Request for Information and Comment on Financial Institutions' Use of Artificial Intelligence, Including Machine Learning, 16837-16842 [2021-06607]
Download as PDF
<![CDATA[
Federal Register / Vol. 86, No. 60 / Wednesday, March 31, 2021 / Notices
[FR Doc. 2021–06577 Filed 3–30–21; 8:45 am]
BILLING CODE 4810–70–P
DEPARTMENT OF THE TREASURY
Office of the Comptroller of the
Currency
[Docket ID OCC–2020–0049]
BOARD OF GOVERNORS OF THE
FEDERAL RESERVE SYSTEM
[Docket No. OP–1743]
FEDERAL DEPOSIT INSURANCE
CORPORATION
RIN 3064–ZA24
BUREAU OF CONSUMER FINANCIAL
PROTECTION
[Docket No. CFPB–2021–0004]
NATIONAL CREDIT UNION
ADMINISTRATION
[Docket No. NCUA–2021–0023]
Request for Information and Comment
on Financial Institutions’ Use of
Artificial Intelligence, Including
Machine Learning
Board of Governors of the
Federal Reserve System, Bureau of
Consumer Financial Protection, Federal
Deposit Insurance Corporation, National
Credit Union Administration, and Office
of the Comptroller of the Currency
(agencies).
ACTION: Request for information and
comment.
AGENCY:
The agencies are gathering
information and comments on financial
institutions’ use of artificial intelligence
(AI), including machine learning (ML).
The purpose of this request for
information (RFI) is to understand
respondents’ views on the use of AI by
financial institutions in their provision
of services to customers and for other
business or operational purposes;
appropriate governance, risk
management, and controls over AI; and
any challenges in developing, adopting,
and managing AI. The RFI also solicits
respondents’ views on the use of AI in
financial services to assist in
determining whether any clarifications
from the agencies would be helpful for
financial institutions’ use of AI in a safe
and sound manner and in compliance
with applicable laws and regulations,
including those related to consumer
protection.
DATES: Comments must be received by
June 1, 2021.
ADDRESSES: Interested parties are
encouraged to submit written comments
jbell on DSKJLSW7X2PROD with NOTICES
SUMMARY:
VerDate Sep<11>2014
18:54 Mar 30, 2021
Jkt 253001
jointly to all of the agencies.
Commenters are encouraged to use the
title ‘‘Request for Information and
Comment on Financial Institutions’ Use
of Artificial Intelligence, including
Machine Learning’’ to facilitate the
organization and distribution of
comments among the agencies.
Commenters are also encouraged to
identify the number of the specific
question for comment to which they are
responding. Please send comments by
one method only and should be directed
to:
OCC: Commenters are encouraged to
submit comments through the Federal
eRulemaking Portal. Please use the title
‘‘Request for Information on Financial
Institutions’ Use of Artificial
Intelligence, including Machine
Learning; Request for Comment’’ to
facilitate the organization and
distribution of the comments. You may
submit comments by any of the
following methods:
• Federal eRulemaking Portal—
Regulations.gov: Go to https://
regulations.gov/. Enter ‘‘Docket ID OCC–
2020–0049’’ in the Search Box and click
‘‘Search.’’ Public comments can be
submitted via the ‘‘Comment’’ box
below the displayed document
information or by clicking on the
document title and then clicking the
‘‘Comment’’ box on the top-left side of
the screen. For help with submitting
effective comments please click on
‘‘Commenter’s Checklist.’’ For
assistance with the Regulations.gov site,
please call (877) 378–5457 (toll free) or
(703) 454–9859 Monday-Friday, 9am5pm ET or email regulations@
erulemakinghelpdesk.com.
• Mail: Chief Counsel’s Office,
Attention: Comment Processing, Office
of the Comptroller of the Currency, 400
7th Street SW, Suite 3E–218,
Washington, DC 20219.
• Hand Delivery/Courier: 400 7th
Street SW, Suite 3E–218, Washington,
DC 20219. Instructions: You must
include ‘‘OCC’’ as the agency name and
‘‘Docket ID OCC–2020–0049’’ in your
comment. In general, the OCC will enter
all comments received into the docket
and publish the comments on the
Regulations.gov website without
change, including any business or
personal information provided such as
name and address information, email
addresses, or phone numbers.
Comments received, including
attachments and other supporting
materials, are part of the public record
and subject to public disclosure. Do not
include any information in your
comment or supporting materials that
you consider confidential or
inappropriate for public disclosure.
PO 00000
Frm 00137
Fmt 4703
Sfmt 4703
16837
You may review comments and other
related materials that pertain to this
action by the following method:
• Viewing Comments Electronically—
Regulations.gov: Go to https://
regulations.gov/. Enter ‘‘Docket ID OCC–
2020–0049’’ in the Search Box and click
‘‘Search.’’ Click on the ‘‘Documents’’ tab
and then the document’s title. After
clicking the document’s title, click the
‘‘Browse Comments’’ tab. Comments can
be viewed and filtered by clicking on
the ‘‘Sort By’’ drop-down on the right
side of the screen or the ‘‘Refine
Results’’ options on the left side of the
screen. Supporting materials can be
viewed by clicking on the ‘‘Documents’’
tab and filtered by clicking on the ‘‘Sort
By’’ drop-down on the right side of the
screen or the ‘‘Refine Documents
Results’’ options on the left side of the
screen.’’ For assistance with the
Regulations.gov site, please call (877)
378–5457 (toll free) or (703) 454–9859
Monday–Friday, 9am–5pm ET or email
regulations@erulemakinghelpdesk.com.
The docket may be viewed after the
close of the comment period in the same
manner as during the comment period.
Board: You may submit comments,
identified by Docket No. OP–1743, by
any of the following methods:
• Agency website: https://
www.federalreserve.gov. Follow the
instructions for submitting comments at
https://www.federalreserve.gov/
generalinfo/foia/ProposedRegs.cfm.
• Email: regs.comments@
federalreserve.gov. Include docket
number in the subject line of the
message.
• Fax: (202) 452–3819 or (202) 452–
3102.
Mail: Ann E. Misback, Secretary,
Board of Governors of the Federal
Reserve System, 20th Street and
Constitution Avenue NW, Washington,
DC 20551. All public comments will be
made available on the Board’s website at
https://www.federalreserve.gov/
generalinfo/foia/ProposedRegs.cfm as
submitted, unless modified for technical
reasons or to remove personally
identifiable information or other
confidential information at the
commenter’s request. Accordingly, your
comments will not be edited to remove
any identifying or contact information.
Public comments may also be viewed in
paper in Room 146, 1709 New York
Avenue NW, Washington, DC 20006,
between 9:00 a.m. and 5:00 p.m. on
weekdays.
FDIC
• Agency website: https://
www.fdic.gov/regulations/laws/federal/.
Follow the instructions for submitting
comments on the agency’s website.
E:\FR\FM\31MRN1.SGM
31MRN1
16838
Federal Register / Vol. 86, No. 60 / Wednesday, March 31, 2021 / Notices
jbell on DSKJLSW7X2PROD with NOTICES
• Email: Comments@fdic.gov. Include
RIN 3064–ZA24 in the subject line of
the message.
• Mail: James P. Sheesley, Assistant
Executive Secretary, Attention:
Comments-RIN 3064–ZA24, Federal
Deposit Insurance Corporation, 550 17th
Street NW, Washington, DC 20429.
• Hand Delivery/Courier: Comments
may be hand-delivered to the guard
station at the rear of the 550 17th Street
NW building (located on F Street) on
business days between 7:00 a.m. and
5:00 p.m. Public Inspection: All
comments received will be posted
without change to https://www.fdic.gov/
regulations/laws/federal/—including
any personal information provided— for
public inspection. Paper copies of
public comments may be ordered from
the FDIC Public Information Center,
3501 North Fairfax Drive, Room E–1002,
Arlington, VA 22226 or by telephone at
(877) 275–3342 or (703) 562–2200.
Bureau of Consumer Financial
Protection (Bureau)
You may submit responsive
information and other comments,
identified by Docket No. CFPB–2021–
0004, by any of the following methods:
• Federal eRulemaking Portal: Go to
https://www.regulations.gov. Follow the
instructions for submitting comments.
• Email: 2021–RFI–AI@cfpb.gov.
Include Docket No. CFPB–2021–0004 in
the subject line of the message.
• Mail/Hand Delivery/Courier:
Comment Intake, Bureau of Consumer
Financial Protection, 1700 G Street NW,
Washington, DC 20552. Please note that
due to circumstances associated with
the COVID–19 pandemic, the CFPB
discourages the submission of
comments by mail, hand delivery, or
courier.
• Instructions: 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, and in light of
difficulties associated with mail and
hand deliveries during the COVID–19
pandemic, commenters are encouraged
to submit comments electronically.
In general, all comments received will
be posted without change to https://
www.regulations.gov. In addition, once
the Bureau’s headquarters reopens,
comments will be available for public
inspection and copying at 1700 G Street
NW, Washington, DC 20552, on official
business days between the hours of 10
a.m. and 5 p.m. Eastern Time. At that
time, you can make an appointment to
inspect the documents by calling 202–
435–7275.
VerDate Sep<11>2014
18:54 Mar 30, 2021
Jkt 253001
All submissions in response to this
RFI, including attachments and other
supporting materials, will become part
of the public record and subject to
public disclosure. Please do not include
in your submissions sensitive personal
information, such as account numbers
or Social Security numbers, or names of
other individuals, or other information
that you would not ordinarily make
public, such as trade secrets or
confidential commercial information.
Submissions will not be edited to
remove any identifying or contact
information, or other information that
you would not ordinarily make public.
If you wish to submit trade secret or
confidential commercial information,
please contact the individuals listed in
the FOR FURTHER INFORMATION CONTACT
section below. Information submitted to
the Bureau will be treated in accordance
with the Bureau’s Rule on the
Disclosure of Records and Information,
12 CFR part 1070 et seq.
NCUA: You may submit comments to
the NCUA, Docket No. NCUA –2021–
0023, by any of the methods set forth
below. Commenters are encouraged to
submit comments through the Federal
eRulemaking Portal, if possible. Please
use the title ‘‘Request for Information
and Comment: Financial Institutions’
Use of Artificial Intelligence, including
Machine Learning’’ to facilitate the
organization and distribution of the
comments. (Please send comments by
one method only):
• Federal eRulemaking Portal:
www.regulations.gov. Follow the
instructions for submitting comments.
• Fax: (703) 518–6319.
• Mail: Address to Melane ConyersAusbrooks, Secretary of the Board,
National Credit Union Administration,
1775 Duke Street, Alexandria, VA.
22314–3428.
In general, the NCUA will enter all
comments received into the docket and
publish the comments on the
Regulations.gov website without
change, including any business or
personal information that you provide
such as name and address information,
email addresses, or phone numbers.
Comments received, including
attachments and other supporting
materials, are part of the public record
and subject to public disclosure. Do not
include any information in your
comment or supporting materials that
you consider confidential or
inappropriate for public disclosure.
You may review comments and other
related materials that pertain to this
Request for Information by any of the
following methods:
• Viewing Comments Electronically:
You may view all public comments on
PO 00000
Frm 00138
Fmt 4703
Sfmt 4703
the Federal eRulemaking Portal at
https://www.regulations.gov as
submitted, except for those NCUA
cannot post for technical reasons.
• Due to social distancing measures
in effect, the usual opportunity to
inspect paper copies of comments in the
NCUA’s law library is not currently
available. After social distancing
measures are relaxed, visitors may make
an appointment to review paper copies
by calling (703) 518–6540 or emailing
OGCMail@ncua.gov.
FOR FURTHER INFORMATION CONTACT:
OCC: Kevin Greenfield, Deputy
Comptroller for Operational Risk,
Norine Richards, Director for Bank
Technology Operations, Paul Reymann,
Director for Consumer Compliance
Policy, or Siobhan Williams, Bank
Information Technology Analyst, Bank
Supervision Policy Department, (202)
649–6550; Beth Knickerbocker, Chief
Innovation Officer, or Maggie Colvin,
Innovation Officer, Office of Innovation,
(202) 649–5200; Alireza Ebrahim, Senior
Financial Economist, Risk Analytics
Division, (202) 649–5515; or Jorge D.
Aguilar, Counsel, Chief Counsel’s
Office, (202) 649–7187.
Board: David Palmer, Lead Financial
Institution Policy Analyst, (202) 452–
2904, Jeff Ernst, Lead Financial
Institution Policy Analyst, (202) 452–
2814, or Kavita Jain, Deputy Associate
Director, (202) 452–2062, Division of
Supervision and Regulation; Dana
Miller, Senior Counsel, (202) 452–2751,
or Carol Evans, Associate Director, (202)
452–2051, Division of Consumer and
Community Affairs, or Patricia Yeh,
Senior Counsel, (202) 452–3089, or
Gavin Smith, Senior Counsel, (202)
452–3474, Legal Division, Board of
Governors of the Federal Reserve
System, 20th and C Streets NW,
Washington, DC 20551. For the hearing
impaired only, Telecommunication
Device for the Deaf (TDD), (202) 263–
4869.
FDIC: Sumaya Muraywid, Senior
Examination Specialist, Division of Risk
Management Supervision, (202) 898–
3904, smuraywid@fdic.gov, David
Friedman, Senior Policy Analyst,
Division of Depositor and Consumer
Protection, 202–898–7168, dfriedman@
fdic.gov; or Chris Ledoux, Corporate
Expert, Legal Division, 202–898–3535,
cledoux@fdic.gov.
Bureau: Albert D. Chang, Senior
Counsel, Office of Innovation, (202)
450–7299; Patrice Alexander Ficklin,
Fair Lending Director, Office of Fair
Lending & Equal Opportunity, (202)
435–7205; and Kathryn Lazarev, Senior
Counsel, Office of Regulations, (202)
435–7700. If you require this document
E:\FR\FM\31MRN1.SGM
31MRN1
Federal Register / Vol. 86, No. 60 / Wednesday, March 31, 2021 / Notices
The agencies support responsible
innovation by financial institutions that
includes the identification and
management of risks associated with the
use of new technologies and techniques.
With appropriate governance, risk
management, and compliance
management, financial institutions’ use
of innovative technologies and
techniques, such as those involving AI,
has the potential to augment business
decision-making, and enhance services
available to consumers and businesses.
The Appendix of this RFI includes a
non-comprehensive list of laws,
regulations, and other agency issuances
that may be relevant to the use of AI
approaches by agency-supervised
institutions.1 Financial institutions are
exploring AI-based applications in a
variety of fields. Uses of AI by financial
institutions include (but are not limited
to):
• Flagging unusual transactions. This
involves employing AI to identify
potentially suspicious, anomalous, or
outlier transactions (e.g., fraud detection
and financial crime monitoring). It
involves using different forms of data
(e.g., email text, audio data—both
structured 2 and unstructured), with the
aim of identifying fraud or anomalous
transactions with greater accuracy and
timeliness. It also includes identifying
transactions for Bank Secrecy Act/antimoney laundering investigations,
monitoring employees for improper
practices, and detecting data anomalies.
• Personalization of customer
services. AI technologies, such as voice
recognition and natural language
processing (NLP),3 are used to improve
customer experience and to gain
efficiencies in the allocation of financial
institution resources. One example is
the use of chatbots 4 to automate routine
customer interactions, such as account
opening activities and general customer
inquiries. AI is leveraged at call centers
to process and triage customer calls to
provide customized service. These
technologies are also used to better
target marketing and customize trade
recommendations.
• Credit decisions. This involves the
use of AI to inform credit decisions in
order to enhance or supplement existing
techniques. This application of AI may
use traditional data or employ
alternative data 5 (such as cash flow
transactional information from a bank
account).
• Risk management. AI may be used
to augment risk management and
control practices. For example, an AI
approach might be used to complement
and provide a check on another, more
traditional credit model. Financial
institutions may also use AI to enhance
credit monitoring (including through
early warning alerts), payment
collections, loan restructuring and
recovery, and loss forecasting. AI can
assist internal audit and independent
risk management to increase sample size
(such as for testing), evaluate risk, and
refer higher-risk issues to human
analysts. AI may also be used in
liquidity risk management, for example,
to enhance monitoring of market
conditions or collateral management.
• Textual analysis. Textual analysis
refers to the use of NLP for handling
unstructured data (generally text) and
obtaining insights from that data or
improving efficiency of existing
processes. Applications include analysis
of regulations, news flow, earnings
reports, consumer complaints, analyst
ratings changes, and legal documents.
• Cybersecurity. AI may be used to
detect threats and malicious activity,
reveal attackers, identify compromised
systems, and support threat mitigation.
Examples include real-time
investigation of potential attacks, the
use of behavior-based detection to
collect network metadata, flagging and
blocking of new ransomware and other
malicious attacks, identifying
compromised accounts and files
involved in exfiltration, and deep
forensic analysis of malicious files.
1 In this RFI, the term ‘‘AI approach’’ refers to a
tool, model, process, or application that employs AI
technology in some form.
2 The term ‘‘structured data’’ generally refers to a
set of data that has been systematically organized
or arranged.
3 ‘‘Natural language processing’’ generally refers
to the use of computers to understand or analyze
natural language text or speech.
4 The term ‘‘chatbot’’ generally refers to a software
application used to conduct an on-line chat
conversation via text or text-to-speech, in lieu of
providing direct contact with a live human agent.
5 For the purposes of this RFI, alternative data
means information not typically found in the
consumer’s credit files of the nationwide consumer
reporting agencies or customarily provided by
consumers as part of applications for credit.
in an alternative electronic format,
please contact CFPB_Accessibility@
cfpb.gov.
NCUA: Timothy Segerson, Deputy
Director, Office of Examination &
Insurance, 703–518–6300; Chrisanthy
Loizos, Senior Trial Attorney, Office of
General Counsel, 703–518–6540; Joe
Goldberg, Director, Division of
Consumer Compliance Policy and
Outreach, 703–518–1142.
SUPPLEMENTARY INFORMATION:
jbell on DSKJLSW7X2PROD with NOTICES
Background Information
VerDate Sep<11>2014
18:54 Mar 30, 2021
Jkt 253001
PO 00000
Frm 00139
Fmt 4703
Sfmt 4703
16839
Potential Benefits of AI
AI has the potential to offer improved
efficiency, enhanced performance, and
cost reduction for financial institutions,
as well as benefits to consumers and
businesses. AI can identify relationships
among variables that are not intuitive or
not revealed by more traditional
techniques. AI can better process certain
forms of information, such as text, that
may be impractical or difficult to
process using traditional techniques. AI
also facilitates processing significantly
large and detailed datasets, both
structured and unstructured, by
identifying patterns or correlations that
would be impracticable to ascertain
otherwise.
Other potential AI benefits include
more accurate, lower-cost, and faster
underwriting, as well as expanded
credit access for consumers and small
businesses that may not have obtained
credit under traditional credit
underwriting approaches. AI
applications may also enhance an
institution’s ability to provide products
and services with greater customization.
Potential Risks of AI
It is important for financial
institutions to have processes in place
for identifying and managing potential
risks associated with AI, as they do for
any process, tool, or model employed.
Many of the potential risks associated
with using AI are not unique to AI. For
instance, the use of AI could result in
operational vulnerabilities, such as
internal process or control breakdowns,
cyber threats, information technology
lapses, risks associated with the use of
third parties, and model risk, all of
which could affect a financial
institution’s safety and soundness. The
use of AI can also create or heighten
consumer protection risks, such as risks
of unlawful discrimination, unfair,
deceptive, or abusive acts or practices
(UDAAP) under the Dodd-Frank Wall
Street Reform and Consumer Protection
Act (Dodd-Frank Act), unfair or
deceptive acts or practices (UDAP)
under the Federal Trade Commission
Act (FTC Act), or privacy concerns.
AI may present particular risk
management challenges to financial
institutions in the areas of
explainability, data usage, and dynamic
updating.
• Explainability. For the purposes of
this RFI, explainability refers to how an
AI approach uses inputs to produce
outputs. Some AI approaches can
exhibit a ‘‘lack of explainability’’ for
their overall functioning (sometimes
known as global explainability) or how
they arrive at an individual outcome in
E:\FR\FM\31MRN1.SGM
31MRN1
16840
Federal Register / Vol. 86, No. 60 / Wednesday, March 31, 2021 / Notices
a given situation (sometimes referred to
as local explainability). Lack of
explainability can pose different
challenges in different contexts. Lack of
explainability can also inhibit financial
institution management’s understanding
of the conceptual soundness 6 of an AI
approach, which can increase
uncertainty around the AI approach’s
reliability, and increase risk when used
in new contexts. Lack of explainability
can also inhibit independent review and
audit and make compliance with laws
and regulations, including consumer
protection requirements, more
challenging.
• Broader or More Intensive Data
Usage. Data plays a particularly
important role in AI. In many cases, AI
algorithms identify patterns and
correlations in training data without
human context or intervention, and then
use that information to generate
predictions or categorizations.7 Because
the AI algorithm is dependent upon the
training data, an AI system generally
reflects any limitations of that dataset.
As a result, as with other systems, AI
may perpetuate or even amplify bias or
inaccuracies inherent in the training
data, or make incorrect predictions if
that data set is incomplete or nonrepresentative.
• Dynamic Updating. Some AI
approaches have the capacity to update
on their own, sometimes without
human interaction, often known as
dynamic updating. Monitoring and
tracking an AI approach that evolves on
its own can present challenges in review
and validation, particularly when a
change in external circumstances (e.g.,
economic downturns and financial
crises) may cause inputs to vary
materially from the original training
data. Dynamic updating techniques can
produce changes that range from minor
adjustments to existing elements of a
model to the introduction of entirely
new elements.
jbell on DSKJLSW7X2PROD with NOTICES
Request for Comment
As discussed, the agencies recognize
that AI has the potential to offer
improved efficiency, enhanced
performance, and cost reduction for
financial institutions, as well as benefits
to consumers and businesses. In this
RFI, the agencies are seeking
information on financial institutions’
6 For this RFI, the term ‘‘conceptual soundness’’
generally refers to the quality of the theory, design,
methodology, data, developmental testing, and
confirmation that an approach is appropriate for the
intended use.
7 In this context, training data are the data used
to develop and calibrate an AI approach; for
example, a financial institution might use a large
dataset of past fraudulent transactions to train the
approach to detect and prevent future fraud.
VerDate Sep<11>2014
18:54 Mar 30, 2021
Jkt 253001
risk management practices related to the
use of AI; barriers or challenges facing
financial institutions when developing,
adopting, and managing AI and its risks;
and benefits to financial institutions and
their customers from the use of AI. The
RFI also solicits respondents’ views on
the use of AI in financial services,
which will help the agencies determine
whether any clarification would be
helpful for financial institutions’ use of
AI in a safe and sound manner and in
compliance with applicable laws and
regulations, including those related to
consumer protection.
Explainability
Understanding the conceptual
soundness of any model, tool,
application, or system aids in managing
its risks including those related to lack
of explainability. The importance of
conceptual soundness is described in
existing agency guidance and is well
established in industry practice. For
traditional approaches, conceptual
soundness is foundational both to
development and validation/
independent review. In the case of
certain less transparent AI approaches,
however, evaluations of conceptual
soundness can be complicated. The
underlying theory and logic may be less
accessible to users than that of
traditional approaches or more
transparent AI approaches. Without
insight into an approach’s general
operating principles, financial
institution management may not be able
to evaluate with confidence how the
system will function in unforeseen
circumstances. To address lack of
explainability of certain AI approaches,
researchers have developed techniques
to help explain predictions or
categorizations. These techniques are
often referred to as ‘‘post-hoc’’ methods,
because they are used to interpret the
outputs rather than the design.
Question 1: How do financial
institutions identify and manage risks
relating to AI explainability? What
barriers or challenges for explainability
exist for developing, adopting, and
managing AI?
Question 2: How do financial
institutions use post-hoc methods to
assist in evaluating conceptual
soundness? How common are these
methods? Are there limitations of these
methods (whether to explain an AI
approach’s overall operation or to
explain a specific prediction or
categorization)? If so, please provide
details on such limitations.
Question 3: For which uses of AI is
lack of explainability more of a
challenge? Please describe those
challenges in detail. How do financial
PO 00000
Frm 00140
Fmt 4703
Sfmt 4703
institutions account for and manage the
varied challenges and risks posed by
different uses?
Risks From Broader or More Intensive
Data Processing and Usage
Like other systems, AI is designed to
interact directly with training data to
identify correlations and patterns and
use that information for prediction or
categorization. This means that data
quality is important for AI. If the
training data are biased or incomplete,
AI may incorporate those shortcomings
into its predictions or categorizations.
AI may use alternative datasets in
certain applications (such as credit
underwriting, fraud detection, and
trading) in ways that can assist in
identifying related trends or predictions
that may be difficult to identify with
traditional methods. The importance of
practices such as data quality
assessments to determine relevance and
suitability of data used in a model, may
be heightened in the use of AI. Finally,
in many cases, AI developers process or
optimize raw data so that the data can
be better used for training. Various data
processing techniques exist, some of
which may affect performance.
Question 4: How do financial
institutions using AI manage risks
related to data quality and data
processing? How, if at all, have control
processes or automated data quality
routines changed to address the data
quality needs of AI? How does risk
management for alternative data
compare to that of traditional data? Are
there any barriers or challenges that data
quality and data processing pose for
developing, adopting, and managing AI?
If so, please provide details on those
barriers or challenges.
Question 5: Are there specific uses of
AI for which alternative data are
particularly effective?
Overfitting
‘‘Overfitting’’ can occur when an
algorithm ‘‘learns’’ from idiosyncratic
patterns in the training data that are not
representative of the population as a
whole. Overfitting is not unique to AI,
but it can be more pronounced in AI
than with traditional models.
Undetected overfitting could result in
incorrect predictions or categorizations.
Question 6: How do financial
institutions manage AI risks relating to
overfitting? What barriers or challenges,
if any, does overfitting pose for
developing, adopting, and managing AI?
How do financial institutions develop
their AI so that it will adapt to new and
potentially different populations
(outside of the test and training data)?
E:\FR\FM\31MRN1.SGM
31MRN1
Federal Register / Vol. 86, No. 60 / Wednesday, March 31, 2021 / Notices
jbell on DSKJLSW7X2PROD with NOTICES
Cybersecurity Risk
Like other data-intensive
technologies, AI may be exposed to risk
from a variety of criminal cybersecurity
threats. For example, AI can be
vulnerable to ‘‘data poisoning attacks,’’
which attempt to corrupt and
contaminate training data to
compromise the system’s performance.
Question 7: Have financial
institutions identified particular
cybersecurity risks or experienced such
incidents with respect to AI? If so, what
practices are financial institutions using
to manage cybersecurity risks related to
AI? Please describe any barriers or
challenges to the use of AI associated
with cybersecurity risks. Are there
specific information security or
cybersecurity controls that can be
applied to AI?
Dynamic Updating
A particular characteristic of some AI
is the ability for it to learn or evolve
over time, especially as it captures new
training data. Over time, this could
result in drift (i.e., the AI approach
could change) as it learns from the new
data. This can present challenges for
validating, monitoring, tracking, and
documenting the AI approach,
including for persons conducting an
independent review. It may be
important to understand whether an AI
approach that was independently
reviewed initially has significantly
evolved over time (e.g., using an influx
of new data). Dynamic updating can
also affect how results are tracked over
time. For example, initial performance
thresholds chosen to monitor the
approach could become less meaningful
if the AI approach has significantly
changed to focus on different target
outcomes. Similar risks can arise with
AI approaches that are not updated as
their context evolves, since they are
more closely tuned to their training
data. For example, AI approaches that
are validated in one circumstance may
not perform well in another, and an
independent review conducted in a
previous context may no longer be
accurate in new circumstances.
Question 8: How do financial
institutions manage AI risks relating to
dynamic updating? Describe any
barriers or challenges that may impede
the use of AI that involve dynamic
updating. How do financial institutions
gain an understanding of whether AI
approaches producing different outputs
over time based on the same inputs are
operating as intended?
AI Use by Community Institutions
A financial institution’s AI strategy,
use of AI, and associated risk
VerDate Sep<11>2014
18:54 Mar 30, 2021
Jkt 253001
management practices could vary
substantially based on the financial
institution’s size, complexity of
operations, business model, staffing,
and risk profile, and this could affect
the corresponding risks that arise.
Community institutions may be more
likely to use third-party AI approaches
or rely on third-party services that use
AI. This may pose different challenges
(e.g., level of expertise of AI or insight
into the third-party AI approach) in a
financial institution’s adoption of AI.
Question 9: Do community
institutions face particular challenges in
developing, adopting, and using AI? If
so, please provide detail about such
challenges. What practices are
employed to address those impediments
or challenges?
Oversight of Third Parties
Financial institutions may opt to use
AI developed by third parties, rather
than develop the approach internally.
Existing agency guidance (as noted in
the Appendix) describes information
and risks that may be relevant to
financial institutions when selecting
third-party approaches (including ones
using AI) and sets out principles for the
validation of such third-party
approaches.
Question 10: Please describe any
particular challenges or impediments
financial institutions face in using AI
developed or provided by third parties
and a description of how financial
institutions manage the associated risks.
Please provide detail on any challenges
or impediments. How do those
challenges or impediments vary by
financial institution size and
complexity?
Fair Lending
Depending on the specific use, there
may be uncertainty about how less
transparent and explainable AI
approaches align with applicable
consumer protection legal and
regulatory frameworks, which often
address fairness and transparency. For
example, it may be challenging to verify
that a less transparent and explainable
approach comports with fair lending
laws.
Question 11: What techniques are
available to facilitate or evaluate the
compliance of AI-based credit
determination approaches with fair
lending laws or mitigate risks of noncompliance? Please explain these
techniques and their objectives,
limitations of those techniques, and
how those techniques relate to fair
lending legal requirements.
Question 12: What are the risks that
AI can be biased and/or result in
PO 00000
Frm 00141
Fmt 4703
Sfmt 4703
16841
discrimination on prohibited bases? Are
there effective ways to reduce risk of
discrimination, whether during
development, validation, revision, and/
or use? What are some of the barriers to
or limitations of those methods?
Question 13: To what extent do model
risk management principles and
practices aid or inhibit evaluations of
AI-based credit determination
approaches for compliance with fair
lending laws?
Question 14: As part of their
compliance management systems,
financial institutions may conduct fair
lending risk assessments by using
models designed to evaluate fair lending
risks (‘‘fair lending risk assessment
models’’). What challenges, if any, do
financial institutions face when
applying internal model risk
management principles and practices to
the development, validation, or use of
fair lending risk assessment models
based on AI?
Question 15: The Equal Credit
Opportunity Act (ECOA), which is
implemented by Regulation B, requires
creditors to notify an applicant of the
principal reasons for taking adverse
action for credit or to provide an
applicant a disclosure of the right to
request those reasons. What approaches
can be used to identify the reasons for
taking adverse action on a credit
application, when AI is employed? Does
Regulation B provide sufficient clarity
for the statement of reasons for adverse
action when AI is used? If not, please
describe in detail any opportunities for
clarity.
Additional Considerations
Question 16: To the extent not already
discussed, please identify any
additional uses of AI by financial
institutions and any risk management
challenges or other factors that may
impede adoption and use of AI.
Question 17: To the extent not already
discussed, please identify any benefits
or risks to financial institutions’
customers or prospective customers
from the use of AI by those financial
institutions. Please provide any
suggestions on how to maximize
benefits or address any identified risks.
Appendix: Laws, Regulations,
Supervisory Guidance, and Other
Agency Statements Relevant to AI
This Appendix contains a list of laws,
regulations, supervisory guidance, and other
statements issued by the agencies that may be
relevant to AI. This includes existing laws
and regulations relating to safety and
soundness and consumer protection. The
items below do not constitute an exhaustive
list; other laws, regulations, guidance, and
statements may be relevant based on the
E:\FR\FM\31MRN1.SGM
31MRN1
16842
Federal Register / Vol. 86, No. 60 / Wednesday, March 31, 2021 / Notices
• CFPB Innovation Spotlight on Providing
Adverse Action Notices When Using AI/
ML Models 14
particular facts and circumstances. Some
laws and regulations are applicable to any
process or tool a financial institution
employs, regardless of whether a financial
institution utilizes AI or how.
Laws and Regulations
• Section 39 of the Federal Deposit Insurance
Act as implemented through the agencies’
safety and soundness regulations 8
• Sections 501 and 505(b) of Gramm-LeachBliley Act as implemented through the
agencies’ regulations and standards,
including Interagency Guidelines
Establishing Information Security
Standards 9
• Fair Credit Reporting Act (FCRA)/Reg. V
• Equal Credit Opportunity Act (ECOA)/Reg.
B
• Fair Housing Act (FHA)
• Section 5 of the Federal Trade Commission
Act (prohibiting UDAP) and sections 1031
and 1036 of the Dodd-Frank Act
(prohibiting unfair, deceptive, or abusive
acts or practices (UDAAP))
Supervisory Guidance and Statements
• Interagency Statement on the Use of
Alternative Data in Credit Underwriting 10
• Supervisory Guidance on Model Risk
Management 11
• Third-Party/Outsourcing Risk
Management 12
• New, Modified, or Expanded Bank
Products and Services 13
Examination Manuals/Procedures/Other
Resources
• Federal Financial Institutions Examination
Council Information Technology
Examination Handbook 15
• Consumer Compliance Manuals and
Booklets 16
• Interagency Fair Lending Examination
Procedures 17
• CFPB Examination Procedures, ECOA
Baseline Review Module 5: Fair Lending
Risks Related to Models 18
Blake J. Paulson,
Acting Comptroller of the Currency.
By order of the Board of Governors of the
Federal Reserve System.
Ann Misback,
Secretary of the Board.
Federal Deposit Insurance Corporation.
Dated at Washington, DC, on or about
February 25, 2021.
James P. Sheesley,
Assistant Executive Secretary.
David Uejio,
Acting Director, Bureau of Consumer
Financial Protection.
Melane Conyers-Ausbrooks,
Secretary of the Board, National Credit Union
Administration.
[FR Doc. 2021–06607 Filed 3–30–21; 8:45 am]
jbell on DSKJLSW7X2PROD with NOTICES
8 Refer
to the Interagency Guidelines Establishing
Standards for Safety and Soundness, 12 CFR 364,
Appendix A (FDIC); 12 CFR 263 (FRB); 12 CFR 30,
appendix A (OCC).
9 Refer to the Interagency Guidelines Establishing
Information Security Standards, 12 CFR 364,
Appendix B (FDIC); 12 CFR 208, Appendix D–2 and
12 CFR 225, Appendix F (FRB); 12 CFR 30,
appendix B (OCC); Guidelines for Safeguarding
Member Information, 12 CFR 748, Appendix A
(NCUA).
10 Refer to FDIC FIL–82–2019, https://
www.fdic.gov/news/financial-institution-letters/
2019/fil19082.html; Federal Reserve CA Letter 19–
11, https://www.federalreserve.gov/supervisionreg/
caletters/caletters.htm; and OCC Bulletin 2019–62,
https://www.occ.gov/news-issuances/bulletins/
2019/bulletin-2019-62.html.
11 Refer to the ‘‘Supervisory Guidance on Model
Risk Management,’’ Federal Reserve SR Letter 11–
7, https://www.federalreserve.gov/supervisionreg/
srletters/srletters.htm; OCC Bulletin 2011–12,
https://www.occ.gov/news-issuances/bulletins/
2011/bulletin-2011-12.html; and FDIC Financial
Institution Letter (FIL)–22–2017, https://
www.fdic.gov/news/financial-institution-letters/
2017/fil17022.html.
12 FDIC: Guidance for Managing Third-Party Risk
(FIL)–44–2008, https://www.fdic.gov/news/
financial-institution-letters/2008/fil08044.html;
OCC Bulletin 2013–29, OCC Bulletin 2020–10;
NCUA: Evaluating Third Party Relationships,
Supervisory Letter (SL) 07–01 (Oct. 2007); and FRB:
Guidance on Outsourcing Risk (SR 13–19), https://
www.federalreserve.gov/supervisionreg/srletters/
srletters.htm.
13 OCC Bulletin 2017–43, https://
www.occ.treas.gov/news-issuances/bulletins/2017/
bulletin-2017-43.html; and NCUA 19–CU–04 (Dec.
2019), https://www.ncua.gov/regulationsupervision/letters-credit-unions-other-guidance/
use-alternative-data-credit-underwriting.
VerDate Sep<11>2014
18:54 Mar 30, 2021
Jkt 253001
BILLING CODE 4810–33–P; 6210–01–P; 4810–AM–P;
6714–01–P
DEPARTMENT OF THE TREASURY
Proposed Collection; Comment
Request
Departmental offices,
Department of the Treasury.
SUMMARY: The Department of the
Treasury, as part of its continuing effort
to reduce paperwork burdens, invites
the general public and other Federal
AGENCY:
14 Patrice Alexander Ficklin, Tom Pahl, and Paul
Watkins, CFPB Blog, Innovation spotlight:
Providing adverse action notices when using AI/ML
models (July 7, 2020), available at https://
www.consumerfinance.gov/about-us/blog/
innovation-spotlight-providing-adverse-actionnotices-when-using-ai-ml-models/.
15 FFIEC IT Handbook, https://
ithandbook.ffiec.gov/.
16 OCC Consumer Compliance series of
Comptroller’s Handbook booklets, https://
www.occ.treas.gov/topics/supervision-andexamination/consumer-compliance/indexconsumer-compliance.html; NCUA: Evaluating
Compliance Risk—Updated Compliance Indicators,
SL–17–01 (March 2017), https://www.ncua.gov/
regulation-supervision/letters-credit-unions-otherguidance/use-alternative-data-credit-underwriting.
17 Interagency Fair Lending Examination
Procedures, https://www.ffiec.gov/PDF/fairlend.pdf.
18 See, CFPB ECOA Baseline Review, p. 24,
https://files.consumerfinance.gov/f/documents/
cfpb_supervision-and-examination-manual_ecoabaseline-exam-procedures_2019-04.pdf.
PO 00000
Frm 00142
Fmt 4703
Sfmt 4703
agencies to comment on the revision of
a currently approved information
collection that is to be proposed for
approval by the Office of Management
and Budget. The Office of International
Affairs of the Department of the
Treasury is soliciting comments
concerning Treasury International
Capital (TIC) Form SHL/SHLA, ‘‘Survey
of Foreign-Residents’ Holdings of U.S.
Securities, including Selected Money
Market Instruments’’.
DATES: Written comments should be
received on or before June 1, 2021 to be
assured of consideration.
ADDRESSES: Direct all written comments
to Dwight Wolkow, International
Portfolio Investment Data Systems,
Department of the Treasury, Room 1050
MT, 1500 Pennsylvania Avenue NW,
Washington, DC 20220. In view of
possible delays in mail delivery, please
also notify Mr. Wolkow by email
(comments2TIC@do.treas.gov) or by
telephone (cell: 202–923–0518).
FOR FURTHER INFORMATION CONTACT:
Copies of the proposed forms and
instructions are available on the
Treasury International Capital (TIC)
Forms web page ‘‘Forms SHL/SHLA |
U.S. Department of the Treasury’’.
Requests for additional information
should be directed to Mr. Wolkow by
email (comments2TIC@treasury.gov), or
by telephone (cell: 202–923–0518).
SUPPLEMENTARY INFORMATION:
Title: Treasury International Capital
(TIC) Form SHL/SHLA, ‘‘Survey of
Foreign-Residents’ Holdings of U.S.
Securities, including Selected Money
Market Instruments’’.
OMB Control Number: 1505–0123.
Abstract: This form collects foreignresidents’ holdings of U.S. securities.
These data are used by the U.S.
Government in the formulation of
international financial and monetary
policies, and for the computation of the
U.S. balance of payments accounts and
of the U.S. international investment
position. These data are also used to
provide information to the public and to
meet international reporting
commitments. The data collection
includes large benchmark surveys (Form
SHL) conducted every five years, and
smaller annual surveys (Form SHLA)
conducted in the non-benchmark years.
The data collected under an annual
survey are used in conjunction with the
results of the preceding benchmark
survey and of recent TIC form SLT
(‘‘Aggregate Holdings of Long-Term
Securities by U.S. and Foreign
Residents’’) reporting to make economywide estimates for that non-benchmark
year. Currently, the determination of
who must report in the annual surveys
E:\FR\FM\31MRN1.SGM
31MRN1
]]>Agencies
[Federal Register Volume 86, Number 60 (Wednesday, March 31, 2021)]
[Notices]
[Pages 16837-16842]
From the Federal Register Online via the Government Publishing Office [www.gpo.gov]
[FR Doc No: 2021-06607]
-----------------------------------------------------------------------
DEPARTMENT OF THE TREASURY
Office of the Comptroller of the Currency
[Docket ID OCC-2020-0049]
BOARD OF GOVERNORS OF THE FEDERAL RESERVE SYSTEM
[Docket No. OP-1743]
FEDERAL DEPOSIT INSURANCE CORPORATION
RIN 3064-ZA24
BUREAU OF CONSUMER FINANCIAL PROTECTION
[Docket No. CFPB-2021-0004]
NATIONAL CREDIT UNION ADMINISTRATION
[Docket No. NCUA-2021-0023]
Request for Information and Comment on Financial Institutions'
Use of Artificial Intelligence, Including Machine Learning
AGENCY: Board of Governors of the Federal Reserve System, Bureau of
Consumer Financial Protection, Federal Deposit Insurance Corporation,
National Credit Union Administration, and Office of the Comptroller of
the Currency (agencies).
ACTION: Request for information and comment.
-----------------------------------------------------------------------
SUMMARY: The agencies are gathering information and comments on
financial institutions' use of artificial intelligence (AI), including
machine learning (ML). The purpose of this request for information
(RFI) is to understand respondents' views on the use of AI by financial
institutions in their provision of services to customers and for other
business or operational purposes; appropriate governance, risk
management, and controls over AI; and any challenges in developing,
adopting, and managing AI. The RFI also solicits respondents' views on
the use of AI in financial services to assist in determining whether
any clarifications from the agencies would be helpful for financial
institutions' use of AI in a safe and sound manner and in compliance
with applicable laws and regulations, including those related to
consumer protection.
DATES: Comments must be received by June 1, 2021.
ADDRESSES: Interested parties are encouraged to submit written comments
jointly to all of the agencies. Commenters are encouraged to use the
title ``Request for Information and Comment on Financial Institutions'
Use of Artificial Intelligence, including Machine Learning'' to
facilitate the organization and distribution of comments among the
agencies. Commenters are also encouraged to identify the number of the
specific question for comment to which they are responding. Please send
comments by one method only and should be directed to:
OCC: Commenters are encouraged to submit comments through the
Federal eRulemaking Portal. Please use the title ``Request for
Information on Financial Institutions' Use of Artificial Intelligence,
including Machine Learning; Request for Comment'' to facilitate the
organization and distribution of the comments. You may submit comments
by any of the following methods:
Federal eRulemaking Portal--Regulations.gov: Go to https://regulations.gov/. Enter ``Docket ID OCC-2020-0049'' in the Search Box
and click ``Search.'' Public comments can be submitted via the
``Comment'' box below the displayed document information or by clicking
on the document title and then clicking the ``Comment'' box on the top-
left side of the screen. For help with submitting effective comments
please click on ``Commenter's Checklist.'' For assistance with the
Regulations.gov site, please call (877) 378-5457 (toll free) or (703)
454-9859 Monday-Friday, 9am-5pm ET or email
[email protected].
Mail: Chief Counsel's Office, Attention: Comment
Processing, Office of the Comptroller of the Currency, 400 7th Street
SW, Suite 3E-218, Washington, DC 20219.
Hand Delivery/Courier: 400 7th Street SW, Suite 3E-218,
Washington, DC 20219. Instructions: You must include ``OCC'' as the
agency name and ``Docket ID OCC-2020-0049'' in your comment. In
general, the OCC will enter all comments received into the docket and
publish the comments on the Regulations.gov website without change,
including any business or personal information provided such as name
and address information, email addresses, or phone numbers. Comments
received, including attachments and other supporting materials, are
part of the public record and subject to public disclosure. Do not
include any information in your comment or supporting materials that
you consider confidential or inappropriate for public disclosure.
You may review comments and other related materials that pertain to
this action by the following method:
Viewing Comments Electronically--Regulations.gov: Go to
https://regulations.gov/. Enter ``Docket ID OCC-2020-0049'' in the
Search Box and click ``Search.'' Click on the ``Documents'' tab and
then the document's title. After clicking the document's title, click
the ``Browse Comments'' tab. Comments can be viewed and filtered by
clicking on the ``Sort By'' drop-down on the right side of the screen
or the ``Refine Results'' options on the left side of the screen.
Supporting materials can be viewed by clicking on the ``Documents'' tab
and filtered by clicking on the ``Sort By'' drop-down on the right side
of the screen or the ``Refine Documents Results'' options on the left
side of the screen.'' For assistance with the Regulations.gov site,
please call (877) 378-5457 (toll free) or (703) 454-9859 Monday-Friday,
9am-5pm ET or email [email protected]. The docket may
be viewed after the close of the comment period in the same manner as
during the comment period.
Board: You may submit comments, identified by Docket No. OP-1743,
by any of the following methods:
Agency website: https://www.federalreserve.gov. Follow the
instructions for submitting comments at https://www.federalreserve.gov/generalinfo/foia/ProposedRegs.cfm.
Email: [email protected]. Include docket
number in the subject line of the message.
Fax: (202) 452-3819 or (202) 452-3102.
Mail: Ann E. Misback, Secretary, Board of Governors of the Federal
Reserve System, 20th Street and Constitution Avenue NW, Washington, DC
20551. All public comments will be made available on the Board's
website at https://www.federalreserve.gov/generalinfo/foia/ProposedRegs.cfm as submitted, unless modified for technical reasons or
to remove personally identifiable information or other confidential
information at the commenter's request. Accordingly, your comments will
not be edited to remove any identifying or contact information. Public
comments may also be viewed in paper in Room 146, 1709 New York Avenue
NW, Washington, DC 20006, between 9:00 a.m. and 5:00 p.m. on weekdays.
FDIC
Agency website: https://www.fdic.gov/regulations/laws/federal/. Follow the instructions for submitting comments on the
agency's website.
[[Page 16838]]
Email: [email protected]. Include RIN 3064-ZA24 in the
subject line of the message.
Mail: James P. Sheesley, Assistant Executive Secretary,
Attention: Comments-RIN 3064-ZA24, Federal Deposit Insurance
Corporation, 550 17th Street NW, Washington, DC 20429.
Hand Delivery/Courier: Comments may be hand-delivered to
the guard station at the rear of the 550 17th Street NW building
(located on F Street) on business days between 7:00 a.m. and 5:00 p.m.
Public Inspection: All comments received will be posted without change
to https://www.fdic.gov/regulations/laws/federal/--including any
personal information provided-- for public inspection. Paper copies of
public comments may be ordered from the FDIC Public Information Center,
3501 North Fairfax Drive, Room E-1002, Arlington, VA 22226 or by
telephone at (877) 275-3342 or (703) 562-2200.
Bureau of Consumer Financial Protection (Bureau)
You may submit responsive information and other comments,
identified by Docket No. CFPB-2021-0004, by any of the following
methods:
Federal eRulemaking Portal: Go to https://www.regulations.gov. Follow the instructions for submitting comments.
Email: [email protected]. Include Docket No. CFPB-2021-
0004 in the subject line of the message.
Mail/Hand Delivery/Courier: Comment Intake, Bureau of
Consumer Financial Protection, 1700 G Street NW, Washington, DC 20552.
Please note that due to circumstances associated with the COVID-19
pandemic, the CFPB discourages the submission of comments by mail, hand
delivery, or courier.
Instructions: 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, and in light of difficulties associated with mail
and hand deliveries during the COVID-19 pandemic, commenters are
encouraged to submit comments electronically.
In general, all comments received will be posted without change to
https://www.regulations.gov. In addition, once the Bureau's headquarters
reopens, comments will be available for public inspection and copying
at 1700 G Street NW, Washington, DC 20552, on official business days
between the hours of 10 a.m. and 5 p.m. Eastern Time. At that time, you
can make an appointment to inspect the documents by calling 202-435-
7275.
All submissions in response to this RFI, including attachments and
other supporting materials, will become part of the public record and
subject to public disclosure. Please do not include in your submissions
sensitive personal information, such as account numbers or Social
Security numbers, or names of other individuals, or other information
that you would not ordinarily make public, such as trade secrets or
confidential commercial information. Submissions will not be edited to
remove any identifying or contact information, or other information
that you would not ordinarily make public. If you wish to submit trade
secret or confidential commercial information, please contact the
individuals listed in the FOR FURTHER INFORMATION CONTACT section
below. Information submitted to the Bureau will be treated in
accordance with the Bureau's Rule on the Disclosure of Records and
Information, 12 CFR part 1070 et seq.
NCUA: You may submit comments to the NCUA, Docket No. NCUA -2021-
0023, by any of the methods set forth below. Commenters are encouraged
to submit comments through the Federal eRulemaking Portal, if possible.
Please use the title ``Request for Information and Comment: Financial
Institutions' Use of Artificial Intelligence, including Machine
Learning'' to facilitate the organization and distribution of the
comments. (Please send comments by one method only):
Federal eRulemaking Portal: www.regulations.gov. Follow
the instructions for submitting comments.
Fax: (703) 518-6319.
Mail: Address to Melane Conyers-Ausbrooks, Secretary of
the Board, National Credit Union Administration, 1775 Duke Street,
Alexandria, VA. 22314-3428.
In general, the NCUA will enter all comments received into the
docket and publish the comments on the Regulations.gov website without
change, including any business or personal information that you provide
such as name and address information, email addresses, or phone
numbers. Comments received, including attachments and other supporting
materials, are part of the public record and subject to public
disclosure. Do not include any information in your comment or
supporting materials that you consider confidential or inappropriate
for public disclosure.
You may review comments and other related materials that pertain to
this Request for Information by any of the following methods:
Viewing Comments Electronically: You may view all public
comments on the Federal eRulemaking Portal at https://www.regulations.gov as submitted, except for those NCUA cannot post for
technical reasons.
Due to social distancing measures in effect, the usual
opportunity to inspect paper copies of comments in the NCUA's law
library is not currently available. After social distancing measures
are relaxed, visitors may make an appointment to review paper copies by
calling (703) 518-6540 or emailing [email protected].
FOR FURTHER INFORMATION CONTACT: OCC: Kevin Greenfield, Deputy
Comptroller for Operational Risk, Norine Richards, Director for Bank
Technology Operations, Paul Reymann, Director for Consumer Compliance
Policy, or Siobhan Williams, Bank Information Technology Analyst, Bank
Supervision Policy Department, (202) 649-6550; Beth Knickerbocker,
Chief Innovation Officer, or Maggie Colvin, Innovation Officer, Office
of Innovation, (202) 649-5200; Alireza Ebrahim, Senior Financial
Economist, Risk Analytics Division, (202) 649-5515; or Jorge D.
Aguilar, Counsel, Chief Counsel's Office, (202) 649-7187.
Board: David Palmer, Lead Financial Institution Policy Analyst,
(202) 452-2904, Jeff Ernst, Lead Financial Institution Policy Analyst,
(202) 452-2814, or Kavita Jain, Deputy Associate Director, (202) 452-
2062, Division of Supervision and Regulation; Dana Miller, Senior
Counsel, (202) 452-2751, or Carol Evans, Associate Director, (202) 452-
2051, Division of Consumer and Community Affairs, or Patricia Yeh,
Senior Counsel, (202) 452-3089, or Gavin Smith, Senior Counsel, (202)
452-3474, Legal Division, Board of Governors of the Federal Reserve
System, 20th and C Streets NW, Washington, DC 20551. For the hearing
impaired only, Telecommunication Device for the Deaf (TDD), (202) 263-
4869.
FDIC: Sumaya Muraywid, Senior Examination Specialist, Division of
Risk Management Supervision, (202) 898-3904, [email protected], David
Friedman, Senior Policy Analyst, Division of Depositor and Consumer
Protection, 202-898-7168, [email protected]; or Chris Ledoux,
Corporate Expert, Legal Division, 202-898-3535, [email protected].
Bureau: Albert D. Chang, Senior Counsel, Office of Innovation,
(202) 450-7299; Patrice Alexander Ficklin, Fair Lending Director,
Office of Fair Lending & Equal Opportunity, (202) 435-7205; and Kathryn
Lazarev, Senior Counsel, Office of Regulations, (202) 435-7700. If you
require this document
[[Page 16839]]
in an alternative electronic format, please contact
[email protected].
NCUA: Timothy Segerson, Deputy Director, Office of Examination &
Insurance, 703-518-6300; Chrisanthy Loizos, Senior Trial Attorney,
Office of General Counsel, 703-518-6540; Joe Goldberg, Director,
Division of Consumer Compliance Policy and Outreach, 703-518-1142.
SUPPLEMENTARY INFORMATION:
Background Information
The agencies support responsible innovation by financial
institutions that includes the identification and management of risks
associated with the use of new technologies and techniques. With
appropriate governance, risk management, and compliance management,
financial institutions' use of innovative technologies and techniques,
such as those involving AI, has the potential to augment business
decision-making, and enhance services available to consumers and
businesses. The Appendix of this RFI includes a non-comprehensive list
of laws, regulations, and other agency issuances that may be relevant
to the use of AI approaches by agency-supervised institutions.\1\
Financial institutions are exploring AI-based applications in a variety
of fields. Uses of AI by financial institutions include (but are not
limited to):
---------------------------------------------------------------------------
\1\ In this RFI, the term ``AI approach'' refers to a tool,
model, process, or application that employs AI technology in some
form.
---------------------------------------------------------------------------
Flagging unusual transactions. This involves employing AI
to identify potentially suspicious, anomalous, or outlier transactions
(e.g., fraud detection and financial crime monitoring). It involves
using different forms of data (e.g., email text, audio data--both
structured \2\ and unstructured), with the aim of identifying fraud or
anomalous transactions with greater accuracy and timeliness. It also
includes identifying transactions for Bank Secrecy Act/anti-money
laundering investigations, monitoring employees for improper practices,
and detecting data anomalies.
---------------------------------------------------------------------------
\2\ The term ``structured data'' generally refers to a set of
data that has been systematically organized or arranged.
---------------------------------------------------------------------------
Personalization of customer services. AI technologies,
such as voice recognition and natural language processing (NLP),\3\ are
used to improve customer experience and to gain efficiencies in the
allocation of financial institution resources. One example is the use
of chatbots \4\ to automate routine customer interactions, such as
account opening activities and general customer inquiries. AI is
leveraged at call centers to process and triage customer calls to
provide customized service. These technologies are also used to better
target marketing and customize trade recommendations.
---------------------------------------------------------------------------
\3\ ``Natural language processing'' generally refers to the use
of computers to understand or analyze natural language text or
speech.
\4\ The term ``chatbot'' generally refers to a software
application used to conduct an on-line chat conversation via text or
text-to-speech, in lieu of providing direct contact with a live
human agent.
---------------------------------------------------------------------------
Credit decisions. This involves the use of AI to inform
credit decisions in order to enhance or supplement existing techniques.
This application of AI may use traditional data or employ alternative
data \5\ (such as cash flow transactional information from a bank
account).
---------------------------------------------------------------------------
\5\ For the purposes of this RFI, alternative data means
information not typically found in the consumer's credit files of
the nationwide consumer reporting agencies or customarily provided
by consumers as part of applications for credit.
---------------------------------------------------------------------------
Risk management. AI may be used to augment risk management
and control practices. For example, an AI approach might be used to
complement and provide a check on another, more traditional credit
model. Financial institutions may also use AI to enhance credit
monitoring (including through early warning alerts), payment
collections, loan restructuring and recovery, and loss forecasting. AI
can assist internal audit and independent risk management to increase
sample size (such as for testing), evaluate risk, and refer higher-risk
issues to human analysts. AI may also be used in liquidity risk
management, for example, to enhance monitoring of market conditions or
collateral management.
Textual analysis. Textual analysis refers to the use of
NLP for handling unstructured data (generally text) and obtaining
insights from that data or improving efficiency of existing processes.
Applications include analysis of regulations, news flow, earnings
reports, consumer complaints, analyst ratings changes, and legal
documents.
Cybersecurity. AI may be used to detect threats and
malicious activity, reveal attackers, identify compromised systems, and
support threat mitigation. Examples include real-time investigation of
potential attacks, the use of behavior-based detection to collect
network metadata, flagging and blocking of new ransomware and other
malicious attacks, identifying compromised accounts and files involved
in exfiltration, and deep forensic analysis of malicious files.
Potential Benefits of AI
AI has the potential to offer improved efficiency, enhanced
performance, and cost reduction for financial institutions, as well as
benefits to consumers and businesses. AI can identify relationships
among variables that are not intuitive or not revealed by more
traditional techniques. AI can better process certain forms of
information, such as text, that may be impractical or difficult to
process using traditional techniques. AI also facilitates processing
significantly large and detailed datasets, both structured and
unstructured, by identifying patterns or correlations that would be
impracticable to ascertain otherwise.
Other potential AI benefits include more accurate, lower-cost, and
faster underwriting, as well as expanded credit access for consumers
and small businesses that may not have obtained credit under
traditional credit underwriting approaches. AI applications may also
enhance an institution's ability to provide products and services with
greater customization.
Potential Risks of AI
It is important for financial institutions to have processes in
place for identifying and managing potential risks associated with AI,
as they do for any process, tool, or model employed. Many of the
potential risks associated with using AI are not unique to AI. For
instance, the use of AI could result in operational vulnerabilities,
such as internal process or control breakdowns, cyber threats,
information technology lapses, risks associated with the use of third
parties, and model risk, all of which could affect a financial
institution's safety and soundness. The use of AI can also create or
heighten consumer protection risks, such as risks of unlawful
discrimination, unfair, deceptive, or abusive acts or practices (UDAAP)
under the Dodd-Frank Wall Street Reform and Consumer Protection Act
(Dodd-Frank Act), unfair or deceptive acts or practices (UDAP) under
the Federal Trade Commission Act (FTC Act), or privacy concerns.
AI may present particular risk management challenges to financial
institutions in the areas of explainability, data usage, and dynamic
updating.
Explainability. For the purposes of this RFI,
explainability refers to how an AI approach uses inputs to produce
outputs. Some AI approaches can exhibit a ``lack of explainability''
for their overall functioning (sometimes known as global
explainability) or how they arrive at an individual outcome in
[[Page 16840]]
a given situation (sometimes referred to as local explainability). Lack
of explainability can pose different challenges in different contexts.
Lack of explainability can also inhibit financial institution
management's understanding of the conceptual soundness \6\ of an AI
approach, which can increase uncertainty around the AI approach's
reliability, and increase risk when used in new contexts. Lack of
explainability can also inhibit independent review and audit and make
compliance with laws and regulations, including consumer protection
requirements, more challenging.
---------------------------------------------------------------------------
\6\ For this RFI, the term ``conceptual soundness'' generally
refers to the quality of the theory, design, methodology, data,
developmental testing, and confirmation that an approach is
appropriate for the intended use.
---------------------------------------------------------------------------
Broader or More Intensive Data Usage. Data plays a
particularly important role in AI. In many cases, AI algorithms
identify patterns and correlations in training data without human
context or intervention, and then use that information to generate
predictions or categorizations.\7\ Because the AI algorithm is
dependent upon the training data, an AI system generally reflects any
limitations of that dataset. As a result, as with other systems, AI may
perpetuate or even amplify bias or inaccuracies inherent in the
training data, or make incorrect predictions if that data set is
incomplete or non-representative.
---------------------------------------------------------------------------
\7\ In this context, training data are the data used to develop
and calibrate an AI approach; for example, a financial institution
might use a large dataset of past fraudulent transactions to train
the approach to detect and prevent future fraud.
---------------------------------------------------------------------------
Dynamic Updating. Some AI approaches have the capacity to
update on their own, sometimes without human interaction, often known
as dynamic updating. Monitoring and tracking an AI approach that
evolves on its own can present challenges in review and validation,
particularly when a change in external circumstances (e.g., economic
downturns and financial crises) may cause inputs to vary materially
from the original training data. Dynamic updating techniques can
produce changes that range from minor adjustments to existing elements
of a model to the introduction of entirely new elements.
Request for Comment
As discussed, the agencies recognize that AI has the potential to
offer improved efficiency, enhanced performance, and cost reduction for
financial institutions, as well as benefits to consumers and
businesses. In this RFI, the agencies are seeking information on
financial institutions' risk management practices related to the use of
AI; barriers or challenges facing financial institutions when
developing, adopting, and managing AI and its risks; and benefits to
financial institutions and their customers from the use of AI. The RFI
also solicits respondents' views on the use of AI in financial
services, which will help the agencies determine whether any
clarification would be helpful for financial institutions' use of AI in
a safe and sound manner and in compliance with applicable laws and
regulations, including those related to consumer protection.
Explainability
Understanding the conceptual soundness of any model, tool,
application, or system aids in managing its risks including those
related to lack of explainability. The importance of conceptual
soundness is described in existing agency guidance and is well
established in industry practice. For traditional approaches,
conceptual soundness is foundational both to development and
validation/independent review. In the case of certain less transparent
AI approaches, however, evaluations of conceptual soundness can be
complicated. The underlying theory and logic may be less accessible to
users than that of traditional approaches or more transparent AI
approaches. Without insight into an approach's general operating
principles, financial institution management may not be able to
evaluate with confidence how the system will function in unforeseen
circumstances. To address lack of explainability of certain AI
approaches, researchers have developed techniques to help explain
predictions or categorizations. These techniques are often referred to
as ``post-hoc'' methods, because they are used to interpret the outputs
rather than the design.
Question 1: How do financial institutions identify and manage risks
relating to AI explainability? What barriers or challenges for
explainability exist for developing, adopting, and managing AI?
Question 2: How do financial institutions use post-hoc methods to
assist in evaluating conceptual soundness? How common are these
methods? Are there limitations of these methods (whether to explain an
AI approach's overall operation or to explain a specific prediction or
categorization)? If so, please provide details on such limitations.
Question 3: For which uses of AI is lack of explainability more of
a challenge? Please describe those challenges in detail. How do
financial institutions account for and manage the varied challenges and
risks posed by different uses?
Risks From Broader or More Intensive Data Processing and Usage
Like other systems, AI is designed to interact directly with
training data to identify correlations and patterns and use that
information for prediction or categorization. This means that data
quality is important for AI. If the training data are biased or
incomplete, AI may incorporate those shortcomings into its predictions
or categorizations.
AI may use alternative datasets in certain applications (such as
credit underwriting, fraud detection, and trading) in ways that can
assist in identifying related trends or predictions that may be
difficult to identify with traditional methods. The importance of
practices such as data quality assessments to determine relevance and
suitability of data used in a model, may be heightened in the use of
AI. Finally, in many cases, AI developers process or optimize raw data
so that the data can be better used for training. Various data
processing techniques exist, some of which may affect performance.
Question 4: How do financial institutions using AI manage risks
related to data quality and data processing? How, if at all, have
control processes or automated data quality routines changed to address
the data quality needs of AI? How does risk management for alternative
data compare to that of traditional data? Are there any barriers or
challenges that data quality and data processing pose for developing,
adopting, and managing AI? If so, please provide details on those
barriers or challenges.
Question 5: Are there specific uses of AI for which alternative
data are particularly effective?
Overfitting
``Overfitting'' can occur when an algorithm ``learns'' from
idiosyncratic patterns in the training data that are not representative
of the population as a whole. Overfitting is not unique to AI, but it
can be more pronounced in AI than with traditional models. Undetected
overfitting could result in incorrect predictions or categorizations.
Question 6: How do financial institutions manage AI risks relating
to overfitting? What barriers or challenges, if any, does overfitting
pose for developing, adopting, and managing AI? How do financial
institutions develop their AI so that it will adapt to new and
potentially different populations (outside of the test and training
data)?
[[Page 16841]]
Cybersecurity Risk
Like other data-intensive technologies, AI may be exposed to risk
from a variety of criminal cybersecurity threats. For example, AI can
be vulnerable to ``data poisoning attacks,'' which attempt to corrupt
and contaminate training data to compromise the system's performance.
Question 7: Have financial institutions identified particular
cybersecurity risks or experienced such incidents with respect to AI?
If so, what practices are financial institutions using to manage
cybersecurity risks related to AI? Please describe any barriers or
challenges to the use of AI associated with cybersecurity risks. Are
there specific information security or cybersecurity controls that can
be applied to AI?
Dynamic Updating
A particular characteristic of some AI is the ability for it to
learn or evolve over time, especially as it captures new training data.
Over time, this could result in drift (i.e., the AI approach could
change) as it learns from the new data. This can present challenges for
validating, monitoring, tracking, and documenting the AI approach,
including for persons conducting an independent review. It may be
important to understand whether an AI approach that was independently
reviewed initially has significantly evolved over time (e.g., using an
influx of new data). Dynamic updating can also affect how results are
tracked over time. For example, initial performance thresholds chosen
to monitor the approach could become less meaningful if the AI approach
has significantly changed to focus on different target outcomes.
Similar risks can arise with AI approaches that are not updated as
their context evolves, since they are more closely tuned to their
training data. For example, AI approaches that are validated in one
circumstance may not perform well in another, and an independent review
conducted in a previous context may no longer be accurate in new
circumstances.
Question 8: How do financial institutions manage AI risks relating
to dynamic updating? Describe any barriers or challenges that may
impede the use of AI that involve dynamic updating. How do financial
institutions gain an understanding of whether AI approaches producing
different outputs over time based on the same inputs are operating as
intended?
AI Use by Community Institutions
A financial institution's AI strategy, use of AI, and associated
risk management practices could vary substantially based on the
financial institution's size, complexity of operations, business model,
staffing, and risk profile, and this could affect the corresponding
risks that arise. Community institutions may be more likely to use
third-party AI approaches or rely on third-party services that use AI.
This may pose different challenges (e.g., level of expertise of AI or
insight into the third-party AI approach) in a financial institution's
adoption of AI.
Question 9: Do community institutions face particular challenges in
developing, adopting, and using AI? If so, please provide detail about
such challenges. What practices are employed to address those
impediments or challenges?
Oversight of Third Parties
Financial institutions may opt to use AI developed by third
parties, rather than develop the approach internally. Existing agency
guidance (as noted in the Appendix) describes information and risks
that may be relevant to financial institutions when selecting third-
party approaches (including ones using AI) and sets out principles for
the validation of such third-party approaches.
Question 10: Please describe any particular challenges or
impediments financial institutions face in using AI developed or
provided by third parties and a description of how financial
institutions manage the associated risks. Please provide detail on any
challenges or impediments. How do those challenges or impediments vary
by financial institution size and complexity?
Fair Lending
Depending on the specific use, there may be uncertainty about how
less transparent and explainable AI approaches align with applicable
consumer protection legal and regulatory frameworks, which often
address fairness and transparency. For example, it may be challenging
to verify that a less transparent and explainable approach comports
with fair lending laws.
Question 11: What techniques are available to facilitate or
evaluate the compliance of AI-based credit determination approaches
with fair lending laws or mitigate risks of non-compliance? Please
explain these techniques and their objectives, limitations of those
techniques, and how those techniques relate to fair lending legal
requirements.
Question 12: What are the risks that AI can be biased and/or result
in discrimination on prohibited bases? Are there effective ways to
reduce risk of discrimination, whether during development, validation,
revision, and/or use? What are some of the barriers to or limitations
of those methods?
Question 13: To what extent do model risk management principles and
practices aid or inhibit evaluations of AI-based credit determination
approaches for compliance with fair lending laws?
Question 14: As part of their compliance management systems,
financial institutions may conduct fair lending risk assessments by
using models designed to evaluate fair lending risks (``fair lending
risk assessment models''). What challenges, if any, do financial
institutions face when applying internal model risk management
principles and practices to the development, validation, or use of fair
lending risk assessment models based on AI?
Question 15: The Equal Credit Opportunity Act (ECOA), which is
implemented by Regulation B, requires creditors to notify an applicant
of the principal reasons for taking adverse action for credit or to
provide an applicant a disclosure of the right to request those
reasons. What approaches can be used to identify the reasons for taking
adverse action on a credit application, when AI is employed? Does
Regulation B provide sufficient clarity for the statement of reasons
for adverse action when AI is used? If not, please describe in detail
any opportunities for clarity.
Additional Considerations
Question 16: To the extent not already discussed, please identify
any additional uses of AI by financial institutions and any risk
management challenges or other factors that may impede adoption and use
of AI.
Question 17: To the extent not already discussed, please identify
any benefits or risks to financial institutions' customers or
prospective customers from the use of AI by those financial
institutions. Please provide any suggestions on how to maximize
benefits or address any identified risks.
Appendix: Laws, Regulations, Supervisory Guidance, and Other Agency
Statements Relevant to AI
This Appendix contains a list of laws, regulations, supervisory
guidance, and other statements issued by the agencies that may be
relevant to AI. This includes existing laws and regulations relating
to safety and soundness and consumer protection. The items below do
not constitute an exhaustive list; other laws, regulations,
guidance, and statements may be relevant based on the
[[Page 16842]]
particular facts and circumstances. Some laws and regulations are
applicable to any process or tool a financial institution employs,
regardless of whether a financial institution utilizes AI or how.
Laws and Regulations
Section 39 of the Federal Deposit Insurance Act as
implemented through the agencies' safety and soundness regulations
\8\
---------------------------------------------------------------------------
\8\ Refer to the Interagency Guidelines Establishing Standards
for Safety and Soundness, 12 CFR 364, Appendix A (FDIC); 12 CFR 263
(FRB); 12 CFR 30, appendix A (OCC).
---------------------------------------------------------------------------
Sections 501 and 505(b) of Gramm-Leach-Bliley Act as
implemented through the agencies' regulations and standards,
including Interagency Guidelines Establishing Information Security
Standards \9\
---------------------------------------------------------------------------
\9\ Refer to the Interagency Guidelines Establishing Information
Security Standards, 12 CFR 364, Appendix B (FDIC); 12 CFR 208,
Appendix D-2 and 12 CFR 225, Appendix F (FRB); 12 CFR 30, appendix B
(OCC); Guidelines for Safeguarding Member Information, 12 CFR 748,
Appendix A (NCUA).
---------------------------------------------------------------------------
Fair Credit Reporting Act (FCRA)/Reg. V
Equal Credit Opportunity Act (ECOA)/Reg. B
Fair Housing Act (FHA)
Section 5 of the Federal Trade Commission Act (prohibiting
UDAP) and sections 1031 and 1036 of the Dodd-Frank Act (prohibiting
unfair, deceptive, or abusive acts or practices (UDAAP))
Supervisory Guidance and Statements
Interagency Statement on the Use of Alternative Data in
Credit Underwriting \10\
---------------------------------------------------------------------------
\10\ Refer to FDIC FIL-82-2019, https://www.fdic.gov/news/financial-institution-letters/2019/fil19082.html; Federal Reserve CA
Letter 19-11, https://www.federalreserve.gov/supervisionreg/caletters/caletters.htm; and OCC Bulletin 2019-62, https://www.occ.gov/news-issuances/bulletins/2019/bulletin-2019-62.html.
---------------------------------------------------------------------------
Supervisory Guidance on Model Risk Management \11\
---------------------------------------------------------------------------
\11\ Refer to the ``Supervisory Guidance on Model Risk
Management,'' Federal Reserve SR Letter 11-7, https://www.federalreserve.gov/supervisionreg/srletters/srletters.htm; OCC
Bulletin 2011-12, https://www.occ.gov/news-issuances/bulletins/2011/bulletin-2011-12.html; and FDIC Financial Institution Letter (FIL)-
22-2017, https://www.fdic.gov/news/financial-institution-letters/2017/fil17022.html.
---------------------------------------------------------------------------
Third-Party/Outsourcing Risk Management \12\
---------------------------------------------------------------------------
\12\ FDIC: Guidance for Managing Third-Party Risk (FIL)-44-2008,
https://www.fdic.gov/news/financial-institution-letters/2008/fil08044.html; OCC Bulletin 2013-29, OCC Bulletin 2020-10; NCUA:
Evaluating Third Party Relationships, Supervisory Letter (SL) 07-01
(Oct. 2007); and FRB: Guidance on Outsourcing Risk (SR 13-19),
https://www.federalreserve.gov/supervisionreg/srletters/srletters.htm.
---------------------------------------------------------------------------
New, Modified, or Expanded Bank Products and Services \13\
---------------------------------------------------------------------------
\13\ OCC Bulletin 2017-43, https://www.occ.treas.gov/news-issuances/bulletins/2017/bulletin-2017-43.html; and NCUA 19-CU-04
(Dec. 2019), https://www.ncua.gov/regulation-supervision/letters-credit-unions-other-guidance/use-alternative-data-credit-underwriting.
---------------------------------------------------------------------------
CFPB Innovation Spotlight on Providing Adverse Action
Notices When Using AI/ML Models \14\
---------------------------------------------------------------------------
\14\ Patrice Alexander Ficklin, Tom Pahl, and Paul Watkins, CFPB
Blog, Innovation spotlight: Providing adverse action notices when
using AI/ML models (July 7, 2020), available at https://www.consumerfinance.gov/about-us/blog/innovation-spotlight-providing-adverse-action-notices-when-using-ai-ml-models/.
---------------------------------------------------------------------------
Examination Manuals/Procedures/Other Resources
Federal Financial Institutions Examination Council
Information Technology Examination Handbook \15\
---------------------------------------------------------------------------
\15\ FFIEC IT Handbook, https://ithandbook.ffiec.gov/.
---------------------------------------------------------------------------
Consumer Compliance Manuals and Booklets \16\
---------------------------------------------------------------------------
\16\ OCC Consumer Compliance series of Comptroller's Handbook
booklets, https://www.occ.treas.gov/topics/supervision-and-examination/consumer-compliance/index-consumer-compliance.html;
NCUA: Evaluating Compliance Risk--Updated Compliance Indicators, SL-
17-01 (March 2017), https://www.ncua.gov/regulation-supervision/letters-credit-unions-other-guidance/use-alternative-data-credit-underwriting.
---------------------------------------------------------------------------
Interagency Fair Lending Examination Procedures \17\
---------------------------------------------------------------------------
\17\ Interagency Fair Lending Examination Procedures, https://www.ffiec.gov/PDF/fairlend.pdf.
---------------------------------------------------------------------------
CFPB Examination Procedures, ECOA Baseline Review Module 5:
Fair Lending Risks Related to Models \18\
---------------------------------------------------------------------------
\18\ See, CFPB ECOA Baseline Review, p. 24, https://files.consumerfinance.gov/f/documents/cfpb_supervision-and-examination-manual_ecoa-baseline-exam-procedures_2019-04.pdf.
Blake J. Paulson,
Acting Comptroller of the Currency.
By order of the Board of Governors of the Federal Reserve
System.
Ann Misback,
Secretary of the Board.
Federal Deposit Insurance Corporation.
Dated at Washington, DC, on or about February 25, 2021.
James P. Sheesley,
Assistant Executive Secretary.
David Uejio,
Acting Director, Bureau of Consumer Financial Protection.
Melane Conyers-Ausbrooks,
Secretary of the Board, National Credit Union Administration.
[FR Doc. 2021-06607 Filed 3-30-21; 8:45 am]
BILLING CODE 4810-33-P; 6210-01-P; 4810-AM-P; 6714-01-P
