With the growing use of artificial intelligence (AI) in
financial markets, broker-dealers and investment advisers need to
pay attention to the risks posed by AI on firms’ compliance
with federal securities laws. While machine learning is
increasingly integrated into today’s financial ecosystem
including call centers, compliance systems, robo-advisers, and
algorithmic trading, the use of AI systems, and generative AI
systems specifically, are developing rapidly.
The combination of AI’s development and integration into our
financial markets presents a variety of unique opportunities and
challenges for market participants and regulators. With regulatory
interest in AI high, broker-dealers and investment advisers should
examine whether their existing compliance programs address
AI-related risks adequately.
What is Artificial Intelligence?
With so much coverage of AI in 2023, it may seem strange to ask:
What is AI? AI is an amorphous term and has been around since the
mid-20th century. At its core, AI is a machine/computer that mimics
human intelligence and cognition. Most of us are familiar with the
“expert system” form of AI that was introduced in the
1980s. Expert systems are programs that solve problems or answer
questions by relying on a system of rules coded into the program by
experts. While these systems emulate human decision-making ability,
they are confined by the limits of their programmed knowledge base.
Expert systems are used in making medical diagnoses, computer
gaming, accounting software, and more, proving useful in performing
specialized tasks but not suited to adaptive problem solving.
Machine and deep learning are more recent forms of AI. Machine
learning is AI in which the machine solves certain problems by
itself, with little or no the input of human-developed algorithms.
This has an obvious advantage over expert systems because machine
learning is not dependent on a programmed knowledge base, and it
does not require the laborious development of algorithms by human
programmers. Machine learning-based AI, however, may require human
intervention to learn and differentiate between data
inputs—like how a website may prompt you to choose a bike
from a series of photos to verify you are human. These models
follow three primary forms of learning: supervised, unsupervised,
or reinforcement. Under the supervised learning model, human
operators train the model with pre-classified datasets to help it
learn how to classify data or predict outcomes. Under the
unsupervised learning model, the machine will analyze unclassified
datasets to discover patterns or outcomes without human
intervention. Finally, in reinforcement learning, the machine is
trained through trial-and-error by seeking a pre-determined action
or outcome.
Deep learning is a more advanced version of machine learning.
Deep learning relies on neural networks, comprised of node layers,
which emulate neurons sending signals in a brain. The nodes have at
least three layers: an input layer, one or more hidden layers, and
an output layer. The nodes can receive data, analyze it, and
formulate an output. In this way, deep learning AI can answer more
complex questions than prior forms of AI. Deep learning can digest
raw data without the need for human intervention to help it
differentiate between data inputs. This gives deep learning-based
AI an advantage over machine learning, especially when working with
large datasets. Users can also combine deep learning with
reinforcement learning to analyze large datasets and optimize
outcomes from that large data. As AI improves, so-called deep
reinforcement learning systems can more closely emulate the
reward-seeking behaviors of the human brain. This promises both
exciting and frightening possibilities for regulated entities and
the financial markets.
Current AI technology is limited to artificial narrow
intelligence (ANI), which is designed to perform a single or narrow
set of tasks. Although we have witnessed tremendous progress in
machine and deep learning, AI is still limited to performing just
certain tasks with increased proficiency. Take, for example,
OpenAI’s ChatGPT. ChatGPT is a generative multimodal model
limited to producing natural language responses. And, as lawyers
should know, ChatGPT can occasionally produce unreliable
results.
Researchers like OpenAI are working towards the development of
true artificial general intelligence, or systems that are designed
to accomplish any intellectual task that a human can perform.
Beyond this, AI may eventually surpass human intelligence and
achieve artificial superintelligence. The generative AI models of
today will likely look like a primitive AIM chatbot in 20
years.
Artificial Intelligence in Financial Markets
Broker-dealers and investment advisers have long used AI tools
in the financial markets. AI-based applications have proliferated
for uses such as operational functions, compliance functions,
administrative functions, customer outreach, or portfolio
management. Chatbots, for example, provide efficient and easily
accessible assistance to clients, and robo-advisers analyze markets
and provide investment recommendations to investors.
Trading models are another popular area where firms deploy AI
technology in financial markets. Quantitative traders have used
algorithmic models to identify investments or trade securities
since the 1970s. Increasingly, however, broker-dealers and
investment advisers utilize more advanced machine learning for
these purposes. Several firms have introduced AI-powered
robo-advisers to investors. For instance, JPMorgan has publicly
announced its development of “IndexGPT,” an AI advisor to
analyze and select securities for individual investors’
portfolios.
Risks of Artificial Intelligence
Recent advances in AI raise novel risks for broker-dealers and
investment advisers. These risks include, but are not limited to,
the following:
- Conflicts of Interest. The risk for identified
or new conflicts of interest might increase with the use of AI,
especially with the use of robo-advisors or chatbots. AI programs
could make investment recommendations to investors that are more
profitable to the firm. Firms may not even understand how or why AI
programs are making these recommendations. With the sophistication
of modern AI programs, users or programmers may not fully
understand the decision-making process of the AI. The risk for a
conflicted output rises when the determination of a recommendation
is not readily transparent and explainable. - Market Manipulation. AI trading programs
powered by machine and deep learning may learn how to manipulate
markets. An AI program designed to achieve profits without
limitations or faulty limitations may learn how to manipulate
markets, for example, by “spoofing” the market or
executing “wash sales.” Trading algorithms already caused
the “flash crash” in 2010. It is not far-fetched for AI
to start manipulating markets in the near future. - Deception. Generative AI is already notorious
for being used to create “deepfakes,” which are realistic
images, videos, or audio based on real people. Bad actors can
utilize these digital forgeries to cause havoc in the financial
markets by imitating market leaders and delivering fake news, for
example. Or deepfakes can target specific persons by imitating
superiors or customers to seemingly authorize actions regarding a
customer’s account. - Fraud. As AI becomes more integrated into
firms’ investment recommendations and trading decisions, there
is a greater risk that bad actors will use confidential customer
trading data for their own ends. For example, bad actors might
build a proprietary AI trading program that uses customer trading
data to front-run or trade ahead of potentially market moving
trades. - Data Privacy. AI programs have access to wide
swaths of data, including, potentially, personal customer data. AI
systems analyze this personal data to learn and/or make decisions
or determine outcomes. The collection and analysis of large swaths
of personal data raises concerns about how that data is used and
who has access to the data. - Discrimination. The possibility for unfair
treatment, bias, and discrimination is ripe when AI learns from
human data. When learning from data saturated with historical
biases or racism, AI is likely to pick up its own biases. This
phenomenon is already well documented. A 2018 study, for instance,
found facial recognition programs performed poorly on people of
color.1 From chatbot interactions, hiring decisions, or
investment recommendations, an AI’s own bias could cloud its
judgment and skew outcomes in unlawful or undesirable ways.
Safeguarding AI-Related Risks
Broker-dealers and investment advisers are subject to a variety
of regulations implicated by the use of AI. Broker-dealers’ and
investment advisers’ advice and recommendations must be in the
best interests of their clients and they cannot place their own
interests ahead of investors’ interests.2
Broker-dealers and investment advisers also have overarching
obligations to adopt and implement written policies and procedures
reasonably designed to prevent violations of the federal securities
laws.3 Broker-dealers and investment advisers also have
an obligation to safeguard customer records and data.4
Further, federal securities laws prohibit fraudulent conduct by
broker-dealers and investment advisers.
SEC Actions
The U.S. Securities and Exchange Commission (SEC) has already
taken note of the risks posed by AI. On July 26, 2023, the SEC
proposed new rules to address risks that AI utilizing predictive
data analytics will place a firm’s interests ahead of
investors’ interests.5 Under the SEC’s proposed
rule, broker-dealers and investment advisors would have to evaluate
any use or reasonably foreseeable potential use of “covered
technologies” in any investor interaction, identify conflicts
of interest where the use of the covered technology would place the
firm’s interests ahead of investors’, and eliminate or
neutralize the effect of those conflicts. The SEC defined a covered
technology as, “an analytical, technological, or computational
function, algorithm, model, correlation matrix, or similar method
or process that optimizes for, predicts, guides, forecasts, or
directs investment-related behaviors or outcomes.”6
This covers a wide swath of AI technologies already in use, such as
tools that analyze investor behavior to provide curated research
reports or AI that provides tailored investment
recommendations.
The SEC’s Division of Examinations is not waiting for the
finalization of this proposed rule. As discussed in its 2024
Examination Priorities, this SEC Division has already established a
specialized team to address emerging issues and risks in this area.
The 2024 Examination Priorities specifically advise SEC
registrants, such as broker-dealers and investment advisers, as
follows:
We also established specialized teams within our different
examination programs, allowing us to better address emerging issues
and risks associated with crypto assets, financial technology, such
as artificial intelligence, and
cybersecurity, among others. Finally, we continued to strengthen
our leadership team by bringing onboard a number of key senior and
advisory positions and building additional capacity in various
examination programs to keep pace with the rapidly developing
market ecosystem consistent with Congress’ fiscal year 2023
appropriation. (Emphasis added.)7
The Division of Examinations continued:
The Division remains focused on certain services, including
automated investment tools, artificial intelligence, and trading
algorithms or platforms, and the risks associated with the use of
emerging technologies and alternative sources of data.
SEC Chair Gary Gensler has pursued aggressive examination,
enforcement, and regulatory agendas. By all indications, he intends
to treat the intersection of AI and federal securities laws no
differently. His speech nine days before the proposed rule was
released revealed that he is as well-versed in this area (and in
technology generally) as any leader of a U.S. financial
regulator.8
Looking Forward
Given the current and proposed regulatory framework, it is vital
for broker-dealers and investment advisers to have a firm
understanding of the AI tools they use and then implement
appropriate policies and procedures for those AI tools. Firms
should not wait to assess their use of AI, including future use,
and put guardrails in place to ensure customers are protected and
the firms satisfy all regulatory expectations.
Firms should begin by assessing what AI technology they are
actually using or plan to use. After this is complete, assessing
whether such use presents any conflicts of interest, potential
customer harm, or violation of applicable rules and regulations is
recommended. Firms should also consider keeping an inventory of all
the AI applications they use, the risks posed by each AI
application, and mitigating controls to address each AI-related
risk.
Next, firms should implement and periodically review their
written policies and procedures to address AI governance and the
regulatory risks posed by AI. Any existing policies and procedures
may be similarly enhanced to address conflicts of interest related
to AI, potential customer harm, and potential regulatory
violations. For example, firms may determine to be deliberate and
intentional about their use of any new AI systems, explicitly
requiring review and assessment of such AI before personnel are
permitted to use it. Further, supervision by cross-function teams
and periodic testing is also helpful to understand how the AI
systems are performing.
Firms should also consider reviewing their contracts with
customers to assess whether the firm or its vendors have the
requisite rights to use or share the data. Separately, firms may
also want to evaluate their contracts with their vendors to see
what protections the firm has with respect to the vendor’s use
of the data shared by the firm as well as the services received
from the vendor.
For robo-advisers and AI tools that provide investment
recommendations or advice, firms should pay particular attention to
the “explainability” of the AI’s recommendations. As
AI becomes more advanced, the decision-making process may
subsequently become more opaque. As evidenced by the Biden
Administration’s recent Executive Order on the Safe, Secure, and
Trustworthy Development and Use of Artificial Intelligence,
auditability, transparency, and explainability of AI systems
(whether or not developed by the user) are all critical aspects of
appropriate AI development and use.9 Accordingly, it is
important to try to understand the AI decision-making process and
implement appropriate guardrails where needed. This could include
periodic testing of robo-advisers, human oversight of
recommendations, or limitations on the recommendations. It is
similarly important for firms to ensure that AI technology is not
placing the firm’s interest ahead of investors’ interests.
Testing and review will help ensure that broker-dealers and
investment advisers maintain compliance with federal securities
laws and stave off risks of significant examination findings,
referrals to the SEC’s Division of Enforcement, costly
litigation, and the corresponding reputational damage to firms and
firms’ stakeholders.
Firms should also place importance on safeguarding and
monitoring how AI systems use customer data by adopting policies
and procedures to ensure that AI tools do not misappropriate
customer data for the firms’ own use in trading and restrict
who has access to the customer data. Organizations should also
consider updating their written policies and procedures to reflect
what customer data they collect, how that data is used, how the
data is shared, and whether appropriate customer consent has been
obtained. Finally, regardless of AI use, firms must safeguard
against cybersecurity breaches.
The growth of AI in the financial markets will lead to more
attention from regulators regarding the use of AI. Accordingly,
broker-dealers and investment advisers should begin to assess their
use of AI, including future use, and put in guardrails to ensure
that their customers are protected.
Footnotes
1. See Buolamwini, J. and Timnit Gebru, T.
“Gender Shades: Intersectional Accuracy Disparities
in Commercial Gender Classification,” Proceedings of
Machine Learning Research, 2018.
2. See Regulation Best Interest: The
Broker-Dealer Standard of Conduct, Exchange Act Release No. 86031,
84 FR 33318 (June 5, 2019); Commission Interpretation Regarding
Standard of Conduct for Investment Advisers, Investment Advisers
Act Release No. 5248, 84 FR 33669 (June 5, 2019).
3. See Compliance Programs of Investment
Companies and Investment Advisers, Release No. IA-2204, 68 FR 74713
(Dec. 24, 2003); FINRA Rule 3110.
4. See Privacy of Consumer Financial Information
(Regulation S-P), Exchange Act Release No. 42974, 65 FR 40333 (June
22, 2000).
5. Proposed Rule, Conflicts of Interest Associated with
the Use of Predictive Data Analytics by Broker-Dealers and
Investment Advisers, Exchange Act Release No. 97990 (July 26,
2023).
6. Id. at 42.
7. See “2024 Examination Priorities.” U.S.
Securities and Exchange Commission, Division of Examinations,
15 October 2023.
8. See Gensler, G. “Isaac Newton to AI” Remarks before the
National Press Club.” U.S. Securities and Exchange
Commission, 17 July 2023.
9. Exec. Order No. 14,110, 88 Fed. Reg. 75,191 (Oct. 30,
2023).
The content of this article is intended to provide a general
guide to the subject matter. Specialist advice should be sought
about your specific circumstances.
