Request for Information Regarding Use of Alternative Data and Modeling Techniques in the Credit Process, 11183-11191 [2017-03361]

Agencies

• BUREAU OF CONSUMER FINANCIAL PROTECTION

[Federal Register Volume 82, Number 33 (Tuesday, February 21, 2017)]
[Notices]
[Pages 11183-11191]
From the Federal Register Online via the Government Publishing Office [www.gpo.gov]
[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 https://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 https://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 
https://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 https://www.perc.net/publications/give-credit-where-credit-is-due/; CFSI, The Predictive Value of Alternative 
Credit Scores (Nov. 2007), available at https://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 https://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 https://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 https://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 
https://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.gpo.gov/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