are indeed
creditworthy, to bridge that 45 percent versus 83 percent gap.
We believe that consumers are more than their credit scores,
and by going beyond the FICO score and including a wide variety
of other information such as a consumer's employment history
and educational background, we have built a significantly more
accurate credit model.
While most people believe a better credit model means
saying no to more applicants, the truth is just the opposite.
Because Upstart's model is more accurate, we have significantly
higher approval rates and lower interest rates than a
traditional model.
But we also understood that consumer protection laws
weren't to be taken lightly. Thus, we proactively met with the
appropriate regulator, the Consumer Financial Protection Bureau
(CFPB), before launching our lending program. After several
years of good-faith efforts between Upstart and the CFPB to
determine the proper way to measure bias, we demonstrated that
our AI-driven model doesn't result in unlawful disparate impact
against protected classes of consumers.
Because AI models change and improve over time, we
developed automated tests with the regulators' input, in order
to report on the impact of our credit decisions across
underserved groups on a quarterly basis. We have been providing
this information to the CFPB for the last 18 months. Moreover,
we were able to report to the CFPB that our AI-based system
improved access to affordable credit; specifically, our model
approves 27 percent more consumers and lowers interest rates by
3.57 percentage points compared to a traditional lending model.
For near-prime consumers in the 620 to 660 FICO range, our
model approves 95 percent more consumers and reduces interest
rates by 5.42 percentage points compared to a traditional
model. And, most importantly, Upstart's model provides higher
approval rates and lower interest rates for every traditionally
underserved demographic. That is the type of consumer benefit
we should all get excited about.
In September 2017, Upstart received the first ever no-
action letter from the CFPB, recognizing that Upstart's
platform improves access to affordable credit without
introducing unlawful bias. The concern that use of alternative
data and algorithmic decisioning can replicate or even amplify
human bias in lending is well-founded. However, in Upstart's
experience, the fair-lending laws enacted in the 1970s and the
substance of fair-lending enforcement, that is, monitoring and
testing the impact on actual consumers who apply for loans,
translates very well to the AI-driven world of today.
But in reality, the path we walked at Upstart is
insufficient to create a robust and competitive market that
will maximize financial inclusion and credit access. In our
early days at Upstart, we couldn't know for certain whether our
model would be biased. It wasn't until loans were originated
that we were able to demonstrate that our platform was fair. As
an early-stage startup, this was a risk worth taking, but it is
not a risk a large bank would have considered.
If broader and deeper financial inclusion among American
consumers is important to this committee, it is worth
considering rulemaking or legislation that will provide some
type of limited sandbox for model development and testing. By
combining regulatory support with model innovation, with
rigorous and standardized testing, we can ensure that we don't
forego the clear and obvious benefits that AI and alternative
data can offer to the American consumer. Thank you.
[The prepared statement of Mr. Girouard can be found on
page 54 of the appendix.]
Chairman Lynch. Thank you. I now yield myself 5 minutes for
questioning. Thank you all. This is a great group. One of the
nicer things about this task force is that it is bipartisan,
and we are here for the same purpose: We are looking for
guidance. We have an assortment of issues that we are
confronting. Obviously, the banking industry is transformed, I
think, because of technology so that we have an old banking
culture that is very much rule-based, and it seems to be
merging or morphing into this sort of tech hybrid where you
move fast and break things. And so, there is that clash of
cultures.
But I can generally group our concerns into four areas. One
is the whole issue of companies vacuuming up this personal
data, this behavioral surplus, as Shoshana Zuboff describes in
her book, ``The Age of Surveillance Capitalism.'' And under
what conditions do consumers have a choice in terms of what
gets vacuumed up and what gets used in terms of the algorithms
that are employed to judge their creditworthiness or on other
matters. There is that whole permission aspect that Ms. Wu and
Ms. Johnson both raised. Actually, all of you, I think,
addressed that in some regard.
Then the data use, how that gets used, what data is
permissible to use and what is not. Then, one of the concerns
that this committee has is regarding the security of that data.
We had Facebook in, and Mr. Marcus, who is heading up their
Libra cryptocurrency project. And it is obvious from our
history with Facebook, that Facebook does not do privacy well,
and so we worry about that. If you look at the terms of service
agreement, the one that is on your phone with Facebook, it is
about 20 pages long. And if you look at it closely, it
basically is the opposite of a privacy agreement. It basically
gives Facebook the ability to gather all your data and then
sell it to their advertisers. And if you don't agree, if you
don't click, ``I agree,'' you don't get Facebook. So, I am
worried about fintechs using that same sort of adhesion
contract to get people to surrender their data, in order to get
the value of what Mr. Girouard has described, which is perhaps
lower rates, better access to credit, all the benefits that
might flow from one of the fintech lenders.
And then, lastly, we are struggling with how to hold people
accountable with financial data. Should there be--I asked Mr.
Marcus, but he wasn't forthcoming with an answer--I asked him,
I said, would you accept fiduciary liability for the
mishandling of consumers' personal financial data because of
the consequences that can occur because of that mishandling?
So, Ms. Wu, let's talk about, how do we get into this? How
do we introduce this permission regime where people can--and,
Ms. Johnson, I will go to you on this as well--how do we
introduce this? Right now, it is a permissionless vacuuming up
of data. How do we change the paradigm and the model from what
we have now to a more rule-based, if you will, structure with
some of the fintech that is emerging?
Ms. Wu. Thank you for the question, Chairman Lynch, it is
an excellent one. There is the sort of limited issue of
alternative data for credit purposes, where we would urge that
any legislation always be on an opt-in basis, that consumers
have a choice, and that that choice be real and meaningful,
that it not be in mice type of 20 pages of fine print that you
mentioned. From a broader perspective of privacy in general,
yes, we all should have more control over our own data, the
right to opt in, opt out, or even have our data deleted.
Chairman Lynch. Very good.
Ms. Johnson?
Ms. Johnson. I think that Ms. Wu's point is absolutely
consistent with what our expectations ought to be. I think the
challenges are two-part, one part technical, so I might defer
to Mr. Girouard to respond as to how their model might address
this very specific and technical point. But for AI to be
effective, as I describe in my written testimony, there has to
be a certain quantity or volume of observations available. They
have to be uniform to a certain extent, and that facilitates
the learning algorithm's ability to work through the data in a
manner that is exceptionally efficient and reduces operating
costs, thereby enabling fintech lending platforms to reduce the
cost of borrowing for consumers.
One of the challenges I am very curious about how we will
navigate is the extent to which we are rightly asking that
consumer's consent prior to their data being used, and how we
reconcile that with how machine-learning algorithms operate.
So, I think there is a gap there that we have to have enough of
a conversation about, to be successful in crafting regulation.
The other thing I just mentioned really quickly about
consent is that the extent that the data is being gathered
really may be the point of departure for some of our concerns.
In many instances, consumers are completely unaware that the
data is being gathered. And in some instances, they are
voluntarily giving the data over for the better credit
opportunities or reduced price credit opportunities, which is
disconcerting, to be quite honest, because it suggests those
who are most vulnerable might be exposed to--or exploited, in
fact, by arrangements whereby they share the most intimate
details of their financial lives or their personal lives for
the purpose of getting better access to credit.
Chairman Lynch. Thank you very much. I now yield to my
friend, the gentleman from Arkansas, Mr. Hill, for 5 minutes.
Mr. Hill. Thank you, Mr. Chairman.
And, again, thanks to the panel. This is another really
excellent panel that has been assembled for the task force
work, and I think all of you bring a great perspective.
Certainly, this issue of customer choice is an important
one, and we all are frustrated, I think, with moving away from
passwords into a more robust authentication, which is critical
to a digital world, critical to fintech being successful,
whether you are working at the biggest bank in the country or a
great startup. We need to get beyond ``password1'' and our name
as authentication, and we have been talking about that a lot.
Secondly, this issue of, I own the data, I am the consumer,
and I am allocating you some data for a project we are working
on together, and so broadening that transparency in access to
my data for the purpose of taking a decision that I want to
have with an online partner. These are really important areas
and thank you for bringing those up.
Mr. Girouard, I want to talk a little bit about your model
and the alternative use or, as was described by Mr. Rieke, your
expansion, I would say, of conventional data. I will ask if you
use ``fringe data'' or not, as he defined it, but we will find
out. But I am very impressed that you have been working 18
months with the CFPB, which is a beloved institution in
Washington and certainly in this committee and, therefore, has
imminent authority over that relationship. And congratulations
for having a no-action letter. We think that is a great
improvement for CFPB operations as an absolutely serious
comment and a great way for them to demonstrate the ability for
fintech innovation in a mini sandbox if you want to call that a
derivative of that.
My first question is, the conventional data you expand
beyond FICO, what is the nature of that in your business?
Mr. Girouard. Sure. I want to say first thatthe data we use
in our models comes entirely from two sources: one, is a credit
reporting agency; and two, is directly from the consumer
themselves. So we aren't ``hoovering data in many places.'' We
don't take data from Facebook, et cetera. What we to do is
include information--and I had mentioned a couple of them--
somebody's work history, where do they work, are they a nurse,
are they a policeman, et cetera, their educational history, the
degree of education obtained, their area of study. These are
things that are unique to our model. We also look at some
behavioral things when they interact with us, what sort of--how
much--what size loan do they ask for, how did they find us,
things of this nature. These all end up being helpful and
predictive toward our model.
Mr. Hill. Do you consider that--of course, it is provided
by the customer. They are seeking the loan, so they have
granted you permission to do that. Are you also seeing their
cash-flow data for a period of months by access to their bank
account in making your determination?
Mr. Girouard. Today, that is not something we do. We do
request and, with consent, get access to a bank account really
for verification purposes and to avoid fraud and such. But it
is, as of today, not part of our credit decisioning.
Mr. Hill. You talked about how you are doing that, and, of
course, the CFPB is learning, too, and you keep some of your
credit, and your partner bank has some of your credit
originated by you on their books, and then you securitize
credit. So, for the loans that you keep and for the loans that
are on the bank's books, of course, those are being reviewed by
compliance officials for compliance with all fair lending laws
and the like? Isn't that right?
Mr. Girouard. Sure. There are many layers of oversight and
governance over what we do. The vast majority, almost all
Upstart loans are originated through bank partners--some of
which are FDIC-regulated, and some of which are OCC-regulated.
So, we are beholden to all of them and go through very regular
audits and such.
Mr. Hill. What is your view of what statutorily ought to
change about the creation of a sandbox at our bank regulatory
agencies? What does that mean to you? I see it in your
testimony. You don't really explain what you mean by that. How
do you define it?
Mr. Girouard. Our belief, as I said, is that the right way
to handle regulation for alternative data, and the use of
alternative data is actually to measure the outcome, to look at
its impact on consumers and whether there is bias in the
outcome. The challenge with that, and the way the world works
today, is, you don't know until you originate the loans. So,
you are taking on some risk that, during that period of
evaluation of building and testing that model, you could be in
violation of the law, of fair-lending laws. The sandbox concept
is, how do you actually make progress there? How do you
actually build a better model that is both more effective and
more accurate, but also fair and unbiased without testing and
moving? And the notion of a sandbox is to provide some freedom,
not just for a startup like we were 5 years ago, but to a large
financial institution, a bank, to do the same thing.
Mr. Hill. This is like a phase one or a phase two clinical
trial in the drug research industry. How long do you think that
would take and how much of a Big Data set would that be, in
just your world of personal lending, do you think would be
necessary to prove out a concept like that, analytically? 18
months?
Mr. Girouard. Yes. That is--
Mr. Hill. Do you look at it in time, or do you look at it
in total data set, or both?
Mr. Girouard. It is a little of both.
Mr. Hill. Because you have to go through the economic cycle
of these borrowers, to some degree, some seasoning of these
borrowers.
Mr. Girouard. That is really about the efficacy question,
meaning, does this model work well? But the fairness question
actually is answered quite quickly because you know right away
who you are approving andwho you are not approving.
Mr. Hill. Thank you. I yield back, Mr. Chairman.
Chairman Lynch. The Chair now recognizes the gentleman from
Georgia, Mr. Scott, for 5 minutes.
Mr. Scott. Mr. Evans, let me start with you, because in
your testimony you provide a very good survey of the literature
of the potential benefits of alternative data, but you also
mention the risks. First of all, I think it would be helpful if
you gave us some examples. What are we talking about when we
say alternative data? What would that be?
Mr. Evans. This could range from data that we have had
significant experience with, like on-time rental payments,
mobile payments, and the like. But it could also be data that
we glean from your digital footprint online or your browser
history.
Mr. Scott. But these data points also must uphold the fair
lending laws and standards that we have in place. I think the
critical question is, how do we strike the balance? How do we
strike the necessary balance, particularly given the innovative
nature, the rapidity of our technology moving?
Mr. Evans. Excellent question. And there are two things
that I would point out from our body of work. One, we looked
across the globe, and we looked at some of the innovative
things other countries were doing, and they were things like
the regulatory sandboxes, and innovation offices. We have to
understand the technology, and the way to understand the
technology is to engage. We have recommendations that are open
to regulators to make sure they are carefully thinking through
whether these innovation offices and other types of knowledge-
sharing initiatives would be appropriate here in the United
States.
Also, guidance is extremely important because it sets the
rules of the road. It sets parameters. And if the fintech firms
aren't getting that kind of guidance, they are not--
Mr. Scott. And do you think the regulators are living up to
that? Do you think they are giving this guidance properly now?
Mr. Evans. I would say no. There are certainly places where
you can find good information from the Federal Reserve and
others, but they haven't communicated this guidance in a
written, formal way, so that people understand that this is
relevant guidance for firms to follow. When you get many
touches across the fragmented regulatory system, it is helpful
to know that the guidance is coordinated; it is not coming from
just one regulator.
Mr. Scott. Ms. Johnson, you said something in your
statement that I agree wholeheartedly with: you said that
credit is a critical choice. It is almost a life-and-death
choice. Can you imagine not having a checking account? Not
having a savings account? Not having a credit card? Not having
any history in this time? And yet, we have almost 60 million
Americans in that shape. How critical, in your words, is this,
at this point, with our unbanked, and if we fail in this
ability to make the alternatives work, what would that look
like? How serious is this situation facing hese 60 million
unbanked, or what you refer to as invisibles, and making them
visible?
Ms. Johnson. This is a great question. Thank you. I think I
might dissect or sort of bifurcate the question into two parts,
one part just being thoughtful at the outset about the idea
that credit, as we are describing it, originates from--or the
decision-making process, or determinations about credit,
originates from an evaluation of eligibility, right? The notion
that credit reports are used merely for credit is mistaken. We
know that credit reports might be used in other processes to
determine employment and access to other resources. So, in some
instances, we are talking about credit and the data that is
evaluated to determine whether or not someone has access to
credit, as a gateway. This is a sort of a gateway to a variety
of critical access, to a variety of critical and important
resources in our society. Credit is a critical resource and
credit reports are a critical factor in the lives of
individuals because it may impact their ability to access other
resources beyond credit, right?
Mr. Scott. Yes.
Ms. Johnson. That is the first point, just to segregate out
the ideas that what we are evaluating here, the data that is
being gathered, there are many important impacts with respect
to that data, that are beyond just simply whether or not one
qualifies for a credit card or a home mortgage loan. Although,
access to those resources is important as well.
I would also underscore--Congresswoman Porter was one of my
colleagues in the academy before joining you all here on the
committee and in Congress, and her work has historically, along
with others, underscored the significance of the financial
status of individuals as impacting a variety of elements of
their lives, and your point underscores that as well. I just
suggest that credit and the data that is being gathered for the
purposes of evaluating credit will impact access to financing,
but it impacts access to a number of other things, including
education.
Mr. Scott. And, Mr. Chairman, may I just ask this--one of
the values of--
Chairman Lynch. The gentleman has gone a minute-and-a-half
over. Go ahead, though.
Mr. Scott. Thank you. One of the values of the fintechs is
that they are now providing help and services to the unbanked
that our traditional banks are not doing, will not do. And I am
not going to ask you to answer that, but I am sure you will
agree that that is an area we can develop more of, to use our
emerging fintechs to be a valuable asset, because many of the
existing actors in the financial services industry are not
going to touch these unbanked and underbanked. But, anyway,
thank you.
And thank you, Mr. Chairman. I'm sorry.
Chairman Lynch. Quite all right.
The Chair now recognizes the ranking member of the full
Financial Services Committee, the gentleman from North
Carolina, Mr. McHenry, for however much time he may consume.
Mr. McHenry. I will respect the Chair. Thank you, Mr.
Lynch. And thank you, Mr. Hill, for your leadership.
It is my hope that this task force can--we can build some
consensus around financial technology. This is a nonideological
space in an otherwise highly polarized Washington. And I think
it shows that we can use technology to get better societal
outcome--well, the same or better societal outcomes that we
seek in current law.
We have very important provisions of law that have been put
in place through a massive amount of work to ensure that we
don't discriminate against people based off of what I would
describe as superficial reasons. And that work, where you are
located, what you look like, who your parents were, any of that
stuff, right?
And what we see now in China is that you have this--you
have a social score as well. And it is political connections
and all of this stuff. And I hear this underlying the whole
panel, we don't want that. Just because you tweet and you are a
jerk on Twitter doesn't mean you are uncreditworthy. Or if you
follow nuts on the left or the right on Twitter, that should
not make you more or less creditworthy.
Getting into the fundamentals of this, how you use
alternative data, Mr. Girouard, you brought this up. Let's talk
about the sandbox approach that Mr. Hill brought up in his
question.
So, the question of innovation and financial inclusion, I
think, should go hand-in-hand. What are the benefits of a
sandbox approach, Mr. Girouard, in your view?
Mr. Girouard. As someone who has gone through the process,
as we did over 4 years, frankly, with the CFPB, the sandbox
isn't to our advantage. We already walked the walk and walked
over the coals.
But honestly, in the interest of the American consumer, you
want a robust environment where not just small companies but
the largest banks have an opportunity to innovate in modeling
and in credit decisioning, because it can only benefit the
consumer.
A sandbox is necessary because--let me just give an
example. In the very early days of our lending, I met with the
CEO of one of the top banks in the country, one of the largest
card issuers in the country, and his words to me were, ``I love
what you are doing. I am really glad you are doing it, because
we will never be able to do that.''
And I think honestly, it may be to my business advantage
that that is the case, but it is not to the American consumer's
advantage. We need innovation across the industry, not just in
unsecured personal loans, but in mortgages, in auto lending, in
HELOCs, in all flavors of credit.
Mr. McHenry. What will the benefit be if you use
alternative data and somebody has, under a traditional score,
less than A-plus credit, but you see through alternative data
that they actually pay their rent, they pay their cellphone
bill, and they have never missed those payments, it enhances
that credit score, right?
Others, it would actually say that that credit score is not
as good, because they are not paying or they continue to have
issues.
There is this picking and choosing, when you say, we only
want to use good stuff, if it is alternative data. Well, that
is not representative that everyone is a good credit risk,
right? How do you prove that out in terms of ensuring it is not
discriminatory based off of our traditional metrics under
Federal law?
Mr. Girouard. Let me just say, the important background is
that FICO and income, which are the two anchors of almost any
lender, are terribly biased. And they are so biased that the
additional of alternative data, whether that is education,
whether that is the name of the company you work for--there are
a variety of other things--actually reduces the bias and the
credit decisioning, because the baseline is so biased itself.
That is why it represents such an opportunity.
The other really important--
Mr. McHenry. Okay. Across the panel, does anybody disagree
with that statement?
Ms. Johnson. I would add something.
Mr. McHenry. But any disagreement with the contents in the
last 5 sentences of what Mr. Girouard said?
Anyone on the panel?
Ms. Johnson. There is bias certainly in the existing data,
because it is the result of systemic--we just talk about data
collection for algorithms generally. We have to acknowledge
that at the outset, the data that is being collected is biased.
One of the best and easiest, most accessible examples,
would be in criminal law enforcement. To the extent that an
area is overpoliced by police in a particular city or area,
there will be more arrests in that area--
Mr. McHenry. No, but I am talking about consumer credit,
and I am talking about the specifics of this. That is a larger
societal issue. We are the Financial Services Committee and not
the Judiciary Committee. That is a major issue; I certainly
understand that. And I appreciate that.
But let's talk about what we are going to fix here in the
Financial Services Committee. When you say that alternative
data can be an enhancement--and I understand all of the caveats
that all of you in a very loyal sort of way, if I would say,
say, yes, it has great opportunities but there are risks. Of
course there are, right?
But when we are talking about getting unbanked or credit
invisible people and making them visible, I think that is a
proper societal tradeoff in order to get more people into the
world of being banked, rather than underbanked or unbanked.
And so, I appreciate the hearing. And with that, Mr.
Chairman, I yield back.
Mr. Scott [presiding]. Thank you, Mr. McHenry.
The gentleman from New Jersey, Mr. Gottheimer, is
recognized now for 5 minutes.
Mr. Gottheimer. Thank you, Mr. Chairman, and thank you to
all of the witnesses for being here today. I appreciate it.
Traditional information used to make lending decisions and
establish credit scores often does not account for the 26
million customers and consumers without a credit history or the
19 million consumers with a short or outdated credit history to
form a credit score, groups that are often labeled as thin file
or credit invisible.
Thankfully, lenders and CRAs have started using alternative
data to make lending decisions, determine credit scores, and
expand consumers' access to data.
I personally believe that this is the future in the era of
renting and Venmo and Uber, that we need to give the next
generation of consumers the ability to build a stronger credit
file through nontraditional data sources. That is why I am
working on the Credit Access and Inclusion Act, legislation
that would allow the reporting of certain alternative data like
rent and telecom payments to consumer reporting agencies to
help thin-file consumers build their credit scores and
hopefully access credit.
We also must ensure traditional credit bureaus and those
using alternative financial service data still comply with the
Fair Credit Reporting Act, also known as FCRA.
Ms. Wu, if I can start with you, how can we ensure that
alternative data sources comply with FCRA data furnishing
requirements?
Ms. Wu. Thank you for the question, Congressman Gottheimer.
One of the things we need to clarify is that any time
third-party data is used for credit decisioning, it should be
covered by the Fair Credit Reporting Act.
The example of Facebook, for example. Facebook may have a
disclaimer in its website saying you are not supposed to use it
for credit. But if they are doing it wink, wink, nudge, nudge,
and lenders are using it for credit, it should be covered by
the Fair Credit Reporting Act.
And so Congress should clarify that, but I also want to say
in the area of sandboxes, the devil is also in the details.
Sandboxes shouldn't be a license to ignore things like the Fair
Credit Reporting Act and the requirements for accuracy,
predictiveness, and notices.
Mr. Gottheimer. Thanks for your answer.
Just a follow-up to that, what kinds of alternative
information would you seek to use that is not already shared by
applicants or regularly requested as part of loan applications,
rental payments, bank statements, and, of course, under the
Fair Credit Reporting Act?
Ms. Wu. First of all, the most important aspect is consumer
choice. The consumer should be allowed the option of sharing it
or not. So if they want to share their bank account data, if
they want to share their utility payment or rent payment data,
they should be permitted to. But if they don't want to, if they
want to say, hands off my data, that also should be respected.
And then the lender should consider that in the same way they
consider credit data.
The other side of this equation of alternative data is, are
the lenders actually going to use it? We have seen lenders who
won't even upgrade to the latest FICO model, let alone use an
alternative score. So, one of the tough parts is actually
getting the lenders to look at it.
And I think one of the things that this committee has done
that is useful is passing Chairman Lynch's bill giving the CFPB
authority to regulate the scoring models. We have heard from
Mr. Evans that there needs to be guidance from the regulators.
The best thing to do is have the experts at the CFPB review
these models and ask, is this predictive, is this accurate,
does this create disparate impact? And the bill that this
committee passed does that.
Mr. Gottheimer. Do you see that changing with some of the
financial institutions? I know that many aren't considering
other datasets. Do you see that changing? Is there a desire
to--how is the trend line on that? What do you think would
really spur that along?
Ms. Wu. I think the things that will spur it along are
things like, Fannie Mae and Freddie Mac are going to be needing
to update their scoring models, and we have actually encouraged
the use of pilots, limited pilots with alternative scores.
Mr. Gottheimer. And are we seeing good news out of that?
Are we getting more access to credit for people? I really am
grateful for your leadership in this space, because I think it
is very, very important that more people have access who should
get it, who qualify for it, but just because of traditional,
the way we have done things forever, they are not getting
access to it, or because it is so black box that you don't know
what is in it. And I think that lack of transparency also has a
big impact.
Ms. Wu. Fannie and Freddie have not adopted the new scoring
models yet, but some of the other testing that has gone on has
shown some promise.
Again, the devil is in the details. We need to be careful.
There is going to be some disparate impact. But the thing about
the disparate impact test is, it doesn't say, okay, there are
some racial disparities you have to stop. Are there more racial
disparities or less? Is it predictive? Predictiveness is so key
here. And if it is not predictive, you shouldn't be using it.
Mr. Gottheimer. Thank you so much. I yield back.
Mr. Scott. Thank you. The gentleman from Ohio, Mr.
Davidson, is recognized for 5 minutes.
Mr. Davidson. Thank you, Mr. Chairman. I thank our
witnesses, and I thank all of my colleagues for thoughtful
questions and good dialogue. And hopefully, this will yield
some progress in this really important space.
Mr. Girouard, I want to follow up where Mr. Hill left off
when he was talking with you about how much time would this
take and how would a sandbox work in a regulatory framework
where we have maybe provided certainty for this path with
legislation.
And in your response to him, you said, well, we don't
really need 18 months; you can know pretty quickly whether it
is discriminatory or not; i.e., is it working? And I just want
to pick up from there, because it seems incomplete.
Because if you give credit to everyone at low rates or,
say, free, it is not discriminatory; it is all free to
everyone, whomever shows up, or it is a fixed rate for
everyone, no matter what, it is not discriminatory.
But if there is a massive default rate, it really doesn't
work, right? You do care about defaults, correct? How far into
that process could we know is it both nondiscriminatory and
actually effective in the sense that it provides a useful tool?
Mr. Girouard. That is a good question. It certainly varies
based on the nature of the product. A mortgage, for example,
plays out over many, many more years. But you do need enough
data, you do need to understand both fairness and efficacy.
Fairness can be sorted out fairly quickly. Efficacy takes time.
You need to see how a loan performs.
Mr. Davidson. Is it really fair to give money to somebody
who has no hope of repaying it?
Mr. Girouard. No, it is actually against everybody's best
interest to do that. Ability to repay--
Mr. Davidson. Efficacy is inherently linked to fairness is,
I guess, the point. And so, I am just curious. If you look at
probabilistic models and you look at the statistics and say,
hey, if you have this pattern, is there a dataset that shows
what the--95 percent certainty, 99 percent certainty, what
range of probability of payment history in the early years,
could you say the sandbox has produced an effective tool so
that it is both nondiscriminatory and it is efficacious?
Mr. Girouard. Congressman, you are asking exactly the right
questions. The sandbox has to be defined in a way that allows
the lender to decide if this new model works. And it won't be
the same sandbox for every type of credit product for a variety
of reasons--
Mr. Davidson. Okay. That gives me concern, because there is
no real hope to pass a law that could provide certainty. It is
essentially like, go negotiate your own deal with a regulator.
Mr. Girouard. With all due respect, I think rulemaking
could absolutely define a sandbox in terms of number of loans,
how long the sandbox can operate for, the total dollars in it.
There is no question in my mind that a reasonable process could
define rules that put a sandbox in place for the major areas of
credit for consumers. That would make a significant improvement
in the ability to see innovation in this area.
Mr. Davidson. Yes. Thanks for your expertise, and I
appreciate your experience in the matter.
Mr. Rieke, your background in privacy at the FTC is
interesting, because so much of this links on privacy. And in
the United States, particularly in banking, with Gramm-Leach-
Bliley, financial institutions have a carveout where they treat
data differently.
In a way, financial institutions, and frankly all sorts of
institutions, if they were looking at their balance sheet, they
might treat their dataset as a valuable asset. Consumers,
however, don't necessarily realize that some places they are
considered to have a property right in their data. Is it an
asset for both?
And as people give up this data, one of the concerns is,
how do we reconcile the de facto impact of GDPR and the looming
patchwork of privacy laws coming in the United States and
Congress' failure to act on privacy with that framework so that
consumers can control their data some and not find themselves,
well, wait, I was denied credit. Well, yes, you blocked all
access to your background, if you go to the far end. And on the
other hand, the idea that, gee, if you click these terms and
conditions, anything that is in it is fair game.
How do we regulate privacy in this space with respect to
credit?
Mr. Rieke. That is a great question. I think the FCRA is a
strong start. If you squint at the text of the FCRA, what comes
out of that is if your data is used for important eligibility
purposes, certain rights and protections attach.
Now, the FCRA is pretty old now. And as Ms. Wu said, if I
am giving permission to Facebook to hand my data over to a
lender, it is questionable whether that framework would attach.
But I think looking at the spirit of the FCRA, which was
created especially for these concerns and were some expansion
so that statute might make sense for the digital age, would be
where I would start.
Mr. Davidson. All right. Thank you. My time has expired and
I yield back.
Mr. Scott. Thank you.
And now the gentlewoman from Virginia, Ms. Wexton, is
recognized for 5 minutes.
Ms. Wexton. Thank you very much.
And thank you to the panelists for coming today. This is
really fascinating, and you are giving us all a lot to wrap our
heads around.
Mr. Girouard, I am really interested in your model and
especially the fact point--the datapoint that it reduces
interest rates by 5.42 percentage points and approves 95
percent more consumers in that near-prime area.
What kind of response are you getting from lenders about
your model? Are they enthusiastic about it?
Mr. Girouard. By lenders, do you mean banks we partner or
mean to partner with?
Ms. Wexton. Yes.
Mr. Girouard. Thank you, Congresswoman. I would generally
say there is a lot of excitement about the potential for a
model like this to be able to serve more customers, to be able
to build on their side, lower the risk of lending. A more
accurate model is intuitively compelling to a bank officer.
Having said that, there certainly remains a lot of concern
about regulatory uncertainty. And there is not in any sense a
clear-eyed statement or a sense from the regulators how to
think about this area of technology to a bank. A no-action
letter that we received from the CFPB is a great start. It is
not by any sense a panacea, because there are many other
regulators. There are many limits to a no-action letter, so
there is plenty of room for either regulatory action or
rulemaking to provide more clarity.
Ms. Wexton. I understand that there is some question about
regulatory certainty. But are the lenders willing to accept
that your model is a more accurate credit reporting model?
Mr. Girouard. I think I can comfortably say yes. I am
almost universally seen acknowledgment that our model is more
accurate and more inclusive.
Ms. Wexton. Okay. And Ms. Wu had indicated that one of the
things that we should consider is making any of these
alternative datapoints that are being used for credit to be
considered as a report under the FCRA.
Would that impact your ability to create this algorithm, or
is that something that would not be an issue for you?
Mr. Girouard. FCRA is to cover third-party data, data
reported to--and then can be shared with a lender. And that is
one part of our data. The other part, which is important to us,
is the data that a consumer, with our consent, with their
consent, submits to us.
And again, that can be--if they are stating their income to
you. That is not something generally a credit reporting agency
has information on.
There will always, at least in my mind, be two paths for
data to come to a bank and a lender, one through FCRA-related
data, through credit reporting agencies, and the other provided
by the consumer themselves. And they are both important.
Ms. Wexton. Okay. Thank you.
And, Ms. Wu, you had also indicated that there should be an
opportunity for consumers to opt out of these alternative
datasets being used for credit purposes, is that correct?
Ms. Wu. Thank you for the question, Congresswoman.
I actually would urge that it would be an opt-in process,
that any time you are creating these large new datasets,
consumers give their written authorization to have their
utility or their bank account information included, to be
considered.
Ms. Wexton. So, they would have to affirmatively opt in--
Ms. Wu. Yes.
Ms. Wexton. --and then get it used.
Okay. And I guess a part of that would be a declination or
a refusal to opt in could not be used against them, right? It
wouldn't factor into the algorithm, but it wouldn't be down
counted for not--
Ms. Wu. If they already have a traditional credit file and
score and they decline to opt in to alternative data, we would
say the lender should go ahead and use the traditional credit
score. If they don't opt in, then the data can't be used,
obviously.
Ms. Wexton. All right.
Ms. Johnson, as a law professor, I know that you are
familiar with the difference between de jour discrimination and
de facto.
Is there a way to be proactive in this space and make sure
that we don't end up with de facto discrimination in these
algorithms, or is it always going to be retrospective, looking
back and seeing what the analysis provides us?
Ms. Johnson. Thank you for the question, Congresswoman.
I think that there is a way for us to be thoughtful in
advance of the release of these types of products in financial
markets.
I think earlier, Chairman Lynch referenced the ``move fast,
break things'' mantra that was adopted by a number of
technology firms, and now as fintech firms are entering into
spaces and operating, as Mr. Girouard mentioned, without clear
regulatory guidance, there will be a temptation to use
information or data, alternative data, to facilitate what may
be faster, more efficient, lower-cost credit evaluation
processes.
We do have some knowledge in advance of the types of data
that tends to lead to bias or discrimination, based on a long
history of legislation and court decisions and agency actions
in this space.
I think one of the things we can do is really identify red
flags and target areas. Some of the data Ms. Wu mentioned
earlier and has been talked about over the course of this
hearing, that it is useful and be thoughtful about would be
rental payment history, but there are any number of reasons
why--and Ms. Wu's organization and others have thought about--
that information may disadvantage or utility bill payment may
disadvantage certain--
Mr. Scott. Ms. Johnson, the time is running out.
Ms. Johnson. Thank you very much.
Ms. Wexton. I yield back.
Mr. Scott. Thank you very much.
The gentleman from Missouri, Mr. Luetkemeyer, is now
recognized for 5 minutes.
Mr. Luetkemeyer. Thank you, Mr. Chairman.
Mr. Girouard, since all banks are required to follow the
ECOA and you partner with a lot of banks, what due diligence
and ongoing monitoring does your company provide your bank
partners to ensure that 100 percent certainty for those banks
of no fair-lending violations?
Mr. Girouard. Sure. That is a very good question. For sure,
providing this technology to banks is not for the faint of
heart. There is what I would say is a process of probably more
than a year of them getting to understand and do diligence on
our processes, fair lending being just one of many, to make
sure that loans originated using this type of system are within
the law. And also, of course, that the creditworthiness is
real, the efficacy of the model is real. So, there is real,
significant work before anything happens, before any
relationship is signed.
After the fact, there is a constant reporting and auditing
like function. The same report that we provide for CFPB for all
loans, we can do for an individual bank. And that gives the
bank comfort that we are actually monitoring on a regular basis
to make sure the loans originated in their name, under their
charter, are within the bounds of fair lending regulation.
Mr. Luetkemeyer. I would like to follow up on the previous
colleague's questions here with regards to the no-action
letter.
I am assuming that because you have a no-action letter, it
is very helpful when you go approach other banks to become
partners with them. Because it would sort of seem like you
are--it is a get-out-of-jail free card from the standpoint that
you have already been sort of preapproved by CFPB, that the
modeling you are doing is something that falls within the
guidelines of everything.
How important is that no-action letter whenever you start
negotiating with the other entities?
Mr. Girouard. It is certainly very important. And the
reason we were willing to invest information and be as
transparent as we were for several years with CFPB, I think, it
is important because it demonstrates to banks that we are not a
``move fast and break things'' company. That may be the name--
or sort of a label you want to paint Silicon Valley startups
with. But we are not in that class. We are a company that takes
regulation and working transparently with regulators seriously.
However, as I said earlier, it is absolutely not a panacea.
They care about the FDIC, they care about the OCC, they care
about State regulators, all of whom could decide to accept the
CFPB's no-action letter and its conclusions or could choose not
to. And that is why I think ultimately it is important to
clarify regulation.
Mr. Luetkemeyer. Why do you think more entities like you
have not gone the no-action letter route? There are not very
many, if any, that have done this, is that correct?
Mr. Girouard. There is none other to date, as far as I am
aware.
Mr. Luetkemeyer. Why do you think that you are the only one
that has done this? It would seem to me to give you a marketing
advantage from the standpoint--if I am a bank and you come to
me and you say, look, I have already had my modeling fall
within the guidelines of the CFPB and all of the other entities
out here that are regulating this, and I will continue to put
these processes in place to protect the integrity of our data,
it looks to me like you have to a win/win there. Why is there
nobody else doing that?
Mr. Girouard. My only conclusion I can draw from that is
one of a few things. Number one, they are not actually using
alternative data in a meaningful way.
Number two, they are using it, but they have found another
way, another path to creating comfort that they are within the
bounds of fair lending laws.
Or, three, they are using it, but they are not using it
responsibly. And I don't necessarily know which of those is the
answer.
Mr. Luetkemeyer. Very good.
Mr. Evans, your testimony points out that CFPB has
developed fair lending examinations related to credit models
and the Federal banking regulators have issued guidance to the
depositor institutions on third-party or vendor management,
including fintechs. However, despite this regulatory framework,
there seems to be a disconnect between lenders and fintechs and
the regulators that provide uncertainty in the fintechs' place.
Can you explain this?
Mr. Evans. Well, yes. And I think it goes back to
ultimately the fragmented nature of the regulatory system.
Fintechs experience uncertainty in that regard, because there
are a number of actors in that particular space.
CFPB's position on one thing may differ from the Federal
Reserve or the OCC's position. And so oversight of fintech
lending requires significant coordination. And the knowledge-
building initiatives that I talked about in my opening
statement would allow regulators to really understand the
fintech products and ensure that the regulatory framework is
adaptable and flexible.
Mr. Luetkemeyer. Okay. Very good. I see that my time has
expired. Thank you.
Thank you, Mr. Chairman.
Chairman Lynch. The Chair now recognizes the gentleman from
Texas, Mr. Green, for 5 minutes.
Mr. Green. Thank you, Mr. Chairman. And I thank you and the
ranking member for hosting this hearing.
I would also like to thank Mr. McAdams for allowing me to
proceed at this time. In fact and in truth, it would be his
turn, and he has allowed me to have the opportunity to proceed.
I would like to move first, if I may, and rather
expeditiously to Mr. Girouard.
Sir, in the model that you currently utilize, do you
maintain the traditional credit score and then do you add these
other, what you are calling, alternative datapoints to the
traditional score?
Mr. Girouard. We do. We vary--we use FICO score. We use--
Mr. Green. That is going to be enough, because I have a lot
to cover. I appreciate it.
Mr. Girouard. Okay.
Mr. Green. Thank you. I don't mean to be rude, crude, and
unrefined.
Mr. Girouard. Not at all.
Mr. Green. Okay. Thank you.
Friends, I started with Mr. Girouard for a reason. What we
are calling alternative data, in most circumstances--there may
be some that I am not covering--is really additional data. It
is additional data. My bill that I have is not about
alternative data, alternative meaning one or another. It is
about additional data. It is about what Mr. Girouard does when
he takes the traditional data and then he adds what we are
calling alternative, but it really is more data that we are
adding. We are not leaving out the traditional scores.
My bill does not require consumers to opt in. Consumers do
this of their own volition. They can allow their additional
data to be scored, and it can help a good many consumers, as
evidenced by what Mr. Girouard has called to our attention.
The bill is a bill that has metamorphosed. I confess that
initially we used the term, ``alternative,'' but we soon
realized that when people heard the term, ``alternative,'' they
assumed that we were somehow going to negate what was already
there as a traditional score.
Now, understanding that we are talking about additional--we
are talking about the utilities, we are talking about the rent,
but we simply added to what is already there, and in doing
this, I think we will give many consumers the opportunity to
own a home, and to make purchases that they would not
ordinarily be able to make.
Those that don't opt in will not be--they won't have that
traditional score in any way encroached upon, infringed upon.
It won't have an impact on that. Only those who opt in.
With that said, I want to give you an opportunity to ask me
a question. Let's turn the tables, if you don't mind, so that
we can become as clear as possible, perhaps perspicuously, so
that there is a better understanding of what this bill is
about.
I am not going to debate persons who want to have an
alternative credit scoring model. That is perfectly acceptable
to me. I would only suggest that if we focus on this bill, that
we use the term, ``additional credit scoring.''
Questions from any member of the panel, please?
Ms. Johnson. I have a question actually.
Mr. Green. Thank you.
Ms. Johnson. And Mr. Girouard may answer it, but it grows
directly out of your question. Thank you, Congressman, for
inviting us to ask.
In the first instance, we have described credit invisibles
as those who do not have a traditional credit score under the
FICO criteria.
To the extent that inclusion is our goal, which I think is
bipartisan motivation for the committee and our thoughtfulness
today--if inclusion is the goal and the idea that you propose
is that alternative data is additional data supplementing an
already robust methodology for analyzing consumer--the
likelihood of consumer default or predicting creditworthiness,
I am not sure I follow how credit invisibles are actually
captured if the data that is being used is not the primary
source of evaluation.
Mr. Green. If I may answer, because there are only 32
seconds left.
You could be a great Member of Congress, by the way, with
your question.
Here is how they are captured. Because they can opt in. And
if they have nothing more, that will be there, plus the
nothing, plus the something. I hate to be so elemental with the
explanation. But what I want to do is make it as clear as
possible that what we are doing is leaving the traditional,
whatever it happens to be, and then we bring these additional
points of data to the scoring process.
Now, given that my time is almost up, and by some standards
up, I see--
Mr. Scott. Will the gentleman yield for a moment?
Mr. Green. I will yield and beg that the Chair would not
look at the clock, if you will, please.
Mr. Scott. Okay. Very quickly, I think another part of
this--
Chairman Lynch. The gentleman will suspend. We can't be
doing this. You are over. If the gentleman wants to conclude
his thought, he can, but--
Mr. Green. I can't yield?
Chairman Lynch. The gentleman's time has expired. I'm
sorry.
The Chair now recognizes the gentleman from Ohio, Mr.
Gonzalez, for 5 minutes.
Mr. Gonzalez of Ohio. Thank you, Mr. Chairman, and Ranking
Member Hill for holding this hearing today, and thank you to
our witnesses. I believe this area is an incredible opportunity
to explore how new technologies can be deployed to allow more
Americans to gain access to credit. That is sort of the promise
or the hope, anyway, of the machine-learning technology.
And I share the sentiment that Ms. Johnson just raised,
which is the goal is to expand credit to as many Americans as
possible.
Mr. Girouard, I want to focus on your company specifically
in the context of the sandbox. And so, we will go there.
Bear with me for a second. You were founded in 2012,
according to Crunchbase anyway, and have raised, I think it was
$144 million in total funding.
At what point did you start working with the CFPB directly
in the funding stream?
Mr. Girouard. I believe our first meeting with the CFPB was
either in 2012 or 2013, about that time.
Mr. Gonzalez of Ohio. Okay. So really, from the beginning,
this was a concerted effort and a decision on your part?
Mr. Girouard. That is correct.
Mr. Gonzalez of Ohio. Okay. How big was the A, if you are--
I don't know if you are allowed to share that, but--
Mr. Girouard. I'm sorry?
Mr. Gonzalez of Ohio. How big was the series A run,
roughly? I will tell you where I am going so you can maybe
answer this.
I want you to talk about the benefits of the sandbox in
terms of allowing for more startups to enter this space.
Because you talked about the big banks potentially being able
to get into it. But I want to see more innovation. You guys
have an incredible team. I was on your site, a bunch of ex-
Googlers and very smart folks. I know there are plenty of folks
in Silicon Valley who would love to get into this space.
How would the sandbox enable that?
Mr. Girouard. The sandbox brings some clarity, which tends
to make the money flow in terms of these companies, first of
all, more entrepreneurs wanting to enter this space. When you
have a very highly regulated area with a lot of confusion, most
entrepreneurs will opt for something else.
If you want more entrepreneurial effort in this area,
bringing clarity will bring both the interest of the
entrepreneurs and the money from the investors, and that will
create companies that are going to make a difference over time.
Mr. Gonzalez of Ohio. So, one of the benefits of the
sandbox is not just that it gives Wells Fargo a chance, but
that it gives the next group of startups a chance as well?
Mr. Girouard. Without question.
Mr. Gonzalez of Ohio. Great. And then I want to shift to
some of the data privacy laws that you have kind of alluded to
as well.
California's privacy law is going to be coming into effect.
And we hear a lot throughout the industry about the problems
that is going to create.
Can you comment on how you see it affecting your business
specifically and AI in general?
Mr. Girouard. Sure. I believe there are real issues related
to privacy and large technology companies that need to be
addressed, and I know are being addressed. And I am very
appreciative of our home State, California, taking the lead on
this.
We are, of course, already preparing, reviewing, and
planning to adapt our practices, our product, to the California
law. What I would just generally add, of course, is a business
like ours operates at a national level, so it would certainly
be a step forward for us to have something of that sort, sort
of managed at a Federal level more than at a State level. But
having said that, we appreciate that is not the way the world
works, and we will adapt to California's law.
Mr. Gonzalez of Ohio. Yes. I think one of my concerns--and,
again, that I keep hearing is when you have this patchwork of
50 different State laws and you want to operate all over the
country, as does everybody, you are creating--not you--but
California has created a bit of chaos. And I know one thing
this committee is committed to is to solving that, which I am
excited about.
And then I guess kind of with my last question, as we are
thinking through what that national standard should be, what is
it about what the California law that you like, and what is it
that you think should be changed or different?
If you are not comfortable answering, that is fine.
Mr. Girouard. I am not sure I am comfortable enough to try
to state that here. Thank you.
Mr. Gonzalez of Ohio. Okay. Thanks. With that, I yield
back. Thanks.
Chairman Lynch. The gentleman yields back.
The Chair now recognizes the gentleman from Utah, Mr.
McAdams, for 5 minutes.
Mr. McAdams. Thank you, Mr. Chairman.
I want to thank the panelists for being here today. And I
care deeply about expanding financial inclusion. But I want to
make sure we are supporting an environment where all Americans
can access credit.
I do know that credit decisions can mean the difference
between a family qualifying for a home or a loan to buy a car
and the incredible life consequences that those decisions have
for each and every potential borrower.
We need to have appropriate consumer protections, and
consumer protections shouldn't be ignored while we get the dial
right to maximize the benefits while minimizing any potential
negative impacts.
But I want to zero in on that balance, the potential
benefits and the potential harm or questions that are raised
from the use of alternative data.
First, speaking towards the benefits, we have heard
testimony today that these alternative data factors are giving
lawmakers more confidence in who they can responsibly lend to,
meaning more consumers have access to credit, ideally at
competitive rates.
My first question is to you, Mr. Girouard. What percentage
of your loan portfolio would you estimate that your company can
make loans to because of the inclusion of alternative data
sources?
Or stated another way, if you were only allowed to use
traditional data sources, what percentage of your customers
would you not be able to lend to because you couldn't assess
their creditworthiness?
Mr. Girouard. Thank you, Congressman. That is a great
question.
That is exactly the data that I presented in my up-front
statements. What the CFPB asked us to do is to look at our
model, if we removed all what you might call alternative data
and used only traditional data.
The difference is, among the general population, we
described as--and this is among people who have applied for
loans at Upstart--is about 27 percent. More people are approved
because of the alternative data. But importantly, when you look
at the near-prime segment, which is people with somewhat lower
FICO scores, it is a 95 percent increase in approvals.
So, it is a very significant difference in improvement on
who we can approve due to the alternative data that we include.
Mr. McAdams. Thank you. And further on that point, some
alternative data factors are now being used to include--or
maybe not furthering the point--but in a different direction.
Some of those alternative data factors are now being used to
include online behavioral data such as online shopping habits,
and social network connections.
My next question is for Mr. Rieke. I believe you made this
distinction in your testimony between the types of datapoints
and the conventional alternative data.
Out of curiosity, how much of these alternative data
sources are moving the needle on a credit score? And I am not
referring to alternative data such as bill payments, or online
utility payments, of which I think most Americans would
intuitively understand why that could be included in this
credit score. But the online shopping habits, social network
usage, how much does that affect an individual's data score?
Are we talking 5 points of credit? One point? Fifty points?
And for someone who has a thin credit file, how much are these
factors weighted compared to traditional data factors?
Mr. Rieke. The short answer is, we don't know. Most of the
fringe alternative data that has to do with social media and
shopping habits and web behavior, there is a lot more hype than
reality, in terms of what I have been able to ascertain in our
research. There are a lot of start-up companies making some
pretty strong claims to the media, and then maybe once they
hire a lawyer kind of backing off of those or starting to
practice overseas.
And so there is some academic research studies that showed
web signals like what website you come from, whether you are
using an iPhone or Android phone, can really help kind of
narrow in on what kind of person you are, mostly because those
are proxies for wealth.
But in terms of the real science and research around the
predictiveness of fringe alternative data, it is really hard to
say, because companies hold that data close, and I think there
is a lot less of that really happening in the United States
today because of issues with the ECOA.
Mr. McAdams. Do consumers understand what information on
them is being collected and used in their credit decisions, and
are there industry standard practices on disclosure?
Mr. Rieke. I am not aware of any kind of formal industry
standard best practices. There are some private businesses,
like Credit Karma, that do, in my view, a pretty good job of
showing the basic FICO score factors and helping people make
sense of that. I have seen nothing resembling that for more
complex or fringe datasets.
Mr. McAdams. Ms. Wu?
Ms. Wu. If I may address that, Congressman, the Fair Credit
Reporting Act and the Equal Credit Opportunity Act do require
that if someone is turned down or priced higher for credit, a
notice goes out explaining what the reasons were. That is
really, really important because of the impact these decisions
have on people's lives.
One of my concerns is with machine-learning and AI, where
the machine itself is determining what factors to use. How do
you make sure consumers have adequate information about what is
going on inside the black box?
The other thing that I wanted to quickly mention is another
type of nonfinancial data that is being used, and Upstart is
using, which is education. And I worry about the impact of
using education as a form of alternative data. Because we know
of the great inequality and racial disparities in terms of what
kind of degrees people get.
Mr. McAdams. Thank you, Ms. Wu.
My hope would be that we can use this data to not only
expand access to credit for more individuals but it can also be
used as a form of improving financial literacy, if individuals
know what data is being used and what things they might do as
individuals to move the needle as well. And hopefully, that
doesn't include unfriending their friends on Facebook.
Thank you. And I yield back.
Chairman Lynch. I thank the gentleman.
The gentleman from Florida, Mr. Lawson, is now recognized
for 5 minutes.
Mr. Lawson. Thank you, Mr. Chairman.
And I welcome the witnesses to the committee.
You might have already responded on this particular issue,
but it is important to me. As most of you know, credit reports
do not tell the full story of one's economic status. As a
matter of fact, they could have a false narrative, depending on
the circumstances.
It is estimated that the use of alternative data such as
utility payments, rent payments, cellphone payments, and other
forms could expand access to credit to over 40 million
consumers here in the United States.
Can everyday payments such as rent payments or cellphone
payments paint a more accurate picture of someone's ability to
pay? Are we headed in the right direction by saying that this
would be a true picture of the individual's ability to get
credit?
And everyone--all of you, if you care to respond to that,
it would be great. I would just like to know--and you might
have already talked about it. But this is talked about all over
the place, especially in Florida, where we have a high
concentration of students in my district, about 80,000 or
90,000 of them.
So, I am anxious to know what your statement is going to
be. And will I tell you, the reason being is when I was coming
out of college, I was given all of these credit cards, Exxon,
all of them, and so I started using them. And because the
invoices, I guess, were going to the dormitory where I used to
live, nobody forwarded them to me.
When I got ready to try to get a loan or do some other
thing, it came up. And it had been over 1 year or 2 years or
so. And I just thought maybe, because I had graduated from
school, they just gave me free credit. I didn't know. And that
is one of the things that affects a lot of students, because
they move around to different places.
That is the reason I wanted to bring that question up and
have all of you respond to it.
Ms. Wu. Congressman, that is a great point. And you are
absolutely right. Traditional credit scores and credit reports
often don't reflect the true financial behavior of a consumer,
precisely because of things like your experience or the fact
that there are a lot of negative marks for things like medical
debt, where people got sick and debts were sent to debt
collectors.
And we know that even among people with a subprime score,
most of them, if you give them credit, will pay it back.
Something like 80 percent of consumers who score a 600 will pay
it back.
So, alternative data could be useful, especially things
like bank account data or rent, and if people choose, if
consumers want to supply their utility and cellphone payments.
Again, the devil is in the details; how you do it is important.
Second-chance scores are better than putting this information
in the traditional credit reports.
We are concerned about factors that lead to more inequality
or reflect inequality. As Mr. Rieke said, using geographic
neighborhood or using what kind of degree you have, because we
know that over 36 percent of non-Hispanic whites have a college
degree, but less than 16 percent of Hispanics and 23 percent of
African Americans do. So, if you use whether or not a consumer
has a college degree, it is going to have some stark racial
disparities.
Mr. Rieke. Congressman, I want to just say I think the
question of ability to repay is a really good target for this.
We are talking about expanding access to credit, but we are not
doing anyone any favors by giving them predatory products or
too many credit products. That can destroy lives.
So, I think ability to repay is a really important nexus
between this question of alternative data and what are we
trying to find out, but also a pretty strong consumer
protection standard.
Ms. Johnson. And I would just echo the earlier reflections.
Thank you, Congressman, for this very important question about
a really important demographic: students.
We know from the New York Federal Reserve that households
face $13 trillion in debt as of the end of the year, fourth
quarter 2018, and $1.5 trillion in student debt.
Student debt for a particular population, and most recently
graduated generations of students, is staggering and crippling.
And unlike past generations, these students are moving out of
their parents' houses later, and they are having extended job
searches.
So, the predatory credit card tactics, the idea of drawing
them into spaces where their credit histories will be marred,
or they won't have credit histories at all because of how long
it is taking them to dig themselves out of educational debt,
really does prompt a need, a very significant need for
alternative mechanisms, pathways for them to gain access to
credit.
I think we are all just thoughtful about how to do that in
a way that is effective for consumers, protects their privacy,
and is thoughtful about discrimination.
Mr. Lawson. Mr. Chairman, I know I am out of time, so I
yield back.
Chairman Lynch. I thank the gentleman.
We have agreed to just do one more brief round of
questioning, so I yield myself 5 minutes for questioning.
In our discussions with Facebook, in an effort to try to
get some accountability on the protection of personal financial
data, the issue of assigning fiduciary responsibility for the
handling of information was suggested. And I would say that the
response from Facebook was evasive, to be generous.
What about that concept that there would be liability for
mishandling the financial data that we surrender to fintech
companies? Is that something that is workable, do you think,
Ms. Wu?
Ms. Wu. I thank you for the question, Chairman Lynch. I
think that whether you call it a fiduciary duty, or you have
legal duties or legal accountability for losing someone's data,
there should be a regulatory scheme in place that holds Big
Data companies, whether they be credit bureaus or Facebook, to
accountability for losing sensitive personal information and
data.
Chairman Lynch. Yes. I guess I should just put a finer
point on that.
When I say ``fiduciary'', I mean in the classic financial
services sense where a fiduciary is required to handle that
information in the best interest of the customer, and not sell
it or deploy it for other purposes. That is what I am getting
at.
I am trying to make sure what happens with personal
financial data is not what happened with general data that is
being vacuumed up and used and deployed without the knowledge
or consent, meaningful consent, of individual consumers. That
is what I am trying to get at.
Ms. Johnson?
Ms. Johnson. Yes. I'd just say thank you, Congressman.
We have examples and models of how to protect financial
transaction data that exist in current regulation. The Gramm-
Leach-Bliley Act, for example, specifically requires that
financial institutions disseminate initial and annual privacy
notices to customers regarding financial transactions. The
provision of the Gramm-Leach-Bliley Act that I am describing
enables consumers to specifically opt out in certain instances
of other uses of financial data.
It also requires financial institutions to anonymize data,
essentially to the extent that they use data for other
purposes, to aggregate the data and ensure that the data is
anonymous and not directly reflective or you couldn't easily
discern that it refers to a particular consumer based on the
profile.
Now, I will say that data scientists at Princeton and
Stanford recently published a study illustrating that they
could successfully decode, if you will, anonymized data and
establish users' identities based on social networking
profiles. The idea that this could happen is obviously
concerning and gives us pause.
But I do think that we have some examples in existing
legislation and regulation that could offer a point of
departure for having a conversation about how to create
accountability, responsibility, and transparency for anyone
who--or entities who are gathering, storing, and distributing
personal consumer financial information.
Chairman Lynch. Great. Let me just jump over to Mr.
Girouard. I pulled up Upstart's terms of service agreement. And
it is a lot shorter than Facebook's. Thank you very much. It is
about 8 pages.
But there is one section in here on limitation of
liability. And it says the customer--``you agree that all
access and use of the site and its contents and your use of the
products and services is at your own risk.''
In no event shall we or any lender be held liable for any
damages, including direct or indirect, special, incidental or
consequential damages, losses or expenses arising in connection
with the site or any linked site or use thereof or inability to
use by any party or in connection, or for failure of
performance, error, omission, interruption, defect, delay in
operation, transmission, computer viruses, et cetera.'' It is
very, very broad.
And this is one of those things where you have to click,
``I agree.'' And either you agree to all of this or you don't
use the site, you don't use Upstart.
Is that fair to the consumer, do you think?
Mr. Girouard. Chairman Lynch, I certainly wish we had a
better option. But it is a complicated world. And certainly a
business needs to protect its interests.
Somebody could say the internet crashes and I was in the
middle of getting a loan, and that just cost me my ability to
buy a home or do something else.
Chairman Lynch. This basically shuts off the consumer from
any recovery at all under any circumstances. I understand cases
like that where the technology breaks down, you could say in
that case, we don't accept any liability.
But in the terms of this, it is airtight where, you
basically block off any type of accountability; you are beyond
reach by this agreement. This is the type of thing I worry
about.
And I just--
Mr. Girouard. It is a fair concern. I genuinely believe we
have the highest consumer ratings we have ever found in our
industry in terms of our respect and the way we treat customers
or prospective customers.
Chairman Lynch. I appreciate that. I am just concerned that
no one has any recourse based on the terms of this agreement.
With that, I yield to my friend, the gentleman from
Arkansas, for 5 minutes.
Mr. Hill. Thank you, Mr. Chairman.
Just following up on that, Mr. Girouard, that particular
thing he read, which obviously we haven't read, but I admired
him in real-time going to your website--and it is a thing of
beauty. And that is the difference between the House and the
Senate, Ms. Johnson. You seem rather concerned about our
technological capabilities here.
That is really talking about your--the connectivity between
the customers and you, isn't it, protecting you from liability,
from the internet or from the website or the connection? Isn't
that what that is mostly addressing?
Mr. Girouard. Certainly. Any commercial agreement between a
consumer and a business has to have reasonable protections in
it. I am not an attorney, let me just admit that. So, for me to
say what is an appropriate limitation of liability is not
something I am probably equipped to speak about today.
Mr. Hill. But we appreciate that. And that is something
that we all deal with in any kind of commercial transaction.
And I think it is made worse sometimes over the internet,
because you don't have any kind of face-to-face explanation and
it is a little bit more passive. But I think making sure
consumers know what they are getting into is important.
Mr. Evans, I wanted to ask you. I read your testimony--
thanks for it--about this harmonization between the regulatory
agencies. You have urged them to adopt a harmonized approach to
guidance under use of alternative data and also on the sandbox
issue.
Did they give you a timeframe when they would have a
harmonized view on that?
Mr. Evans. They did not. They all agreed with the
recommendation and appreciated the spirit of it.
Mr. Hill. Right. I think that is something we have all
talked about here. We will be certainly pressing them for this
more unified approach on vendor due diligence and an IT exam,
guidance on what is an appropriate bank risk profile in this
arena, how to do the board review of vendor due diligence. All
of this is important.
Mr. Evans, did you, in your work on this, see any reason to
make statutory changes to the Fair Credit Reporting Act or the
Equal Credit Opportunity Act?
Mr. Evans. There are certainly some issues. The scope of
the work didn't allow us to rigorously collect all the evidence
for us to provide a conclusion on that, but for sure, the
complexity of some of the algorithms could limit the type of
information that a company is able to provide if they were to
deny credit to an individual.
Mr. Hill. Yes. I read the reliability of data point in your
testimony, and we have talked about that with other witnesses
at previous panels, with just asking the simple question, using
an AI-based model, a machine-learning model, that uses
additional data, just as Mr. Girouard has described, we have
asked the question, is it auditable? And in the instances that
we have had, the answer has been consistently yes. And the
evidence of that is not hypothetical because the loans
originated are subject to a fair lending exam by a commercial
bank or portfolio buyer. And then, of course, in Mr. Girouard's
case, they are also auditable by the CFPB's analysis of this
data. So, as long as a commercial bank is a partner in it, from
my point of view, that seems like the disparate impact test,
the HMDA test if it would be a mortgage, or fair lending, or
equal opportunity type assessments would be made. Is that
generally your view from the work that you did? While you may
have found six industry stakeholders who you had concerns
about, it is doable to validate a model and have an audit trail
as to how the determination was made, isn't it?
Mr. Evans. In the models that we considered, I would say
yes, but I would say our work was limited to the fintech
companies we actually talked to. There could be classes of
models that--
Mr. Hill. Yes, but the obligation is on that user, that
innovator, whether it is a bank partner or a fintech nonbank
partner, to demonstrate that they comply with all the
compliance obligations of the Federal Government.
Mr. Girouard, you have said already, it is auditable, and
the CFPB audits it, and then your bank partner audits it. That
is correct, right, you can validate your model and backtrack
it?
Mr. Girouard. That is correct.
Mr. Hill. And one question that didn't come up today--I
didn't hear it--is back-testing. We have had the most ideal
circumstances of the past 11 years, thanks to the unbelievable
policies of our Federal Reserve, so that we have a very benign
interest rate environment, we have rising real wages, we have a
rising economy. What about back-testing your $4 billion you
have originated? Looking back under more adverse credit
circumstances, what have you learned?
Mr. Girouard. Sure. That is a valid concern, and certainly
any lender, to earn its stripes, really needs to perform
through an economic cycle. First, we test the best we can by
simulating higher unemployment. So, there are ways we can
simulate higher unemployment and look at the impact we expect
it to have on our loan portfolio.
Second, because there actually are recessions, what I might
call microrecessions in parts of the country, small parts of
the country, we can actually look at loan performance in those
particular areas. It is not a perfect proxy, but it is a way to
understand how our loans would perform in a weaker economy.
Mr. Hill. I thank the panel, and I yield back, Mr.
Chairman.
Chairman Lynch. The gentleman yields back.
The Chair now recognizes the gentleman from Georgia, Mr.
Scott, for 5 minutes.
Mr. Scott. Yes, Mr. Lynch, this has indeed been a very,
very informative hearing, and our panelists are well-prepared
and very informative. Thank you for this. As I said in my
opening statement, we are at a new frontier here, and it is an
exciting frontier. But let's go back to the alternative data
because I think that is really the fundamental foundation of
this hearing.
Now, there are different kinds of alternative data that I
am hearing. So, you may say a utility bill, or you may say your
online habits, or you may say your educational attainment. Tell
me, how does a lender weigh these? Would they give more
preference to your educational attainment? Does that have the
same weight as your utility bill? How is it used by lenders
when they are making these underwriting decisions? Let me start
with you, Ms. Wu--or is there anybody who has an immediate
answer to this? Or if we don't have an answer to it, don't you
think we should?
Mr. Girouard. I am glad to answer, Congressman, as one who
does exactly what you are asking about. Traditional lending
systems are what you might describe as rules-based. Okay? A
series of, if the person's FICO is between this and that, if
their income is between this and that, here is what we can
offer them. That is what you might call a rules-based system.
These newer models, what are sometimes termed machine-learning
or AI, are far more sophisticated than that. What they really
do is they look at the history of loans and the data that has
come in about those loans, and it learns about how each of
those factors actually impacted the performance of the loans.
So, it is not a human sitting there trying to evaluate whether
education or FICO or something else is more predictive. The
software will learn over time what is the best combination of
that information, that will be the most accurate model.
The goal of a company like Upstart is to build a more
accurate model, which tends to lead to higher approval rates,
and we do that by relying on the software to do things that
humans can't realistically do, which is to consider not two or
three variables, but hundreds or maybe even a thousand
variables, and that results in a more accurate credit model.
And, fortunately, it also results in one that approves more
people at lower rates.
Mr. Scott. So, you are saying that the machine has a more
accurate ability to give a certain alternative data more weight
over the other? I guess what I am asking is, is there more
benefit for one type of alternative data to be helpful to the
unbanked or underbanked? There is a variety of things. Maybe
also added in there, did he serve in the service? What was his
rank? Was she a schoolteacher? What was the caliber of her
employment structure? Do you see what I am saying? There seems
to me that if we just put all this up to the machine, I am not
sure it is giving it--there ought to be some weight here.
Ms. Wu. That is a great point, Congressman Scott, and
something I am concerned about. Because data that kind of looks
like credit, as Mr. Rieke said, rent or bank account or utility
bills, everybody, if they have a good history, can benefit from
that. But if you are talking about things like education, how
many college grads really are credit invisible? Are we really
expanding access to credit if we say, we will give you a higher
score if you graduate from college, especially if you graduate
from an elite institution?
Mr. Scott. Right.
Ms. Wu. And then, in terms of machine-learning, one thing I
want to add is, yes, it might be up to the lender to give that
weight, but the lender has to be able to explain it. And if all
this data is going into a big black box and the machine is
deciding what is more important or not, you have to be able to
put it on a piece of paper and explain to the consumer what was
more important. The law requires it, because we need
transparency in lending.
Mr. Scott. Yes. Thank you, Mr. Chairman. Great panel.
Mr. Evans. And so there is--I'm sorry.
Chairman Lynch. If you can be quick, Mr. Evans.
Mr. Evans. There is an important tradeoff to think about:
transparency versus predictability. And that is something we
have to grapple with and it is something about which the
regulators can offer guidance.
Chairman Lynch. Thank you very much. I appreciate that.
The Chair now recognizes the gentleman from Florida, Mr.
Lawson, for 5 minutes.
Mr. Lawson. Thank you.
Mr. Hill. Mr. Chairman, I need to just, I think, politely
object. I thought we were going to do just ourselves for a
final round of questioning, and I have no more Members here.
And so, with all due respect to my friends, that is not really
what we agreed to, so--
Chairman Lynch. Okay. I understand the gentleman is short
on time, and I totally respect him, but when I asked for a
second round, I meant a second round for the Members.
Mr. Hill. But you said a second round for the two of us,
sir.
Chairman Lynch. Sir, I was not aware that that is the way
you understood that.
Mr. Hill. That is the way you said it, and that is the way
I understood it.
Chairman Lynch. Perhaps I meant the two of us, meaning the
two sides. I know the gentleman had no other--
Mr. Hill. We have no other Members, so I think just in
fairness under the rules, with the deference of Mr. Scott being
the last questioner, that would be appreciated.
Chairman Lynch. The gentleman, Mr. Green, has yielded and--
Mr. Lawson. I yield back.
Chairman Lynch. --the gentleman, Mr. Lawson, agrees as
well?
Mr. Lawson. Yes.
Chairman Lynch. Okay.
Without objection, the Chair moves to include in the record
of this hearing a letter from the Cato Institute, Center for
Monetary and Financial Alternatives, dated July 24, 2019; a
letter from the Financial Data and Technology Association,
dated July 23, 2019; and also an article from the Student
Borrower Protection Center, entitled, ``Educational Redlining:
The Use of Educational Data in Underwriting.'' Without
objection, it is so ordered.
I would like to thank our witnesses for their testimony
today.
The Chair notes that some Members may have additional
questions for this panel, which they may wish to submit in
writing. Without objection, the hearing record will remain open
for 5 legislative days for Members to submit written questions
to these witnesses and to place their responses in the record.
Also, without objection, Members will have 5 legislative days
to submit extraneous materials to the Chair for inclusion in
the record.
This hearing is now adjourned.
[Whereupon, at 12:12 p.m., the hearing was adjourned.]
A P P E N D I X
July 25, 2019
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