82 FR 11183 - Request for Information Regarding Use of Alternative Data and Modeling Techniques in the Credit Process

BUREAU OF CONSUMER FINANCIAL PROTECTION

Federal Register Volume 82, Issue 33 (February 21, 2017)

Page Range		11183-11191
FR Document		2017-03361

The Consumer Financial Protection Bureau (CFPB or Bureau) seeks information about the use or potential use of alternative data and modeling techniques in the credit process. Alternative data and modeling techniques are changing the way that some financial service providers conduct business. These changes hold the promise of potentially significant benefits for some consumers but also present certain potentially significant risks. The Bureau seeks to learn more about current and future market developments, including existing and emerging consumer benefits and risks, and how these developments could alter the marketplace and the consumer experience. The Bureau also seeks to learn how market participants are or could be mitigating certain risks to consumers, and about consumer preferences, views, and concerns.

Federal Register, Volume 82 Issue 33 (Tuesday, February 21, 2017)

[Federal Register Volume 82, Number 33 (Tuesday, February 21, 2017)]
[Notices]
[Pages 11183-11191]
From the Federal Register Online [www.thefederalregister.org]
[FR Doc No: 2017-03361]

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BUREAU OF CONSUMER FINANCIAL PROTECTION

[Docket No. CFPB-2017-0005]

Request for Information Regarding Use of Alternative Data and
Modeling Techniques in the Credit Process

AGENCY: Bureau of Consumer Financial Protection.

ACTION: Notice and request for information.

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SUMMARY: The Consumer Financial Protection Bureau (CFPB or Bureau)
seeks information about the use or potential use of alternative data
and modeling techniques in the credit process. Alternative data and
modeling techniques are changing the way that some financial service
providers conduct business. These changes hold the promise of
potentially significant benefits for some consumers but also present
certain potentially significant risks. The Bureau seeks to learn more
about current and future market developments, including existing and
emerging consumer benefits and risks, and how these developments could
alter the marketplace and the consumer experience. The Bureau also
seeks to learn how market participants are or could be mitigating
certain risks to consumers, and about consumer preferences, views, and
concerns.

DATES: Comments must be received on or before May 19, 2017.

ADDRESSES: You may submit responsive information and other comments,
identified by Docket No. CFPB-2017-0005, by any of the following
methods:
Electronic: Go to http://www.regulations.gov. Follow the
instructions for submitting comments.
Mail: Monica Jackson, Office of the Executive Secretary,
Consumer Financial Protection Bureau, 1700 G Street NW., Washington, DC
20552.
Hand Delivery/Courier: Monica Jackson, Office of the
Executive Secretary, Consumer Financial Protection Bureau, 1275 First
Street NE., Washington, DC 20002.
Instructions: Please note the number associated with any question
to which you are responding at the top of each response (you are not
required to answer all questions to receive consideration of your
comments). The Bureau encourages the early submission of comments. All
submissions must include the document title and docket number. Because
paper mail in the Washington, DC area and at the Bureau is subject to
delay, commenters are encouraged to submit comments electronically. In
general, all comments received will be posted without change to http://www.regulations.gov. In addition, comments will be available for public
inspection and copying at 1275 First Street NE., Washington, DC 20002,
on official business days between the hours of 10 a.m. and 5 p.m.
Eastern Standard Time. You can make an appointment to inspect the
documents by telephoning 202-435-7275.
All submissions, including attachments and other supporting
materials, will become part of the public record and subject to public
disclosure. Sensitive personal information, such as account numbers or
Social Security numbers, or names of other individuals, should not be
included. Submissions will not be edited to remove any identifying or
contact information.

FOR FURTHER INFORMATION CONTACT: For general inquiries, submission
process questions or any additional information, please contact Monica
Jackson, Office of the Executive Secretary, at 202-435-7275.

Authority: 12 U.S.C. 5511(c).

SUPPLEMENTARY INFORMATION: The Bureau would like to encourage
responsible innovations that could be implemented in a consumer-
friendly way to help serve populations currently underserved by the
mainstream credit system. To that end, in reviewing the comments to
this request for information (RFI), the Bureau seeks not only to
understand the benefits and risks stemming from use of alternative data
and modeling techniques but also to begin to consider future activity
to encourage their responsible use and lower unnecessary barriers,
including any unnecessary regulatory burden or uncertainty that impedes
such use.
The Bureau encourages comments from all interested members of the
public. The Bureau anticipates that the responding public may encompass
the following groups, some of which may overlap in part:
Individual consumers;
Consumer, civil rights, and privacy advocates;
Community development and service organizations;
Lenders, including depository and non-depository
institutions;
Consumer reporting agencies, including specialty consumer
reporting agencies;
Data brokers and aggregators;
Model developers and licensors, as well as companies
involved in the analysis of new or existing models;
Consultants, attorneys, or other professionals who advise
market participants on these issues;
Regulators;
Researchers or members of academia;
Telecommunication, utility, and other non-financial
companies that rely on consumer data for eligibility decisions;
Participants in non-U.S. consumer markets with knowledge
of or experience in the use of alternative data or modeling techniques
for use in the credit process; and
Any other interested parties.
All commenters are welcome to respond in any manner they see fit,
including by sharing their knowledge of standard practices, their
understanding of the market as a whole, or their own positions and
views on the questions included in this RFI. Commenters may also choose
to answer only a subset of questions. The information obtained in
response to this RFI will help the Bureau monitor consumer credit
markets and consider any appropriate steps. Comments may also help
industry develop best practices. The Bureau seeks information
predominantly pertaining to products and services offered to consumers.
However, because some of the Bureau's authorities relate to small
business lending,\1\ the Bureau welcomes information about alternative
data and modeling techniques in business lending markets as well.
Information submitted by financial institutions should not include any
personal information relating to any customer, such as name, Social
Security

[[Page 11184]]

number, address, telephone number, or account number.
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\1\ For example, the Equal Credit Opportunity Act covers both
consumer and commercial credit transactions. 15 U.S.C. 1691 et seq.
In addition, section 1071 of the Dodd-Frank Act requires data
collection and reporting for lending to women-owned, minority-owned,
and small businesses. The Bureau has yet to write regulations
implementing that section but it has begun that process.
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For the purposes of this RFI, we define the following terms. None
of these definitions should be construed as statutory or regulatory
definitions or descriptions of statutory or regulatory coverage.
``Traditional data'' refers to data assembled and managed
in the core credit files of the nationwide consumer reporting agencies,
which includes tradeline information (including certain loan or credit
limit information, debt repayment history, and account status), and
credit inquiries, as well as information from public records relating
to civil judgments, tax liens, and bankruptcies. It also refers to data
customarily provided by consumers as part of applications for credit,
such as income or length of time in residence.
``Alternative data'' refers to any data that are not
``traditional.'' We use ``alternative'' in a descriptive rather than
normative sense and recognize there may not be an easily definable line
between traditional and alternative data.
``Traditional modeling techniques'' refers to statistical
and mathematical techniques, including models, algorithms, and their
outputs, that are traditionally used in automated credit processes,
especially linear and logistic regression methods.
``Alternative modeling techniques'' refers to all other
modeling techniques that are not ``traditional,'' including but not
limited to decision trees, random forests, artificial neural networks,
k-nearest neighbor, genetic programming, ``boosting'' algorithms, etc.
We use ``alternative'' in a descriptive rather than normative sense and
recognize that there may not be an easily definable line between
traditional and alternative modeling techniques.
``The credit process'' refers to all the processes and
decisions made by the creditor during the full lifecycle of the credit
product, including marketing, pre-screening, fraud prevention,
application procedures, underwriting, account management, credit
authorization, the setting of pricing and terms, as well as the
renewal, modification, or refinancing of existing credit, and the
servicing and collection of debts.

Part A: Traditional Automated Credit Process and Its Alternatives

Most of today's automated decisions in the credit process use
traditional modeling techniques that rely upon traditional data
elements as inputs. When lenders make decisions about consumers
relating to applications for credit, increases or reductions in credit
lines, extensions of new offers of credit, or other decisions in the
credit process, lenders typically evaluate consumers using a standard
set of information that includes consumer-supplied data (such as
income, assets and, if secured, any collateral) and other traditional
data supplied by one or more of the nationwide consumer reporting
agencies. Many lenders base their decisions, in whole or in part, on
scores using traditional data as inputs and generated from
commercially-available, third-party models such as one of the many
developed by FICO or VantageScore Solutions. Other lenders may base
their decisions, in whole or in part, on proprietary scoring algorithms
that use traditional data, and perhaps scores from these third-party
models, as well as consumer-supplied information, as inputs. In
addition to using common inputs, there is similar consistency in the
modeling techniques used to generate these automated decision engines.
They have predominantly been developed using multivariate regression
analysis to correlate past credit history and current credit usage
attributes to consumer credit outcomes to determine whether, based on
the performance of other previous consumers who had similar attributes
at the time credit was extended, it is likely that the consumer being
evaluated will default on or become seriously delinquent on the loan
within a certain period of time (often 1-2 years). These traditional
data and modeling techniques have facilitated the standardization and
automation of the credit process, leading to efficiencies in the
provision of credit over the past few decades.
Yet the use of traditional data and modeling techniques has left
some important gaps in access to mainstream credit for certain consumer
groups and segments. The Bureau estimates that 26 million Americans are
``credit invisible,'' meaning that they have no file with the major
credit bureaus, while another 19 million are ``unscorable'' because
their credit file is either too thin or too stale to generate a
reliable score from one of the major credit scoring firms.\2\ Most of
these 45 million Americans are underserved by the mainstream credit
system and they are disproportionately Black and Hispanic, low-income,
or young adults. Some populations, like those recently widowed or
divorced or recent immigrants, have difficulty accessing the mainstream
credit system because they have not established a long enough credit
history on their own or in this country. Some underserved consumers
instead resort to high-cost products that may not help them build
credit history.
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\2\ CFPB, Data Point: Credit Invisibles (May 2015), available at
http://files.consumerfinance.gov/f/201505_cfpb_data-point-credit-invisibles.pdf (figures are from 2010 Census).
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Several commentators have suggested that alternative data and
modeling techniques could address this problem and reach some of the
millions of consumers currently shut out of the mainstream credit
system and enable others to obtain more favorable pricing based on more
refined assessments of their risks.\3\ Discussions point to the wide
array of other data sources beyond traditional credit files that could
be used to assess the creditworthiness of borrowers, including so-
called ``big data.'' \4\ In addition, increased computing power and the
expanded use of machine learning to mine massive datasets could
potentially identify insights not otherwise discoverable through
traditional methods. The application of alternative data and modeling
techniques might also improve decisions in the credit process by
improving the predictiveness of credit-related models, by lowering the
costs of sourcing and analyzing data, or through other process
improvements such as faster decisions.
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\3\ See, e.g., PERC, Give Credit Where Credit Is Due: Increasing
Access To Affordable Mainstream Credit Using Alternative Data (Dec.
2006), available at http://www.perc.net/publications/give-credit-where-credit-is-due/; CFSI, The Predictive Value of Alternative
Credit Scores (Nov. 2007), available at http://www.cfsinnovation.com/Document-Library/The-Predictive-Value-of-Alternative-Credit-Scores;
\4\ ``Big data'' is a distinct concept from alternative data,
though some alternative data may have the attributes generally
ascribed to ``big data.'' In the FTC's words, ``A common framework
for characterizing big data relies on the `three Vs,' the volume,
velocity, and variety of data, each of which is growing at a rapid
rate as technological advances permit the analysis and use of this
data in ways that were not possible previously.'' FTC, Big Data: A
Tool for Inclusion or Exclusion? Understanding the Issues (Jan.
2016), available at https://www.ftc.gov/system/files/documents/reports/big-data-tool-inclusion-or-exclusion-understanding-issues/160106big-data-rpt.pdf.
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If these claimed benefits prove valid, the use of alternative data
and modeling techniques could significantly reshape the consumer (and
business) credit market. Potentially millions of consumers previously
locked out of mainstream credit could become eligible for credit
products that might help them buy a car or a home. An increasing
ability for lenders to accurately assess risk could reduce the price of
credit for those who are shown to be good risks (although it could
increase the price of credit for those shown to be worse risks), and
might even reduce the overall average price of credit for those who
qualify for credit. The process of

[[Page 11185]]

applying for credit could become more streamlined and convenient.
At the same time, other commentators have pointed out that
alternative data and modeling techniques could present risks for
consumers. These risks include but are not limited to potential issues
with the accuracy of alternative data and modeling techniques; the lack
of transparency, control, and ability to correct data that might result
from their use; potential infringements on consumer privacy; and the
risk that certain data could dampen social mobility, result in
discriminatory outcomes, or otherwise disadvantage certain groups,
characteristics, or behaviors.
The Bureau seeks to learn more about these potential benefits and
risks. In further educating ourselves and the public, the Bureau seeks
to encourage responsible uses of alternative data and modeling
techniques while mitigating the various risks.

Part B: Alternative Data and Modeling Techniques

Based on its research to date, the Bureau is aware of a broad range
of alternative data and modeling techniques that firms are either using
or contemplating. These innovations may be in different stages of
development and market adoption. As set forth below, the Bureau seeks
more information about the stages of development and extent of adoption
of these innovations. In some cases they are broadly used by a wide
range of market participants, while others are in earlier stages of
development. Some may be used often in fraud detection or marketing,
for example, but rarely in underwriting. Some have been developed by
established data aggregators or model developers who license their
technologies or ``platforms'' to lenders; others have been developed
for proprietary use by established lenders; and still others are being
used by early stage lenders as a basis for lending at lower cost or
profitably in certain channels or to consumer segments that established
lenders have not traditionally served or can only serve at higher cost.
Among the numerous online or marketplace lenders that have formed over
the past few years, many have identified use of proprietary alternative
data or machine learning techniques as central to their business
strategies and comparative advantage.
Just how ``alternative'' or ``traditional'' certain data or
modeling techniques are depends on one's perspective. Labeling data or
modeling techniques as ``alternative'' is not intended as a normative
judgment, but to describe the fact that they have not customarily been
used in decisions in the credit process. Any mention in this document
of particular types of alternative data or modeling techniques should
not be construed as endorsement or disapproval by the Bureau.
Data that some have labeled ``alternative'' include but are not
limited to the following: \5\
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\5\ This list is purely descriptive, and nothing should be
implied from the inclusion or exclusion of any data.
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Data showing trends or patterns in traditional loan
repayment data.
Payment data relating to non-loan products requiring
regular (typically monthly) payments, such as telecommunications, rent,
insurance, or utilities.
Checking account transaction and cashflow data and
information about a consumer's assets, which could include the
regularity of a consumer's cash inflows and outflows, or information
about prior income or expense shocks.
Data that some consider to be related to a consumer's
stability, which might include information about the frequency of
changes in residences, employment, phone numbers or email addresses.
Data about a consumer's educational or occupational
attainment, including information about schools attended, degrees
obtained, and job positions held.
Behavioral data about consumers, such as how consumers
interact with a web interface or answer specific questions, or data
about how they shop, browse, use devices, or move about their daily
lives.
Data about consumers' friends and associates, including
data about connections on social media.
Modeling techniques that some have labeled ``alternative'' include
but are not limited to the following:
Decision trees (or sets of decision trees, such as
``random forests'').
Artificial neural networks.
Genetic programming.
``Boosting'' algorithms.
K-nearest neighbors.
Given the rapidly evolving credit market landscape, the Bureau is
eager to learn more about types of alternative data and modeling
techniques, including but not limited to those listed above, and their
uses and impacts.

Part C: Potential Benefits and Risks Associated With Use of Alternative
Data and Modeling Techniques in the Credit Process

Prior Research and Interest in Alternative Data and Modeling Techniques

The Bureau is aware that several market participants,\6\ consumer
advocates,\7\ regulators, and other commentators have identified the
use of alternative data and modeling techniques as a source of
potential opportunities and risks. Without seeking to summarize the
full range of prior work, we note here a few relevant recent
publications by other Federal entities.\8\ In September 2014, the
Federal Trade Commission (FTC) held a public workshop on the topic of
``Big Data'' and subsequently published a report in January 2016
entitled ``Big Data: A Tool for Inclusion or Exclusion?'' \9\ This
report outlined potential consumer benefits and risks broadly, rather
than those specific to credit decisions. The FTC found that big data
``is helping target educational, credit, healthcare, and employment
opportunities to low-income and underserved populations'' but could
also contain ``potential inaccuracies and biases [that] might lead to
detrimental effects, including discrimination, for low-income and
underserved populations.'' \10\
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\6\ See, e.g., FICO, ``Can Alternative Data Expand Credit
Access?'' (Dec. 2015), available at http://subscribe.fico.com/can-alternative-data-expand-credit-access; TransUnion, ``The State of
Alternative Data,'' available at https://www.transunion.com/resources/transunion/doc/insights/research-reports/research-report-state-of-alternative-data.pdf.
\7\ See, e.g., National Consumer Law Center, Big Data: A Big
Disappointment for Scoring Consumer Creditworthiness (Mar. 2014),
available at http://www.nclc.org/issues/big-data.html; Leadership
Conference on Civil and Human Rights, ``Civil Rights Principles for
the Era of Big Data,'' February 27, 2014, available at http://www.civilrights.org/press/2014/civil-rights-principles-big-data.html.
\8\ State policymakers and law enforcement officials have also
looked into the potential risks and opportunities of alternative
data, particularly on data privacy issues. For example, in March
2015 the National Association of Attorneys General held a meeting to
discuss ``Big Data: Challenges and Opportunities,'' available at
http://www.naag.org/naag/media/naag-news/untitled-resource1.php. In
addition, the Massachusetts Attorney General hosted a March 2016
forum on data privacy in partnership with the MIT Computer Science
and Artificial Intelligence Lab.
\9\ FTC, Big Data: A Tool for Inclusion or Exclusion? (Jan.
2016), available at https://www.ftc.gov/system/files/documents/reports/big-data-tool-inclusion-or-exclusion-understanding-issues/160106big-data-rpt.pdf.
\10\ Id. at 1.
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Similarly, the Department of the Treasury's May 2016 report on
marketplace lending referenced the use

[[Page 11186]]

of alternative data in underwriting by marketplace lenders as an area
of both promise and risk: ``While data-driven algorithms may expedite
credit assessments and reduce costs, they also carry the risk of
disparate impact in credit outcomes and the potential for fair lending
violations.'' \11\
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\11\ U.S. Treasury, Opportunities and Challenges in Online
Marketplace Lending (May 2016), available at https://www.treasury.gov/connect/blog/Documents/Opportunities_and_Challenges_in_Online_Marketplace_Lending_white_paper.pdf.
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The Obama Administration completed two reports on big data, each
referencing both the promises and risks posed by alternative data in
the credit process.\12\ The latter report notes, among other things,
the importance of mitigating ``algorithmic discrimination,'' designing
the best algorithmic systems, and algorithmic auditing and testing.
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\12\ Executive Office of the President, Big Data: A Report on
Algorithmic Systems, Opportunity, and Civil Rights (May 2016),
available at https://www.whitehouse.gov/sites/default/files/microsites/ostp/2016_0504_data_discrimination.pdf; Executive Office
of the President, Big Data: Seizing Opportunities, Preserving Values
(May 2014), available at https://www.whitehouse.gov/sites/default/files/docs/big_data_privacy_report_may_1_2014.pdf.
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Finally, the Office of the Comptroller of the Currency (OCC), the
Federal Reserve Board of Governors (FRB), and the Federal Deposit
Insurance Corporation (FDIC) recently issued joint guidance \13\
referencing alternative data. The guidance identifies that banks' use
of ``alternative credit histories'' as a means ``to evaluate low- or
moderate-income individuals who lack sufficient conventional credit
histories and who would be denied credit based on the institution's
traditional underwriting standards'' could be considered an
``innovative and flexible practice . . . to address the credit needs of
low- or moderate-income individuals or geographies'' that examiners
would consider in evaluating banks' lending practices under the
Community Reinvestment Act (CRA). The guidance lists a prospective
borrower's rental and utility payments as examples of alternative
credit history.
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\13\ OCC, FRB, and FDIC, Community Reinvestment Act; Interagency
Questions and Answers Regarding Community Reinvestment; Guidance, 81
FR 48506 (July 25, 2016), available at https://www.thefederalregister.org/fdsys/pkg/FR-2016-07-25/pdf/2016-16693.pdf.
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These agencies' attention to the use of alternative data and
modeling techniques in the credit process reflects the growing
importance of these methods and approaches in the marketplace. As a
Federal agency designated by Congress to oversee compliance with the
various consumer financial protection statutes and regulations as they
apply to both banks and non-banks, and with its additional desire to
foster consumer-friendly innovation in the marketplace, the Bureau is
especially interested in increasing its understanding of the consumer
benefits and risks that are likely to accompany these developments and
how they relate to established consumer protections. Through this RFI,
the Bureau seeks to build on the foundation of existing research by
other Federal agencies and develop a deeper understanding of these
potential benefits and risks. The Bureau seeks to encourage responsible
and consumer-friendly uses of alternative data and modeling techniques
that leverage such benefits while providing a clearer path whereby
market participants can mitigate risks to consumers.

Potential Consumer Benefits

Alternative data and modeling techniques have the potential to
benefit consumers in several ways listed below. These benefits, as well
as others not identified here, could accrue differently in different
product markets--what helps consumers in the credit card marketplace
may not help consumers in the mortgage marketplace--or could provide
different levels of benefits to different consumer segments--what helps
consumers with no credit records may not help consumers with long
traditional credit histories.
Greater credit access: The Bureau estimates that
approximately 45 million Americans lack access to mainstream credit
because they have no credit history or because their credit history is
insufficient or stale. The use of alternative data or modeling
techniques could increase access to credit for that population by
providing more information about them and enabling them to be reliably
scored. For example, some consumers might not have traditional loan
repayment history but might pay their mobile phone bills on a regular
basis, a pattern that might be sufficient to reassure some lenders that
they are viable credit risks. Of course, only some portion of that 45
million might be reliably scorable using alternative data and modeling
techniques, and some of those scores might not qualify consumers for
mainstream credit.
Enhanced creditworthiness predictions: Alternative data
and modeling techniques could allow lenders to better assess the
creditworthiness of consumers who are already scored. For example, a
lender might not currently lend below a credit score of 620, but might
be willing to do so if, by adding some new data source, it could
distinguish those sub-620 consumers who present greater or lesser risks
of default. It is important to note that, to the extent alternative
data or modeling techniques could help a creditor identify consumers
who are more and less likely to default than their current credit score
suggests, alternative data could in fact decrease or increase a given
consumer's likelihood of receiving credit, or could raise or lower the
price that any individual is offered for that credit. Though this could
be seen as a detriment to consumers who are less likely to receive
credit (or whose prices increase), it could also be seen as an
improvement in risk assessment, which may provide greater certainty and
allow a lender to increase credit availability for those who qualify.
Indeed, in the longer term consumers whose credit scores understate
their true risk may be better served if they do not obtain additional
credit that they cannot repay.
More timely information: The credit process could be
improved by relying on more timely information about the consumer being
assessed. While all risk assessments use data from the present or past
to predict outcomes in the future (e.g., likelihood of default),
traditional data often lags actual events. For example, the opening of
a new credit account might take months to show up on a consumer's
credit report and in some cases it may not show up at all. Alternative
data could provide more timely indicators, such as real-time access to
a consumer's outstanding credit card balance. It could also help
lenders recognize whether a particular consumer's finances are trending
in a particular direction, such as through a job status change
appearing on social media. Such information could help to distinguish
those consumers whose low scores are a function of prior financial
problems that they have surmounted from those consumers whose financial
challenges have just begun and who may pose a greater risk than the
score indicates. Alternative modeling techniques might also generate
more timely feedback to the extent they dynamically change as new data
are ingested, though such dynamism could also carry certain risks.
Lower costs: The use of alternative data and modeling
techniques may have the potential to lower lenders' costs--these cost
savings might, in turn, be passed along to consumers in the form of
lower prices or in lenders' ability to make smaller loans economically.
For example, a lender might currently verify employment and income by
calling the consumer's employer or manually reviewing tax returns. If,
instead, the lender could automate such tasks by

[[Page 11187]]

processing data associated with the individual's employer, tax returns,
or other methods, its processing costs might significantly decline.
Better service and convenience: Alternative data and
modeling techniques might also be able to drive operational
improvements that enable better customer service outcomes for consumers
or greater convenience. For example, to the extent more tasks can be
automated, it might speed up application processes or reduce any
discretionary judgments that may sometimes lead to discrimination.
Through this RFI, the Bureau seeks to understand how consumers
might benefit from the use of alternative data and modeling techniques
(including in the ways identified above), the degree to which those
benefits impact different consumer segments or products, and any
specific empirical evidence relevant to the likelihood and extent of
those benefits.

Potential Consumer Risks

Use of alternative data and modeling techniques also carries
several potential risks. The Bureau lists some such risks below not to
dissuade the use of alternative data and modeling techniques but rather
to highlight some of the challenges with such use, to encourage
responsible use that takes consideration of and manages these risks,
and to invite commenters to discuss their views about how these and
other risks could be mitigated. As with the consumer benefits, this
list of consumer risks may not encompass all of the perceived or
potential consumer risks, and some risks may apply differently to
different consumer or product segments.
Privacy: Some types of alternative data could raise
privacy concerns because the data are of a sensitive nature and
consumers may not know the data were collected and shared nor expect or
be aware it will be used in decisions in the credit process.
Data quality issues: Some types of alternative data could
raise accuracy concerns because the data are inconsistent, incomplete,
or otherwise inaccurate. Though traditional data raises accuracy
concerns,\14\ it could be that certain types of alternative data have
greater rates of error due to their nature or the fact that the quality
standards for their original purpose are lesser than those associated
with decisions in the credit process. Such concerns may arise in part
because such data have not historically been used in credit or other
eligibility decisions and, as a result, the sources of such data may
not have been subject to the type of accuracy and quality obligations
that would commonly be expected for data to be used in decisions in the
credit process.
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\14\ See FTC, Report to Congress Under Section 319 of the Fair
and Accurate Credit Transactions Act of 2003 (Jan. 2015), available
at https://www.ftc.gov/system/files/documents/reports/section-319-fair-accurate-credit-transactions-act-2003-sixth-interim-final-report-federal-trade/150121factareport.pdf (26% of consumers found
material errors on their credit reports, 13% experienced a change in
their credit score as a result of modifying their reports, and 5%
experienced a significant change that changed their risk tier).
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Lost transparency, control, and ability to correct: Some
sources of alternative data may not permit consumers to access or view
data that is being used in decisions in the credit process, or to
correct any inaccuracies in that data. In some cases, consumers might
not be able to determine the sources of the data. These issues are
compounded if creditors are not transparent about the type of data they
are using and how those data figure into decisions in the credit
process. Certain alternative modeling techniques could compound the
transparency problem if they do not permit easy interpretation of how
various data inputs impact a model's result.
Harder to change credit standing through behavior:
Traditional credit factors are heavily influenced by the consumer's own
financial conduct, such as whether the person paid their loans on time
or how much credit the person has obtained and utilized. Alternative
data that cannot be changed by consumers or that are not specific to
the individual, but relate instead to peers or broader consumer
segments, do not enable consumers to improve their credit rating.
Harder to educate and explain: The more factors that are
integrated into a consumer's credit score or into decisions in the
credit process, or the more complex the modeling process in which the
data are used, the harder it may be to explain to a consumer what
factors led to a particular decision. This may be true for lenders, who
are required to provide adverse action notices to consumers in certain
circumstances, as well as for financial educators, who wish to improve
consumers' understanding of the factors that impact their credit
standing. These complexities make it more difficult for consumers to
exercise control in their financial lives, such as by learning how to
improve their credit rating.
Unintended or undesirable side effects: The use of
alternative data and modeling techniques could penalize or reward
certain groups or behaviors in ways that are difficult to predict. For
example, members of the military may frequently move and the perceived
lack of housing stability or continuity may give a false impression of
overall instability. Or negative inferences could potentially be drawn
about consumers who are not found in the alternative data source being
used by the lender. Foreseeable or otherwise, using alternative data
and modeling techniques could also cause potentially undesirable
results. For example, using some alternative data, especially data
about a trait or attribute that is beyond a consumer's control to
change, even if not illegal to use, could harden barriers to economic
and social mobility, particularly for those currently out of the
financial mainstream.
Discrimination: Alternative data and modeling techniques
could also result in illegal discrimination. For example, using
alternative data that involves categories protected under Federal,
State, or local fair lending laws may be overt discrimination. In
addition, certain alternative data variables might serve as proxies for
certain groups protected by anti-discrimination laws, such as a
variable indicating subscription to a magazine exclusively devoted to
coverage of women's health issues. And the use of other alternative
data might cause a disproportionately negative impact on a prohibited
basis that does not meet a legitimate business need or that could be
reasonably achieved by means that are less disparate in their impact.
Machine learning algorithms that sift through vast amounts of data
could unearth variables, or clusters of variables, that predict the
consumer's likelihood of default (or other relevant outcome) but are
also highly correlated with race, ethnicity, sex, or some other basis
protected by law. Such correlations are not per se discriminatory but
may raise fair lending risks. The use of alternative data and modeling
techniques could potentially lead to disparate impact on the part of a
well-intentioned lender as well as allow ill-meaning lenders to
intentionally discriminate and hide it behind a curtain of programming
code.
Other violations of law: The use of alternative data and
modeling techniques could potentially raise the risk of violating
consumer financial laws, such as the Equal Credit Opportunity Act
(ECOA) and Regulation B, the Fair Credit Reporting Act (FCRA) and
Regulation V, and the prohibitions on unfair, deceptive, or abusive
acts or practices (UDAAPs, collectively). The Bureau also recognizes
that there may be uncertainty about how certain aspects of these laws
apply to

[[Page 11188]]

alternative data and modeling techniques, and the Bureau seeks to
understand specifically where greater certainty would be helpful.
Through this RFI, the Bureau seeks to understand risks to consumers
from the use of alternative data and modeling techniques (including in
the ways identified above), the degree to which those risks impact
different product or consumer segments, and any specific empirical
evidence relevant to the likelihood and extent of those risks. The
Bureau also seeks to understand what steps market participants are
taking to manage risks and realize benefits. The Bureau intends to use
information gleaned from the questions below to help maximize the
benefits and minimize the risks from these developments.

Part D: Questions Related to Alternative Data and Modeling Techniques
Used in the Credit Process

This RFI is intended to cover past, current, and potential uses of
alternative data and modeling techniques. The Bureau is interested in
learning more about the specific types of alternative data and modeling
techniques utilized for various decisions in the credit process, as
well as the policies and procedures used to ensure the responsible use
of these alternative data and methods. In addition, the Bureau seeks to
learn how the use of alternative data and modeling techniques compares
and contrasts with the use of traditional data and modeling techniques
for those same decisions. Finally, of particular interest is a specific
and empirical understanding of the current and potential consumer
benefits and risks associated with the use of alternative data and
modeling techniques, including risks related to specific statutes and
regulations.
While the Bureau recognizes that some commenters may feel that
answering the questions below raises concerns about revealing
proprietary information, we encourage commenters to share as much
detail as possible in this public forum.\15\ We also welcome comments
from representatives, such as attorneys, consultants, or trade
associations, which need not identify their clients or members by name.
---------------------------------------------------------------------------

\15\ We do not seek, nor should commenters provide, actual
alternative data about consumers. Rather we seek information about
different types of alternative data.
---------------------------------------------------------------------------

The questions below are divided into four sections: (1) Alternative
Data; (2) Alternative Modeling Techniques; (3) Potential Benefits and
Risks to Consumers and Market Participants; and (4) Specific Statutes
and Regulations. Each question speaks generally about all decisions in
the credit process, but answers can differentiate, as appropriate,
between uses in marketing, fraud detection and prevention,
underwriting, setting or changes in terms (including pricing),
servicing, collections, or other relevant aspects of the credit
process. The questions are phrased in the present tense, but the Bureau
is equally interested in information about any past but discontinued
uses or in any potential future uses that commenters are considering or
are aware of. The Bureau welcomes any relevant empirical research or
studies on these topics.

Alternative Data

This section asks questions about the types, sources, and purposes
of alternative data. Comments referencing specific practices, firms, or
data are especially helpful.
1. What types of alternative data are used in decisions in the
credit process? Please describe not only the broad categories (e.g.,
cashflow data) but also the specific data element or variables used
(e.g., rent or telephone expense). The questions below refer back to
each type of alternative data listed in response to this question.
2. For each type of alternative data identified above:
a. Please describe the specific decisions in which this type of
alternative data is used, the specific purpose for using it, and the
product(s) and consumer segment(s) for which it is used. For example,
are certain data used to create a proprietary score for underwriting
mortgage loans for non-prime applicants while other data are used to
determine whether credit line increases or decreases are appropriate
for existing credit card users?
b. Please describe any goals, objectives, or challenges that the
use of this type of alternative data is designed to accomplish or
address. For example, a certain type of data might be used in order to
provide a more timely assessment of the consumer's current income while
another type of data might be used to more accurately predict the
stability of future income streams. Please describe the extent to which
use of alternative data has in fact advanced or addressed these goals,
objectives, or challenges.
c. Please describe the source of the data, being as specific as
possible, including if the data are provided by the consumer or
obtained from or through a third party. If obtained from a third party,
please indicate if that third party considers itself to be a consumer
reporting agency subject to the FCRA.
d. Please describe the format in which the data are received or
generated, being as specific as possible.
e. Please describe the breadth or coverage of the data. Are there
certain consumer segments for whom the data are unavailable?
f. Please describe whether the data include both positive and
negative observations. For example, do records of rental payments
include instances where consumers paid on time as well as when they
were late?
g. Please describe if the data are specific to the individual
consumer (e.g., the consumer's actual income) or attributed to the
consumer based upon a perceived peer group (e.g., average income of
consumers obtaining the same educational degree).
h. Please describe the quality of the data, in terms of apparent
errors, missing information, and consistency over time.
i. Please describe the methods or procedures used to assess the
coverage, quality, completeness, consistency, accuracy, and reliability
of the data, as well as who is responsible for overseeing those methods
or procedures.
j. Please describe the original purpose for which the data were
initially generated, assembled, or collected, and the standard for
coverage, quality, completeness, consistency, accuracy, and reliability
that the original data provider applied. Was the consumer able to see,
dispute, or correct the data at the time they were originally collected
or with the original collector of the data or with the subsequent user?
k. Could this particular type of alternative data feasibly be
furnished to one or more of the nationwide consumer reporting agencies?
What would be the investment(s) required to do so? What prevents such
furnishing today?
l. Please describe whether and how the data are used in identifying
and constructing target lists for marketing credit online, by mail, or
in person (i.e., firm offers of credit or invitations to apply).
m. Please describe whether and how the data are used to screen for
potential fraud prior to assessing creditworthiness.
3. For each type of alternative data identified above, please
describe the process for deciding whether to use that type of data,
including the criteria used for evaluating the data and its potential
use. If applicable, please describe the basis for determining the
relationship between the data and the outcome they are designed to
predict. If the

[[Page 11189]]

relationship is empirically derived, describe the type(s) of data used
to derive the relationship (e.g., internal loan performance data,
third-party reject inference data, etc.).
4. For each type of alternative data identified above, please
describe whether the data are used alongside other traditional or
alternative data. How much impact does the alternative data have on the
relevant decision? Is this data used only after a preliminary decision
based on the exclusive use of traditional data, for example, to re-
evaluate consumers who failed a model that used only traditional data?
Or is it used at the same time? Are there particular decisions or
particular products or consumer segments where firms rely exclusively
or predominantly on the use of alternative data?
5. Are there types of alternative data that have been evaluated but
are not being used in decisions in the credit process? If so, please
describe and explain the evaluation process and outcomes and the
reason(s) why the alternative data are not being used for the
particular credit-related decision.
6. For questions 1 through 5 above, please describe any differences
in your answers as they pertain to lending to businesses (especially
small businesses) rather than consumers.

Alternative Modeling Techniques

This section asks questions about alternative modeling techniques.
Comments referencing specific practices, firms, or data are especially
helpful.
What types of alternative modeling techniques are used in decisions
in the credit process? Please describe these modeling techniques in as
much detail as possible, including but not limited to:
a. A detailed explanation of the modeling technique, and how it
transforms inputs into outputs.
b. The product or consumer segment(s) it is used for.
c. The outcome(s) the modeling technique aims to predict.
d. The final output that the modeling technique generates, such as
a score within a defined range or a pass/fail decision, including any
identification of the main factors impacting the final output.
e. A detailed explanation of the specific data types used as
inputs, including both traditional and alternative data.
f. Whether the modeling technique is used concurrently with,
subsequent to, or in conjunction with other traditional or alternative
modeling techniques. How much impact does the alternative modeling
technique have on the decision it informs?
7. For each type of alternative modeling technique identified
above, please describe the model development and governance process
(e.g., initial development, training, testing, validation, beta,
broader use, redevelopment, etc.) in as much detail as possible,
including but not limited to:
a. Whether the process differs based upon the type of outcome being
predicted.
b. Whether the process differs for alternative versus traditional
modeling techniques.
c. Whether the process differs when alternative versus traditional
data are used.
d. Whether specific tests or validations are performed to assess
compliance with fair lending or other regulatory requirements. Are
these similar to or different from those used for traditional modeling
techniques?
e. A description of any judgmental, subjective, or discretionary
decisions made in the development phase. For example, for machine
learning techniques, what are decisions the developer must make in
supervising the training phase, or providing parameters or limits on
its operation?
f. A description of how, if at all, the process handles:
i. Sample selection for model testing/validation.
ii. Potential measurement error.
iii. Overfitting.
iv. Correlations with characteristics prohibited under fair lending
laws.
v. Direction of the relationship between features and outcomes
(e.g., monotonicity).
vi. Any other noteworthy considerations.
8. For questions 7 and 8 above, please describe any differences in
your answers as they pertain to lending to businesses (especially small
businesses) rather than consumers.

Potential Benefits and Risks to Consumers and Market Participants

This section asks questions about the potential benefits and risks
related to the use of alternative data and modeling techniques. The
Bureau encourages commenters to be as specific as possible when
describing the potential benefits and risks, including but not limited
to which consumer segments or groups (e.g., no traditional credit file,
different demographic groups), which products (e.g., auto loans, credit
cards), and which channels (e.g., online, storefront) are most
affected.
9. What does available evidence suggest about the potential
benefits for consumers of using alternative data present to:
a. Improved risk assessment so that consumers are more accurately
paired with appropriate credit products.
b. Increases in access to affordable credit.
c. Lower prices.
d. Quicker or more convenient decisioning process.
10. What does available evidence suggest about the potential
benefits for consumers of using alternative modeling techniques? Such
benefits could include, but are not limited to:
a. Improved risk assessment so that consumers are more accurately
paired with appropriate credit products.
b. Increases in access to credit.
c. Lower prices.
d. Quicker or more convenient decisioning process.
11. What does available evidence suggest about the potential
benefits for market participants of using alternative data? Such
benefits could include, but are not limited to:
a. An increased ability to accurately predict the likelihood of a
certain outcome (e.g., a 90 day delinquency within 24 months).
b. Risk assessment that is more reactive to real-time information.
c. Ability to assess and grant credit to more consumers.
d. Lower operational costs.
e. Quicker or more convenient decisioning process.
f. Competitive advantage, including the ability to compete with
traditional methods.
12. What does available evidence suggest about the potential
benefits for market participants of using alternative modeling
techniques? Such benefits could include, but are not limited to:
a. An increased ability to accurately predict the likelihood of a
certain outcome (e.g., a 90 day delinquency within 24 months).
b. Risk assessment that is more reactive to real-time information.
c. Ability to assess and grant credit to more consumers.
d. Lower operational costs.
e. Quicker or more convenient decisioning process.
f. Competitive advantage, including the ability to compete with
traditional methods.
13. What does available evidence suggest about the potential risks
for consumers of using alternative data? In addition, what steps are
being taken to mitigate these risks? Such risks could include, but are
not limited to:
a. Impacts on consumer privacy.
b. Decreased transparency about the use of one's data and about how
decisions in the credit process are made.

[[Page 11190]]

c. Decreased ability to dispute inaccurate information or correct
errors.
d. Decreased ability of consumers to improve their credit standing.
e. Decreased completeness, consistency, accuracy, or reliability of
data that affects decisions in the credit process.
f. Illegal discrimination.
g. The hardening of barriers to social and economic mobility.
h. Decreased access to affordable credit.
i. Decreased ability to inform and educate consumers about the
factors affecting their credit standing.
14. What does available evidence suggest about the potential risks
for consumers of using alternative modeling techniques? In addition,
what steps are being taken to mitigate these risks? Such risks could
include, but are not limited to:
a. Decreased transparency about the use of one's data and about how
decisions in the credit process are made.
b. Decreased ability to dispute inaccurate information or correct
errors.
c. Decreased ability of consumers to improve their credit standing.
d. Illegal discrimination.
e. Decreased ability to inform and educate consumers about the
factors affecting their credit standing.
15. What does available evidence suggest about the potential risks
for market participants of using alternative data? In addition, what
specific steps are being taken to mitigate these risks? Such risks
could include, but are not limited to:
a. Decreased transparency about how decisions in the credit process
are made.
b. Lack of historical performance data related to certain
alternative data.
c. Decreased completeness, consistency, accuracy, or reliability of
data.
d. Decreased ability to inform and educate consumers about the
factors affecting their credit standing.
e. Decreased consumer trust or acceptance of lender decisions.
16. What does available evidence suggest about the potential risks
for market participants of using alternative modeling techniques? In
addition, what specific steps are being taken to mitigate these risks?
Such risks could include, but are not limited to:
a. Decreased transparency about how decisions in the credit process
are made.
b. Lack of historical performance data related to certain modeling
techniques.
c. Decreased ability to inform and educate consumers about the
factors affecting their credit standing.
d. Decreased consumer trust or acceptance of lender decisions.
17. For questions 10 through 17 above, please describe any
differences in your answers as they pertain to lending to businesses
(especially small businesses) rather than consumers.

Specific Statutes and Regulations

This section asks questions about specific statutes and regulations
as they pertain to alternative data and modeling techniques. Nothing
below should be interpreted as a legal conclusion or interpretation by
the Bureau. While the questions below are focused on the activities of
market participants, the Bureau is equally interested in information
from researchers, consultants, and other third parties about the issues
raised below. The Bureau also recognizes that market participants may
be reluctant to comment publicly on potential legal uncertainties and
invite such parties to submit comments through anonymized channels such
as law firms, trade associations, and the like.
18. The ECOA and Regulation B prohibit discrimination on the basis
of race, color, religion, national origin, sex, marital status, age,
the fact that all or part of the applicant's income derives from any
public assistance program, or the good faith exercise of any right
under the Consumer Credit Protection Act. Evidence of disparate
treatment and evidence of disparate impact can be used to show
discrimination under ECOA and Regulation B.
a. Are there specific challenges or uncertainties that market
participants face in complying with ECOA and Regulation B with respect
to the use of alternative data or modeling techniques?
b. In the absence of data on applicants' ethnicity, race, sex, or
other prohibited basis group membership, how prevalent is the practice
of proxying for those characteristics in order to test for potential
fair lending risks in the use of alternative data or modeling
techniques?
c. How, if at all, are market participants using demographically
conscious model development techniques to ensure that models or
modeling techniques do not result in illegal discrimination?
d. For respondents (such as market participants or consultants,
attorneys, or other professionals who advise market participants) that
evaluate models for potential fair lending risk, please answer the
following questions. For each activity described in your answers,
please specify the point(s) in time (e.g., model development,
validation, implementation, or use) at which the activity is conducted;
the function(s) within the company responsible for conducting the
activity; the type(s) of models reviewed (e.g., underwriting, pricing,
fraud, marketing); how those models are prioritized for review; the
level (e.g., attribute, model, or decisioning process) at which the
activity is conducted; and which prohibited bases (e.g., age, sex,
race, ethnicity) are evaluated.
i. In general, what methods do market participants use to evaluate
alternative data and modeling techniques for fair lending risk?
ii. What steps, if any, do market participants take to determine
whether alternative data may be serving as a proxy for a prohibited
basis? What thresholds, standards, or baselines are used to make this
determination?
iii. What steps, if any, do market participants take to determine
whether use of alternative data has a disproportionately negative
impact on a prohibited basis? What thresholds, standards, or baselines
are used to make this determination? To what extent, if any, do market
participants use traditional data (or scores generated therefrom) as a
baseline for making this determination?
iv. What steps, if any, do market participants take to determine if
the use of alternative data meets a legitimate business need
notwithstanding any disproportionately negative impact that use may
have on a prohibited basis?
v. What steps, if any, do market participants take to ensure that a
legitimate business need met by the use of alternative data cannot
reasonably be achieved as well by means that are less disparate in
their impact?
vi. What other steps, besides those already discussed in response
to questions 19(d)(i)-(v) above, do market participants take to
evaluate or manage potential fair lending risk arising from the use of
alternative data or modeling techniques?
vii. When a lender identifies disparities affecting a prohibited
basis group or other fair lending risks that arise from the use of a
particular variable or model, what steps does the lender take as a
result? To what extent do these steps mitigate that risk?
viii. How do the activities described in response to questions
19(d)(i)-(v) compare with the activities conducted when using
traditional data or modeling techniques?
e. Many entities subject to the Bureau's supervisory or enforcement
jurisdiction have risk management programs in place pursuant to
guidance on model risk management issued by

[[Page 11191]]

prudential regulators.\16\ To what extent do market participants use
principles or processes discussed in that guidance in connection with
their management of fair lending risk?
---------------------------------------------------------------------------

\16\ See Federal Reserve Board SR Letter 11-7 (``Guidance on
Model Risk Management'') (April 4, 2011); Office of the Comptroller
of the Currency (OCC) Bulletin 1997-24 (``Credit Scoring Models'')
(May 20, 1997); OCC Bulletin 2000-16 (``Risk Modeling'') (May 30,
2000); OCC Bulletin 2011-12 (``Sound Practices for Model Risk
Management'') (April 4, 2011); Federal Deposit Insurance Corporation
(FDIC) Supervisory Insights (``Model Governance'') (last updated
December 5, 2005); FDIC Supervisory Insights (``Fair Lending
Implications of Credit Scoring Systems'') (last updated April 11,
2013).
---------------------------------------------------------------------------

f. Are market participants using alternative data or modeling
techniques as a ``second look'' for those who do not meet initial
eligibility requirements based on traditional data or modeling
techniques? If so, what issues and challenges, if any, arise in that
context? Have data that were first used in ``second looks'' eventually
become included in initial screening processes?
g. When using alternative data or modeling techniques, or using
multiple models, are there challenges in determining and disclosing to
applicants the principal reasons for taking adverse action or
describing the reasons for taking adverse action in a manner that
relates to and accurately describes the factors actually considered or
scored?
19. The FCRA and Regulation V regulate the collection,
dissemination, and use of consumer information, including consumer
credit information.
a. Are there specific challenges or uncertainties that market
participants face in complying with the FCRA with respect to the use of
alternative data or modeling techniques?
b. What challenges do companies generating, selling, and brokering
alternative data face in determining whether they are a consumer
reporting agency subject to the FCRA?
c. What challenges do consumer reporting agencies assembling or
evaluating alternative data face in implementing accuracy and dispute
procedures and disclosing file information to consumers?
d. What challenges do lenders face when they obtain alternative
data? Is it typically clear whether the data provider is a consumer
reporting agency subject to the FCRA?
e. How, if at all, do market participants treat alternative data
differently when they receive it from data providers or other sources
that do not appear to be subject to the FCRA?
f. When using alternative data or modeling techniques, or using
multiple credit scores, are there challenges in providing adverse
action notices or risk-based pricing notices? For example, when using
alternative modeling techniques, are there challenges in determining
the key factors that adversely affected the consumer's score? Are there
challenges in providing the source of the information? Do you have
information showing whether consumers understand the information on
these notices or take appropriate follow-up actions?
g. When using alternative data or modeling techniques, are there
challenges in disclosing, pursuant to Section 615(b) of the FCRA, the
nature of the information used in credit-related decisions when such
information comes from a third party that is not a consumer reporting
agency?
h. The FCRA permits consumer reports to be obtained for some non-
credit decisions, such as employment and tenant screening. What
potential impacts could alternative data and modeling techniques have
on these non-credit decisions?
20. The Dodd-Frank Act prohibits unfair, deceptive, or abusive acts
or practices in connection with consumer financial products or
services. Section 5 of the FTC Act similarly prohibits unfair or
deceptive acts or practices in connection with a broader set of
transactions.
a. Are there specific challenges or uncertainties that market
participants face in complying with the prohibitions on UDAAPs with
respect to alternative data or modeling techniques?
b. What steps, if any, do users of alternative data or modeling
techniques take to avoid engaging in UDAAPs?
c. What steps, if any, can the Bureau take to help minimize the
risk of UDAAPs from the use of alternative data and modeling
techniques?

Dated: February 14, 2017.
Richard Cordray,
Director, Bureau of Consumer Financial Protection.
[FR Doc. 2017-03361 Filed 2-17-17; 8:45 am]
BILLING CODE 4810-AM-P

11184 Federal Register / Vol. 82, No. 33 / Tuesday, February 21, 2017 / Notices

number, address, telephone number, or lines, extensions of new offers of credit, they have not established a long enough
account number. or other decisions in the credit process, credit history on their own or in this
For the purposes of this RFI, we lenders typically evaluate consumers country. Some underserved consumers
define the following terms. None of using a standard set of information that instead resort to high-cost products that
these definitions should be construed as includes consumer-supplied data (such may not help them build credit history.
statutory or regulatory definitions or as income, assets and, if secured, any Several commentators have suggested
descriptions of statutory or regulatory collateral) and other traditional data that alternative data and modeling
coverage. supplied by one or more of the techniques could address this problem
• ‘‘Traditional data’’ refers to data nationwide consumer reporting and reach some of the millions of
assembled and managed in the core agencies. Many lenders base their consumers currently shut out of the
credit files of the nationwide consumer decisions, in whole or in part, on scores mainstream credit system and enable
reporting agencies, which includes using traditional data as inputs and others to obtain more favorable pricing
tradeline information (including certain generated from commercially-available, based on more refined assessments of
loan or credit limit information, debt third-party models such as one of the their risks.3 Discussions point to the
repayment history, and account status), many developed by FICO or wide array of other data sources beyond
and credit inquiries, as well as VantageScore Solutions. Other lenders traditional credit files that could be
information from public records relating may base their decisions, in whole or in used to assess the creditworthiness of
to civil judgments, tax liens, and part, on proprietary scoring algorithms borrowers, including so-called ‘‘big
bankruptcies. It also refers to data that use traditional data, and perhaps data.’’ 4 In addition, increased
customarily provided by consumers as scores from these third-party models, as computing power and the expanded use
part of applications for credit, such as well as consumer-supplied information, of machine learning to mine massive
income or length of time in residence. as inputs. In addition to using common datasets could potentially identify
• ‘‘Alternative data’’ refers to any data inputs, there is similar consistency in insights not otherwise discoverable
that are not ‘‘traditional.’’ We use the modeling techniques used to through traditional methods. The
‘‘alternative’’ in a descriptive rather generate these automated decision application of alternative data and
than normative sense and recognize engines. They have predominantly been modeling techniques might also
there may not be an easily definable line developed using multivariate regression improve decisions in the credit process
between traditional and alternative data. analysis to correlate past credit history by improving the predictiveness of
• ‘‘Traditional modeling techniques’’ and current credit usage attributes to credit-related models, by lowering the
refers to statistical and mathematical consumer credit outcomes to determine costs of sourcing and analyzing data, or
techniques, including models, whether, based on the performance of through other process improvements
algorithms, and their outputs, that are other previous consumers who had such as faster decisions.
traditionally used in automated credit similar attributes at the time credit was
processes, especially linear and logistic If these claimed benefits prove valid,
extended, it is likely that the consumer the use of alternative data and modeling
regression methods. being evaluated will default on or
• ‘‘Alternative modeling techniques’’ techniques could significantly reshape
become seriously delinquent on the loan the consumer (and business) credit
refers to all other modeling techniques within a certain period of time (often 1–
that are not ‘‘traditional,’’ including but market. Potentially millions of
2 years). These traditional data and consumers previously locked out of
not limited to decision trees, random modeling techniques have facilitated the
forests, artificial neural networks, k- mainstream credit could become eligible
standardization and automation of the for credit products that might help them
nearest neighbor, genetic programming, credit process, leading to efficiencies in
‘‘boosting’’ algorithms, etc. We use buy a car or a home. An increasing
the provision of credit over the past few ability for lenders to accurately assess
‘‘alternative’’ in a descriptive rather decades.
than normative sense and recognize that risk could reduce the price of credit for
Yet the use of traditional data and those who are shown to be good risks
there may not be an easily definable line modeling techniques has left some
between traditional and alternative (although it could increase the price of
important gaps in access to mainstream credit for those shown to be worse
modeling techniques. credit for certain consumer groups and
• ‘‘The credit process’’ refers to all segments. The Bureau estimates that 26
risks), and might even reduce the
the processes and decisions made by the overall average price of credit for those
million Americans are ‘‘credit who qualify for credit. The process of
creditor during the full lifecycle of the invisible,’’ meaning that they have no
credit product, including marketing, file with the major credit bureaus, while
pre-screening, fraud prevention, another 19 million are ‘‘unscorable’’
3 See, e.g., PERC, Give Credit Where Credit Is Due:

application procedures, underwriting, Increasing Access To Affordable Mainstream Credit
because their credit file is either too thin Using Alternative Data (Dec. 2006), available at
account management, credit or too stale to generate a reliable score http://www.perc.net/publications/give-credit-where-
authorization, the setting of pricing and from one of the major credit scoring credit-is-due/; CFSI, The Predictive Value of
terms, as well as the renewal, firms.2 Most of these 45 million
Alternative Credit Scores (Nov. 2007), available at
modification, or refinancing of existing http://www.cfsinnovation.com/Document-Library/
Americans are underserved by the The-Predictive-Value-of-Alternative-Credit-Scores;
credit, and the servicing and collection
mainstream credit system and they are 4 ‘‘Big data’’ is a distinct concept from alternative
of debts. data, though some alternative data may have the
disproportionately Black and Hispanic,
attributes generally ascribed to ‘‘big data.’’ In the
Part A: Traditional Automated Credit low-income, or young adults. Some FTC’s words, ‘‘A common framework for
Process and Its Alternatives populations, like those recently characterizing big data relies on the ‘three Vs,’ the
sradovich on DSK3GMQ082PROD with NOTICES

Most of today’s automated decisions widowed or divorced or recent volume, velocity, and variety of data, each of which
immigrants, have difficulty accessing is growing at a rapid rate as technological advances
in the credit process use traditional permit the analysis and use of this data in ways that
modeling techniques that rely upon the mainstream credit system because were not possible previously.’’ FTC, Big Data: A
traditional data elements as inputs. Tool for Inclusion or Exclusion? Understanding the
2 CFPB, Data Point: Credit Invisibles (May 2015), Issues (Jan. 2016), available at https://www.ftc.gov/
When lenders make decisions about available at http://files.consumerfinance.gov/f/ system/files/documents/reports/big-data-tool-
consumers relating to applications for 201505_cfpb_data-point-credit-invisibles.pdf inclusion-or-exclusion-understanding-issues/
credit, increases or reductions in credit (figures are from 2010 Census). 160106big-data-rpt.pdf.

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Federal Register / Vol. 82, No. 33 / Tuesday, February 21, 2017 / Notices 11189

relationship is empirically derived, modeling technique have on the consumers of using alternative data
describe the type(s) of data used to decision it informs? present to:
derive the relationship (e.g., internal 7. For each type of alternative a. Improved risk assessment so that
loan performance data, third-party reject modeling technique identified above, consumers are more accurately paired
inference data, etc.). please describe the model development with appropriate credit products.
4. For each type of alternative data and governance process (e.g., initial b. Increases in access to affordable
identified above, please describe development, training, testing, credit.
whether the data are used alongside validation, beta, broader use, c. Lower prices.
other traditional or alternative data. redevelopment, etc.) in as much detail d. Quicker or more convenient
How much impact does the alternative as possible, including but not limited to: decisioning process.
data have on the relevant decision? Is a. Whether the process differs based 10. What does available evidence
this data used only after a preliminary upon the type of outcome being suggest about the potential benefits for
decision based on the exclusive use of predicted. consumers of using alternative modeling
traditional data, for example, to re- b. Whether the process differs for techniques? Such benefits could
evaluate consumers who failed a model alternative versus traditional modeling include, but are not limited to:
techniques. a. Improved risk assessment so that
that used only traditional data? Or is it
c. Whether the process differs when consumers are more accurately paired
used at the same time? Are there
alternative versus traditional data are with appropriate credit products.
particular decisions or particular
used. b. Increases in access to credit.
products or consumer segments where c. Lower prices.
firms rely exclusively or predominantly d. Whether specific tests or
validations are performed to assess d. Quicker or more convenient
on the use of alternative data? decisioning process.
5. Are there types of alternative data compliance with fair lending or other
11. What does available evidence
that have been evaluated but are not regulatory requirements. Are these
suggest about the potential benefits for
being used in decisions in the credit similar to or different from those used
market participants of using alternative
process? If so, please describe and for traditional modeling techniques?
e. A description of any judgmental, data? Such benefits could include, but
explain the evaluation process and are not limited to:
outcomes and the reason(s) why the subjective, or discretionary decisions
a. An increased ability to accurately
alternative data are not being used for made in the development phase. For
predict the likelihood of a certain
the particular credit-related decision. example, for machine learning
outcome (e.g., a 90 day delinquency
6. For questions 1 through 5 above, techniques, what are decisions the
within 24 months).
please describe any differences in your developer must make in supervising the
b. Risk assessment that is more
answers as they pertain to lending to training phase, or providing parameters
reactive to real-time information.
businesses (especially small businesses) or limits on its operation? c. Ability to assess and grant credit to
rather than consumers. f. A description of how, if at all, the more consumers.
process handles: d. Lower operational costs.
Alternative Modeling Techniques i. Sample selection for model testing/ e. Quicker or more convenient
This section asks questions about validation. decisioning process.
alternative modeling techniques. ii. Potential measurement error. f. Competitive advantage, including
iii. Overfitting. the ability to compete with traditional
Comments referencing specific
iv. Correlations with characteristics methods.
practices, firms, or data are especially
prohibited under fair lending laws. 12. What does available evidence
helpful. v. Direction of the relationship
What types of alternative modeling suggest about the potential benefits for
between features and outcomes (e.g., market participants of using alternative
techniques are used in decisions in the monotonicity).
credit process? Please describe these modeling techniques? Such benefits
vi. Any other noteworthy could include, but are not limited to:
modeling techniques in as much detail considerations.
as possible, including but not limited to: a. An increased ability to accurately
8. For questions 7 and 8 above, please predict the likelihood of a certain
a. A detailed explanation of the describe any differences in your
modeling technique, and how it outcome (e.g., a 90 day delinquency
answers as they pertain to lending to within 24 months).
transforms inputs into outputs. businesses (especially small businesses) b. Risk assessment that is more
b. The product or consumer rather than consumers. reactive to real-time information.
segment(s) it is used for.
Potential Benefits and Risks to c. Ability to assess and grant credit to
c. The outcome(s) the modeling more consumers.
technique aims to predict. Consumers and Market Participants
d. Lower operational costs.
d. The final output that the modeling This section asks questions about the e. Quicker or more convenient
technique generates, such as a score potential benefits and risks related to decisioning process.
within a defined range or a pass/fail the use of alternative data and modeling f. Competitive advantage, including
decision, including any identification of techniques. The Bureau encourages the ability to compete with traditional
the main factors impacting the final commenters to be as specific as possible methods.
output. when describing the potential benefits 13. What does available evidence
e. A detailed explanation of the and risks, including but not limited to suggest about the potential risks for
specific data types used as inputs,
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which consumer segments or groups consumers of using alternative data? In
including both traditional and (e.g., no traditional credit file, different addition, what steps are being taken to
alternative data. demographic groups), which products mitigate these risks? Such risks could
f. Whether the modeling technique is (e.g., auto loans, credit cards), and include, but are not limited to:
used concurrently with, subsequent to, which channels (e.g., online, storefront) a. Impacts on consumer privacy.
or in conjunction with other traditional are most affected. b. Decreased transparency about the
or alternative modeling techniques. 9. What does available evidence use of one’s data and about how
How much impact does the alternative suggest about the potential benefits for decisions in the credit process are made.

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Federal Register / Vol. 82, No. 33 / Tuesday, February 21, 2017 / Notices 11191

prudential regulators.16 To what extent credit scores, are there challenges in SUMMARY: The Advisory Committee on
do market participants use principles or providing adverse action notices or risk- Arlington National Cemetery is an
processes discussed in that guidance in based pricing notices? For example, independent Federal advisory
connection with their management of when using alternative modeling committee chartered to provide the
fair lending risk? techniques, are there challenges in Secretary of Defense, through the
f. Are market participants using determining the key factors that Secretary of the Army, independent
alternative data or modeling techniques adversely affected the consumer’s score? advice and recommendations on
as a ‘‘second look’’ for those who do not Are there challenges in providing the Arlington National Cemetery, including,
meet initial eligibility requirements source of the information? Do you have but not limited to cemetery
based on traditional data or modeling information showing whether administration, the erection of
techniques? If so, what issues and consumers understand the information memorials at the cemetery, and master
challenges, if any, arise in that context? on these notices or take appropriate planning for the cemetery. The
Have data that were first used in follow-up actions? Secretary of the Army may act on the
‘‘second looks’’ eventually become g. When using alternative data or Committee’s advice and
included in initial screening processes? modeling techniques, are there recommendations. The Committee is
g. When using alternative data or challenges in disclosing, pursuant to comprised of no more than nine (9)
modeling techniques, or using multiple Section 615(b) of the FCRA, the nature members. Subject to the approval of the
models, are there challenges in of the information used in credit-related Secretary of Defense, the Secretary of
determining and disclosing to decisions when such information comes the Army appoints no more than seven
applicants the principal reasons for from a third party that is not a consumer (7) of these members. The purpose of
taking adverse action or describing the reporting agency? this notice is to solicit nominations from
reasons for taking adverse action in a h. The FCRA permits consumer a wide range of highly qualified persons
manner that relates to and accurately reports to be obtained for some non- to be considered for appointment to the
describes the factors actually considered credit decisions, such as employment Committee. Nominees may be appointed
or scored? and tenant screening. What potential as members of the Committee and its
19. The FCRA and Regulation V impacts could alternative data and sub-committees for terms of service
regulate the collection, dissemination, modeling techniques have on these non- ranging from one to four years. This
and use of consumer information, credit decisions? notice solicits nominations to fill
including consumer credit information. 20. The Dodd-Frank Act prohibits Committee membership vacancies that
a. Are there specific challenges or unfair, deceptive, or abusive acts or may occur through July 31, 2017.
uncertainties that market participants practices in connection with consumer Nominees must be preeminent
face in complying with the FCRA with financial products or services. Section 5 authorities in their respective fields of
respect to the use of alternative data or of the FTC Act similarly prohibits unfair interest or expertise.
modeling techniques? or deceptive acts or practices in DATES: All nominations must be
b. What challenges do companies connection with a broader set of received (see ADDRESSES) no later than
generating, selling, and brokering transactions. May 1, 2017.
alternative data face in determining a. Are there specific challenges or ADDRESSES: Interested persons may
whether they are a consumer reporting uncertainties that market participants submit a resume for consideration by
agency subject to the FCRA? face in complying with the prohibitions the Department of the Army to the
c. What challenges do consumer on UDAAPs with respect to alternative Committee’s Designated Federal Officer
reporting agencies assembling or data or modeling techniques? at the following address: Advisory
evaluating alternative data face in b. What steps, if any, do users of Committee on Arlington National
implementing accuracy and dispute alternative data or modeling techniques Cemetery, ATTN: Designated Federal
procedures and disclosing file take to avoid engaging in UDAAPs? Officer (DFO) (Ms. Yates), Arlington
information to consumers? c. What steps, if any, can the Bureau National Cemetery, Arlington, VA
d. What challenges do lenders face take to help minimize the risk of 22211.
when they obtain alternative data? Is it UDAAPs from the use of alternative data FOR FURTHER INFORMATION CONTACT: Ms.
typically clear whether the data and modeling techniques? Renea C. Yates, Designated Federal
provider is a consumer reporting agency Officer, by email at renea.c.yates.civ@
Dated: February 14, 2017.
subject to the FCRA? mail.mil or by telephone 877–907–8585.
e. How, if at all, do market Richard Cordray,
Director, Bureau of Consumer Financial SUPPLEMENTARY INFORMATION: The
participants treat alternative data
differently when they receive it from Protection. Advisory Committee on Arlington
data providers or other sources that do [FR Doc. 2017–03361 Filed 2–17–17; 8:45 am] National Cemetery was established
not appear to be subject to the FCRA? BILLING CODE 4810–AM–P
pursuant to Title 10, United States Code
f. When using alternative data or Section 4723. The selection, service and
modeling techniques, or using multiple appointment of members of the
Committee are publicized in the
16 See Federal Reserve Board SR Letter 11–7 DEPARTMENT OF DEFENSE Committee Charter, available on the
(‘‘Guidance on Model Risk Management’’) (April 4, Arlington National Cemetery Web site
2011); Office of the Comptroller of the Currency Department of the Army http://www.arlingtoncemetery.mil/
(OCC) Bulletin 1997–24 (‘‘Credit Scoring Models’’)
sradovich on DSK3GMQ082PROD with NOTICES

About/Advisory-Committee-on-
(May 20, 1997); OCC Bulletin 2000–16 (‘‘Risk Advisory Committee on Arlington Arlington-National-Cemetery/Charter.
Modeling’’) (May 30, 2000); OCC Bulletin 2011–12 National Cemetery; Request for
(‘‘Sound Practices for Model Risk Management’’) The substance of the provisions of the
(April 4, 2011); Federal Deposit Insurance Nominations Charter is as follows:
Corporation (FDIC) Supervisory Insights (‘‘Model a. Selection. The Committee Charter
Governance’’) (last updated December 5, 2005); AGENCY:Department of the Army, DoD.
provides that the Committee shall be
FDIC Supervisory Insights (‘‘Fair Lending Notice; Request for
ACTION:
Implications of Credit Scoring Systems’’) (last comprised of no more than nine
Nominations.
updated April 11, 2013). members, all of whom are preeminent

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Document Created: 2018-02-01 15:04:21
Document Modified: 2018-02-01 15:04:21

Category		Regulatory Information
Collection		Federal Register
sudoc Class		AE 2.7: GS 4.107: AE 2.106:
Publisher		Office of the Federal Register, National Archives and Records Administration
Section		Notices
Action		Notice and request for information.
Dates		Comments must be received on or before May 19, 2017.
Contact		For general inquiries, submission process questions or any additional information, please contact Monica Jackson, Office of the Executive Secretary, at 202-435-7275.
FR Citation		82 FR 11183