[House Hearing, 116 Congress]
[From the U.S. Government Publishing Office]
ROBOTS ON WALL STREET: THE IMPACT
OF AI ON CAPITAL MARKETS AND JOBS
IN THE FINANCIAL SERVICES INDUSTRY
=======================================================================
HEARING
BEFORE THE
TASK FORCE ON ARTIFICIAL INTELLIGENCE
OF THE
COMMITTEE ON FINANCIAL SERVICES
U.S. HOUSE OF REPRESENTATIVES
ONE HUNDRED SIXTEENTH CONGRESS
FIRST SESSION
__________
DECEMBER 6, 2019
__________
Printed for the use of the Committee on Financial Services
Serial No. 116-73
[GRAPHIC(S) NOT AVAILABLE IN TIFF FORMAT]
______
U.S. GOVERNMENT PUBLISHING OFFICE
42-632 PDF WASHINGTON : 2020
HOUSE COMMITTEE ON FINANCIAL SERVICES
MAXINE WATERS, California, Chairwoman
CAROLYN B. MALONEY, New York PATRICK McHENRY, North Carolina,
NYDIA M. VELAZQUEZ, New York Ranking Member
BRAD SHERMAN, California ANN WAGNER, Missouri
GREGORY W. MEEKS, New York PETER T. KING, New York
WM. LACY CLAY, Missouri FRANK D. LUCAS, Oklahoma
DAVID SCOTT, Georgia BILL POSEY, Florida
AL GREEN, Texas BLAINE LUETKEMEYER, Missouri
EMANUEL CLEAVER, Missouri BILL HUIZENGA, Michigan
ED PERLMUTTER, Colorado SEAN P. DUFFY, Wisconsin
JIM A. HIMES, Connecticut STEVE STIVERS, Ohio
BILL FOSTER, Illinois ANDY BARR, Kentucky
JOYCE BEATTY, Ohio SCOTT TIPTON, Colorado
DENNY HECK, Washington ROGER WILLIAMS, Texas
JUAN VARGAS, California FRENCH HILL, Arkansas
JOSH GOTTHEIMER, New Jersey TOM EMMER, Minnesota
VICENTE GONZALEZ, Texas LEE M. ZELDIN, New York
AL LAWSON, Florida BARRY LOUDERMILK, Georgia
MICHAEL SAN NICOLAS, Guam ALEXANDER X. MOONEY, West Virginia
RASHIDA TLAIB, Michigan WARREN DAVIDSON, Ohio
KATIE PORTER, California TED BUDD, North Carolina
CINDY AXNE, Iowa DAVID KUSTOFF, Tennessee
SEAN CASTEN, Illinois TREY HOLLINGSWORTH, Indiana
AYANNA PRESSLEY, Massachusetts ANTHONY GONZALEZ, Ohio
BEN McADAMS, Utah JOHN ROSE, Tennessee
ALEXANDRIA OCASIO-CORTEZ, New York BRYAN STEIL, Wisconsin
JENNIFER WEXTON, Virginia LANCE GOODEN, Texas
STEPHEN F. LYNCH, Massachusetts DENVER RIGGLEMAN, Virginia
TULSI GABBARD, Hawaii WILLIAM TIMMONS, South Carolina
ALMA ADAMS, North Carolina
MADELEINE DEAN, Pennsylvania
JESUS ``CHUY'' GARCIA, Illinois
SYLVIA GARCIA, Texas
DEAN PHILLIPS, Minnesota
Charla Ouertatani, Staff Director
TASK FORCE ON ARTIFICIAL INTELLIGENCE
BILL FOSTER, Illinois, Chairman
EMANUEL CLEAVER, Missouri BARRY LOUDERMILK, Georgia, Ranking
KATIE PORTER, California Member
SEAN CASTEN, Illinois TED BUDD, North Carolina
ALMA ADAMS, North Carolina TREY HOLLINGSWORTH, Indiana
SYLVIA GARCIA, Texas ANTHONY GONZALEZ, Ohio
DEAN PHILLIPS, Minnesota DENVER RIGGLEMAN, Virginia
C O N T E N T S
----------
Page
Hearing held on:
December 6, 2019............................................. 1
Appendix:
December 6, 2019............................................. 33
WITNESSES
Friday, December 6, 2019
Fender, Rebecca, CFA, Senior Director, Future of Finance
Initiative, Chartered Financial Analyst (CFA) Institute........ 8
Lopez de Prado, Marcos, Professor of Practice, Engineering
School, Cornell University, and Chief Investment Officer, True
Positive Technologies.......................................... 6
McIlwain, Charlton, Vice Provost, Faculty Engagement and
Development, and Professor of Media, Culture, and
Communication, New York University (NYU)....................... 4
Rejsjo, Martina, Vice President, Nasdaq MarketWatch.............. 11
Wegner, Kirsten, Chief Executive Officer, Modern Markets
Initiative (MMI)............................................... 10
APPENDIX
Prepared statements:
Fender, Rebecca.............................................. 34
Lopez de Prado, Marcos....................................... 53
McIlwain, Charlton........................................... 66
Rejsjo, Martina.............................................. 80
Wegner, Kirsten.............................................. 89
Additional Material Submitted for the Record
Foster, Hon. Bill:
Written statement of Public Citizen.......................... 98
ROBOTS ON WALL STREET:
THE IMPACT OF AI ON
CAPITAL MARKETS AND
JOBS IN THE FINANCIAL
SERVICES INDUSTRY
----------
Friday, December 6, 2019
U.S. House of Representatives,
Task Force on Artificial Intelligence,
Committee on Financial Services,
Washington, D.C.
The task force met, pursuant to notice, at 9:42 a.m., in
room 2128, Rayburn House Office Building, Hon. Bill Foster
[chairman of the task force] presiding.
Members present: Representatives Foster, Cleaver, Casten,
Adams, Garcia of Texas; Loudermilk, Hollingsworth, and
Riggleman.
Also present: Representative Himes.
Chairman Foster. The Task Force on Artificial Intelligence
will now come to order.
Without objection, the Chair is authorized to declare a
recess of the task force at any time. Also, without objection,
members of the full Financial Services Committee who are not
members of this task force are authorized to participate in
today's hearing.
Today's hearing is entitled, ``Robots on Wall Street: The
Impact of AI on Capital Markets and Jobs in the Financial
Services Industry.''
The Chair will now recognize himself for 5 minutes for an
opening statement.
First off, thank you all for joining us today for what
should be a very interesting hearing of this task force. Today,
we are looking at exploring how artificial intelligence (AI) is
being deployed in capital markets, from automated trading, to
portfolio allocation, to investment management decisions.
We are also going to consider how the use of this
technology is changing the nature of work in financial
services, rendering some jobs obsolete and changing the skill
sets needed to excel in others.
It would not be much of an exaggeration today to say that
Wall Street, quite literally, is run by computers. Long gone
are the days where traders would be screaming orders on the
floor of the New York Stock Exchange and financial analysts
would use TI calculators and pore over the ticker tape and
financial statements to glean insights into a company's value.
I actually hear about those days from the limo driver who
takes me back, who used to be a floor trader on the Merc.
Today, trades are automated and orders are executed in
milliseconds or microseconds. Passive exchange-traded funds
(ETFs) have proliferated, relying on algorithmic models to
ensure the fund's holdings of shares are properly weighted to
whatever index or benchmark it is tracking. Quantitative hedge
funds, or quant funds, use algorithms that scour all sorts of
market data to find the stocks that have the most price
momentum or the highest dividends or look for correlations in
the market and in the external data feeds to provide the most
value for investors.
And I think it is very notable that a lot of the shakeup
that we are seeing in those markets is really a reflection of,
sort of, the winner-take-all nature of digital economies--that
any digital business, purely digital business is a natural
monopoly, and as more of finance becomes digitized, you are
going to see more and more of the rewards go to a smaller and
smaller number of dominant players. And I would like to
emphasize, that doesn't mean they are evil; it is just simply a
natural reflection of the nature of the digital marketplace.
Other asset managers may use algorithms to perform complex
research and analysis in real-time on big data sets. This could
include scouring of social media sites, satellite information,
internet traffic, online transactions, and just about anything
else you can think of. This is, I guess, good in terms of
having the market reflect all known data, but there are abusive
corners. For example, imagine what it would be worth if you had
a 10-second early look at Trump's Twitter feed, how much money
you could make trading off that, for example.
The three types of computer-managed funds--index funds,
ETFs, and quant funds--make up about 35 percent of the
approximately $31 trillion American public equities market.
Human managers, such as traditional hedge funds and other
mutual funds, manage just 24 percent of the market.
The rise of the so-called computerization of our stock
markets has a number of benefits. The costs of executing trades
has gone way down, sometimes to zero dollars, and there is more
liquidity in the market. Passive funds charge less than 1
percent of assets under management each year, while active
managers often charge 20 times that much.
It certainly creates additional questions as well, however,
as in the 2010 flash crash. And the more recent mini flash
crashes have shown algorithmic trading can sometimes cause
unpredictable consequences that create market volatility. It
can also exacerbate information asymmetry between different
types of investors, as firms with more and faster access to
enormous data sets are able to obtain a competitive advantage.
Another broader question is how these developments are
impacting the nature of jobs in the financial services
industry. A recent Wells Fargo research report estimated that
technological efficiencies would result in about 200,000 job
cuts over the next decade in the U.S. banking industry. While
these cuts will certainly affect back-office, call-center, and
customer-service positions, the pain will be widespread. Many
front-office workers, such as bankers, traders, and financial
analysts, could also see their head count drop by almost a
third, according to a McKinsey & Company report released
earlier this year.
The report also found that 40 percent of existing jobs at
financial firms could be automated with current technology. So,
if you spend your whole day staring at a big screen, and
particularly if you are receiving a large paycheck, your job
will be at risk.
Understanding the skills that will be needed to excel in
the financial services industry of tomorrow and how we can
encourage these skills is one of the issues that we must tackle
head-on and tackle early. In a world where many functions can
be done by automated AI models, what role does that leave for
humans?
So, I very much look forward to hearing from our witnesses
on these issues.
With that, I would like to recognize the ranking member of
the task force, my friend from Georgia, Mr. Loudermilk, for 5
minutes.
Mr. Loudermilk. Thank you, Mr. Chairman.
And I want to thank each of our witnesses who are here
today. Thank you for taking the time to be here to discuss this
issue. While the rest of America is fixated on other things
going on here, this is something that may not resonate on the
major networks, but it is something that is very important, and
has an impact on our lives, positively but also potentially
negatively, and it is important that we look into this.
And, as you know, today, the task force will examine the
intersection between technology and the capital markets. In
recent years, there have been many technological developments,
including the adoption of artificial intelligence and
automation, that have redefined and reshaped trading and
investing.
The first trades on the New York Stock Exchange were made
in the late 1700s using a manual, paper-intensive process. For
many years, buyers and sellers communicated about orders over
the phone. Today, trading and investing are done on digital
platforms, and investors can trade securities from virtually
anywhere in the world using modern technology.
Electronic trading has benefited the markets in many ways.
It has been positive for investors by leading to lower overhead
and transaction costs, which has contributed to record
investment returns over the last decade.
Several major asset management firms now offer zero-percent
commissions, which means investors can buy and sell stocks
essentially for free and can capture more of the growth of
their investments. This would not be possible without
electronic trading.
Digital trading platforms also provide investors with
access to low-cost financial research and advice 24 hours a day
using robo-advisors.
Electronic trading also makes markets more efficient by
allowing faster searches for prices, better processing of large
sets of data, and more transparent price information. The
proliferation of technology can also lower firms' barriers to
entry, foster more competition, improve risk management, and
increase market access for investors.
In addition to these core benefits, there are many other
cases of companies using AI to improve efficiencies in the
capital markets in unique ways. For example, some clearing
companies are using AI to optimize the settlement of trades and
enhance cybersecurity and fraud detection. Some self-regulatory
organizations are also using AI in regtech and market
surveillance.
While there are many benefits to electronic trading, it can
also present new challenges.
One challenge, which is at the forefront of our discussion
today, is the disruption of the job market. While the rise of
automated trading has displaced many floor traders, job
opportunities in fields like code writing, cloud management,
telecommunications, fiber optics, and data analysis are
growing.
There is some concern that high-frequency trading can
contribute to volatility, but new evidence suggests that high-
frequency trading does not increase volatility and can actually
improve liquidity. There is also some concern that firms that
don't have the latest technology could be competed out of the
markets.
It is important to keep in mind that not all types of
electronic trading are the same, and I look forward to learning
more from the witnesses about the differences between automated
trading, algorithmic trading, high-frequency trading, and
computer trading.
Finally, I look forward to exploring the legislative and
regulatory issues in this space. One issue that I think needs
to be addressed is the protection of source code, because
algorithms are traders' core intellectual property. They must
be protected.
We passed a bill out of this committee and the House on a
bipartisan basis last Congress to ensure that the Securities
and Exchange Commission issues a subpoena before obtaining
these algorithms, rather than getting them through routine
exams. Mr. Chairman, I hope that we will be able to work
together on a bill this Congress.
I thank you, and I yield back.
Chairman Foster. Thank you.
And, today, we are welcoming the testimony of Dr. Charlton
McIlwain, vice provost for faculty engagement and development,
and professor of media, culture, and communication at NYU; Dr.
Marcos Lopez de Prado, professor of practice at the Engineering
School, Cornell University, and chief investment officer of
True Positive Technologies; Ms. Rebecca Fender, CFA, senior
director, Future of Finance Initiative at the Chartered
Financial Analyst Institute; Ms. Kirsten Wegner, chief
executive officer of the Modern Markets Initiative; and Ms.
Martina Rejsjo, head of Nasdaq market surveillance, Nasdaq
Stock Market.
Witnesses are reminded that your oral testimony will be
limited to 5 minutes, and without objection, your full written
statements will be made a part of the record.
Dr. McIlwain, you are now recognized for 5 minutes to give
an oral presentation of your testimony.
STATEMENT OF CHARLTON MCILWAIN, VICE PROVOST, FACULTY
ENGAGEMENT AND DEVELOPMENT, AND PROFESSOR OF MEDIA, CULTURE,
AND COMMUNICATION, NEW YORK UNIVERSITY (NYU)
Mr. McIlwain. Chairman Foster and Ranking Member
Loudermilk, thank you for inviting me to testify.
While my written remarks cover four key areas, my oral
remarks focus on two: the implications of automation on the
workforce; and mitigating algorithmic discrimination and bias.
We have ample reason to be concerned about automation's
future in the financial services sector. First, the financial
services sector is ripe for automation and algorithm-driven
innovation. Second, the fintech sector is on the rise. Third, a
large number of workers will likely be displaced in the
financial services sector even if automation and AI development
is projected to create new types of jobs.
If all of this is true, then the cause for concern is
clear. It lies with the fact that African Americans and Latinx
workers, in particular, are already vastly underrepresented in
the financial services sector workforce.
African Americans, Hispanics, and Asians make up only 22
percent of the financial services industry workforce. African-
American representation in the financial services sector, at
both entry-level and senior-level jobs, declined from 2007 to
2015. Less than 3.5 percent of all financial planners in the
U.S. are Black or Latinx. African Americans make up just 4.4
percent and Hispanics just 2.9 percent of the securities
subsector. Asians make up just 2.8 percent of the central
banking and insurance subsectors.
My point is simple: Racial groups that are already
extremely underrepresented in the financial services industry
will be most at risk in terms of automation and the escalation
of fintech development. This is especially true given the vast
underrepresentation of African Americans and Latinx in the
adjacent technology sector workforce.
If we are to mitigate the likelihood that automation will
disproportionately and negatively affect those already
underrepresented in the financial services industry, we must
plan ahead long into the future rather than allowing the market
to run its course towards predictable outcomes.
Now, to the subject of deterring algorithmic bias.
Certainly, one way to mitigate against algorithmic bias is to
develop best practices for constructing and deploying
algorithmic systems and providing more oversight from industry,
government, and nongovernmental bodies who are able to assess
how such systems are used and the outcomes they produce.
This includes technical solutions that make algorithms more
transparent and auditable and mitigate against potential biases
before such systems gain widespread use rather than trying to
simply correct their effects once their damage is done.
But I want to emphasize that, especially when it comes to
mitigating the potential disparate outcomes that biased
algorithms might have on individuals and communities of color,
simple reliance on technical fixes by technologists is not a
complete solution.
I want to end by drawing on the wisdom of Bayard Rustin, a
former civil rights leader who had a sophisticated
understanding of computerized automation and algorithmic
systems as they existed in his time. He said, ``Today, the
unskilled and semi-skilled worker is the victim, but
cybernation invades the strongholds of the American middle
class as once-proud white-collar workers begin sinking into the
alienated world of the American underclass. And as the new poor
meets the old poor, we find out that automation is a curse. But
it is not the only curse. The chief problem is not automation
but social injustice itself.''
Take as a final example the findings from a recent National
Bureau of Economic Research study, titled, ``Consumer-Lending
Discrimination in the FinTech Era.'' Their researchers sought
to determine whether an algorithmic system could reduce
discrimination in mortgage lending as compared to traditional
face-to-face lending processes.
Their findings were mixed. Yes, the algorithmic system
discriminated 40 percent less than the traditional process, but
that also meant that the process still discriminated against a
large number of Black and Latinx loan applicants. Further, even
though the algorithmic system did not, on balance, discriminate
in terms of loan approval, it did discriminate against Black
and Latinx users in terms of price.
One of the key conclusions of the study states that both
fintechs and face-to-face lenders may discriminate in mortgage
issuance through pricing strategies. We are just scratching the
surface of the role of pricing strategy discrimination in the
algorithmic area of data use.
In short, algorithmic lending may reduce discrimination
relative to face-to-face lenders, but algorithmic lending is
not, alone, sufficient to eliminate discrimination in loan
pricing. Even with the aid of a fair, accurate, and transparent
algorithmic system, racial discrimination persists.
Thank you again for allowing me the opportunity to
contribute to these proceedings.
[The prepared statement of Mr. McIlwain can be found on
page 66 of the appendix.]
Chairman Foster. Thank you.
Dr. Lopez de Prado, you are now recognized for 5 minutes to
give an oral presentation of your testimony.
STATEMENT OF MARCOS LOPEZ DE PRADO, PROFESSOR OF PRACTICE,
ENGINEERING SCHOOL, CORNELL UNIVERSITY, AND CHIEF INVESTMENT
OFFICER, TRUE POSITIVE TECHNOLOGIES
Mr. Lopez de Prado. Thank you, Chairman Foster, Ranking
Member Loudermilk, and distinguished members of this task
force. It is an honor to be asked to contribute to this
committee today.
As a result of recent advances in pattern recognition,
supercomputing, and big data, today, machine-learning
algorithms can perform tasks that until recently only expert
humans could accomplish.
An area of particular interest is the management of
investments, for two reasons. First, some of the most
successful hedge funds in history happen to be algorithmic. The
key advantage of algorithmic funds is that their decisions are
objective, reproducible, and can be improved over time. The
second advantage is that the automation enables substantial
economies of scale and cost reductions. Automated tasks include
ordered execution, portfolio construction, forecasting, credit
rating, and fraud detection.
Financial AI creates a number of challenges for the over 6
million people employed in the finance and insurance industry,
many of whom will lose their jobs, not because they will be
replaced by machines but because they have not been trained to
work alongside algorithms. The retraining of these workers is
an urgent and difficult task.
But not everything is bad news. As technical skills become
more important in finance than personal connections or
privileged upbringing, the wage gap between genders,
ethnicities, and other classifications should narrow. In
finance, too, math could be a great equalizer.
Retraining our existing workforce is of critical
importance; however, it is not enough. We must make sure that
the talent that American universities help contribute and
develop remains in our country. The founders of the next
Google, Amazon, or Apple are this very morning attending a math
or engineering class at one of our universities. Unlike in the
past, odds are these future entrepreneurs are in our country on
a student visa and that they will have a very hard time
remaining in the United States unless we help them. Unless we
help them, they will return to their countries of origin with
their fellow students to compete against us.
On a different note, I would like to draw your attention to
two practical examples of regtech--that is, the application of
machine-learning algorithms to regulatory oversight.
A first embodiment of regtech is the crowdsourcing of
investigations. One of the most challenging tasks faced by
regulators is to identify market manipulators among oceans of
data. This is literally a very challenging task, like searching
for a needle in a haystack.
A practical approach is for regulators to enroll the help
of the data science community, following the example of talent
competitions or the Netflix Prize. Accordingly, regulators
could anonymize transaction data and offer it to the worldwide
community of data scientists, who would be rewarded with a
portion of the fines levied by regulators against wrongdoers.
The next time that financial markets experience something like
the flash crash, this tournament approach could lead to a
faster identification of potential market manipulators.
A second embodiment of regtech is the detection of false
investment products. Academic financial journals are filled
with false investment studies as a consequence of backtest
overfitting. Financial firms offer online tools to overfit
backtests, and even large hedge funds fall constantly for this
trap, leading to investor losses.
One solution is to require financial firms to record all
the backtests involved in the development of a product. With
this information, auditors and regulators could compute the
probability that the investment strategy is overfit, and this
probably could be reported in the funds' promotional material.
Finally, I would like to conclude my remarks with a
discussion of bias. Yes, machine-learning algorithms can
incorporate human biases. The good news is we have a better
chance at detecting the presence of biases in algorithms and
measure that bias with greater accuracy than on humans. The
reason is that we can subject algorithms to a batch of
randomized, controlled experiments, and we will calibrate those
algorithms to perform as intended. Algorithms can assist human
decision-makers by providing a baseline recommendation that
humans can override, thus exposing biases in humans.
As algorithmic investing becomes more prevalent, Congress
and regulators can play a fundamental role in helping reap the
benefits of this technology while mitigating its risks.
Thank you for the opportunity to contribute to this
hearing, and I look forward to your questions.
[The prepared statement of Dr. Lopez de Prado can be found
on page 53 of the appendix.]
Chairman Foster. Thank you.
Ms. Fender, you are now recognized for 5 minutes to give an
oral presentation of your testimony.
STATEMENT OF REBECCA FENDER, CFA, SENIOR DIRECTOR, FUTURE OF
FINANCE INITIATIVE, CHARTERED FINANCIAL ANALYST (CFA) INSTITUTE
Ms. Fender. Chairman Foster, Ranking Member Loudermilk, and
members of the task force, thank you for inviting me to testify
here today. My name is Rebecca Fender, and I am the senior
director of the Future of Finance Initiative at the CFA
Institute, which is our thought leadership platform.
CFA Institute is the largest nonprofit association of
investment professionals in the world, with 170,000 CFA
charterholders in 76 countries. CFA Institute is best known for
its Chartered Financial Analyst designation, the CFA Charter,
which is a rigorous, three-part, graduate-level exam. To earn
the designation, charterholders must also have at least 4 years
of industry experience.
CFA Institute is a nonpartisan organization and seeks to be
a leading voice on global issues of transparency, market
efficiency, and investor protection.
Earlier this year, CFA Institute published a paper on the
investment professional of the future, examining the changing
roles and changing skills of the industry in the next 5 to 10
years. Among the CFA Institute members and candidates we
surveyed, 43 percent think the role they perform today will be
substantially different in 5 to 10 years' time. And it was
greater than 50 percent among financial advisors, traders, and
risk analysts. Another 5 percent do not think their role will
exist by then.
One of the catalysts is technology. CFA Institute sees the
impact of technology on jobs in the investment industry as a
pyramid. At the foundation, we have basic applications.
Everyone will need to learn to do things differently, and they
must be more comfortable using and understanding technology.
Some people will face tech substitution, but many more will
have their roles adapted. In the middle, there are specialist
applications, where technology will enhance work. And at the
top, there are hyperspecialist roles that will be less common
but very valuable. This includes roles at quant firms and AI
labs.
CFA Institute believes the key to this evolution is ongoing
learning. Our exam curriculum now includes material about
machine learning. And among the members and candidates we
surveyed in our recent report, 58 percent have interest in
data-analysis coding languages, like Python and R. Similarly,
data visualization and data interpretation are areas that more
than half have expressed interest in.
In terms of the role of artificial intelligence in the
investment industry, the organizing principle we see is:
artificial intelligence plus human intelligence, or AI+HI.
In these middle and top levels of that technology
hierarchy, investment management and technology teams work
together. AI techniques can augment human intelligence to free
investment professionals from routine tasks and enable smarter
decision-making. Investment professionals will spend less time
finding and entering data and more time ensuring models are
consistent with how markets work. AI unlocks the potential of
unstructured data and can identify patterns in information more
efficiently than humans. AI can amplify an investment team's
performance but cannot replicate its creativity.
In our recent paper, ``AI Pioneers in Investment
Management,'' authored by my colleague, Larry Cao, we have
identified three types of AI in big-data applications that are
emerging in investment managemen: first, the use of natural
language processing, computer vision, and voice recognition to
efficiently process text, image, and audio data; second, the
use of machine-learning techniques to improve the effectiveness
of algorithms used in investment processes; and third, the use
of AI techniques to process big data, including alternative and
unstructured data, for investment insights.
We find that relatively few investment professionals, about
10 percent, are currently using AI and machine-learning
techniques in their investment processes. However, here are a
few examples from our case studies of what the AI pioneers are
doing.
First, Goldman Sachs' sell-side research team is better
able to analyze national concrete companies supplying the
construction industry by using geospatial data of 9,000 U.S.
quarries that each act as local businesses.
Second, the data science team at American Century
Investments studied psychology textbooks to determine patterns
of deception in children and criminals. They then applied
machine learning to these patterns in their earnings calls to
determine where spin, omission, obfuscation, and blame are
being used.
Finally, Bloomberg has had a sentiment analysis product
available since 2009 which analyzes the potential effect of
news stories on valuations. They process 2 million documents a
day through their machine-learning platform. This was
alternative data used only by hedge funds at first, but now
many of their clients use it.
Just as the investment industry is beginning to employ
greater technology, regulators can look at new data in the
world of regtech. This speed and volume of data presents a new
surveillance challenge. Regulators will need to have the tools
and resources to keep pace with changes.
Thank you again for the opportunity to testify today, and I
look forward to your questions.
[The prepared statement of Ms. Fender can be found on page
34 of the appendix.]
Chairman Foster. Thank you.
Ms. Wegner, you are now recognized for 5 minutes to give an
oral presentation of your testimony.
STATEMENT OF KIRSTEN WEGNER, CHIEF EXECUTIVE OFFICER, MODERN
MARKETS INITIATIVE (MMI)
Ms. Wegner. Thank you, Chairman Foster, Ranking Member
Loudermilk, and members of the AI Task Force. It is an honor to
discuss the role of automation of the markets and our
deployment of artificial intelligence in the financial services
industry and our future workforce.
I am Kirsten Wegner, chief executive officer of Modern
Markets Initiative. We are an education and advocacy
organization comprised of automated trading firms. We operate
in over 50 markets globally and, together, employ over 1,600
people. Our advisory board, which is half women, promotes
responsible innovation, including advancing a diverse workforce
in our industry.
Over the past decades, we have seen automated trading
leading to much of the replacement of the exchange-floor-based
intermediaries you see in 1980s Wall Street movies. Technology,
as you have noted, has reduced the cost of trading for the
average investor by more than half over the past decade, both
in direct trading costs and in savings through tighter bid-ask
spreads.
So if you are an investor in a 529 college savings plan, a
pension fund, or a 401(k), then you have benefited from today's
low-cost trading and all of the dependable liquidity that we
see in the markets. And studies have shown that over a lifetime
of savings, investors have 30 percent more in their bank
accounts as the result of the automation.
Now, as we look ahead, there are four points that I want to
discuss here in the oral testimony.
First, global competition to adopt the latest AI
technologies will make human decision-making more efficient in
terms of speed, processing time, depth of data, and it is going
to confirm more efficiencies and cost savings for U.S.
investors across-the-board. Already, competition in the markets
has resulted in near-zero-commission online trading from
Fidelity, Charles Schwab, and Robinhood, and we have seen a
rise in the ETF industry from those efficiencies. Similarly,
automated trading has brought down overall trading costs to a
fraction of the price from decades ago.
Second, we can expect to see a proliferation of regtech as
AI becomes increasingly valuable for individual firms and
regulators to police the markets more efficiently. AI
functionality in regtech includes monitoring, reporting and
compliance, and processing of regulatory filings; loan
origination processing; detection and reporting of illegal and
irregular trading; and detection of cyber risk.
And, notably, I want to point out that through public-
private partnerships, firms can play a role in working with a
regulator to share those limited resources in AI and to share
cutting-edge technology. Since 2017, several Modern Markets
Initiative members have welcomed the opportunity to work
together with FINRA in public-private partnerships. We are
contributing our know-how while welcoming deploying artificial
intelligence together to surveil the markets.
So automated trading firms are incentivized to detect bad
actors, because we, too, can be the victims of fraud. And as
bad actors become more sophisticated globally, it is absolutely
vital that financial regulators have the funding resources so
they, too, have the technological capacity and access to AI and
automated technologies to be a strong and effective cop on the
beat.
Third, as AI technology matures, we can expect increased
demand for high-quality, robust data, including alternative
data, to provide what I call the crude oil for the engines of
AI. This entails large quantities of complex data that humans
alone cannot digest. So I think we are going to see policy
questions arise around this proliferation of data; I think it
was already noted, questions of competition and antitrust in
the digital marketplace. We are going to see increasing
discussion of intellectual property rights and ownership rights
of that data and questions of access to that data and the cost
of data.
I think alternative data has been successful in helping
establish a credit history for the underbanked. That is one
positive. But I think we need to continue discussions
surrounding algorithm bias. And, in my prepared testimony, I
have noted next steps, including industry-led initiatives, to
share best practices, utilize ethics officers, and regtech
approaches.
And, last, I want to talk about the future of the
workforce. AI and automation can and should be a tool rather
than a replacement for humans. Some jobs will disappear, and
others will grow. Areas of growth we can expect to see are in
the computer occupations, jobs related to the transmission,
storage, security, privacy, and integrity of data, the fiber-
optics industry. They are all going to be fueling the AI
economy.
There is massive existing demand for qualified
technological talent across virtually all sectors of our
economy, particularly in the financial sector. The current
baseline participation for women, and particularly women of
color, is something that leaves room for substantial
improvement, and that is something we are focused on. And a
skilled workforce for tomorrow's Wall Street is only as good as
the companies that are there to invest in technology.
I thank you for your time.
[The prepared statement of Ms. Wegner can be found on page
89 of the appendix.]
Chairman Foster. Thank you.
And, Ms. Rejsjo, you are now recognized for 5 minutes to
give an oral presentation of your testimony.
STATEMENT OF MARTINA REJSJO, VICE PRESIDENT, NASDAQ MARKETWATCH
Ms. Rejsjo. Thank you, Chairman Foster and Ranking Member
Loudermilk, for the opportunity to testify on the impact of AI
on our capital markets.
Many people associate AI with high-tech and movies such as
``The Matrix,'' and ``Terminator,'' but we at Nasdaq strongly
believe that we can use this technology to target a wholly
different prey: the fraudster.
As you know, Nasdaq has extensive experience leveraging
technology to operate our markets and markets around the world
to protect participants and investors. We operate 25 exchanges
and 6 clearinghouses around the globe. And we sell marketplace
technology--trading, clearing, and surveillance systems--to
hundreds of the world's markets, regulators, exchanges,
clearinghouses, and broker-dealers.
Our internal surveillance department is monitoring the
markets for insider trading, fraud, and manipulation, as well
as handling real-time events in the market. The accessibility
of the markets and the increase in players with the ability to
deploy manipulative strategies using their own technology and
the exponential increase in data quantities can act as the
perfect ecosystem for market manipulators to hide amongst the
noise. This increased complexity in monitoring presents new
challenges for the surveillance team relying on preconceived
parameters and known factors to detect manipulative patterns.
Our surveillance program is using algorithmic coding to
detect unusual market behavior, running over 40 different
algorithms in real-time, utilizing over 35,000 parameters. In
addition to real-time surveillance, there are over 150 patterns
covering post-trade surveillance to identify a wider range of
potential misconduct. The team proactively develops tools and
procedures to increase the quality of surveillance and to meet
changing demands in the markets.
But with the manner in which patterns are currently
recognized, relying on known factors to describe behavior, it
can be difficult to capture new behavior and to remain
proactive rather than reactive to threats in the market.
In addition, predefined expectations of what patterns look
like can often limit alert results, depending on how alert
parameters are calibrated. Calibration also presents a
continued challenge when determining the best balance between
false positives and true alerts.
These challenges led to a calibration between the Nasdaq
Machine Intelligence Lab, Nasdaq's market technology business,
and the Nasdaq U.S. Surveillance Team, to enhance surveillance
capacities with the help of artificial intelligence.
Using AI to detect abnormal behavior patterns is based on
the notion that manipulative behavior can be identified by
signals in the markets, that a scheme to defraud market
participants often has a specific pattern to it. There is a
price rise or decline, an action is taken, and the trading is
then back to normal. So, this signaling concept leads to new
ways to look at pattern detection.
By leveraging AI, detection models are not tied to static
logic or parameters. We are able to train the AI machine based
on visual patterns of manipulation, and we started to look at
this spoofing pattern.
The machine must then further train with human input, and
then transfer learning was used to expand the scope of this
project beyond spoofing. Transfer learning leveraged AI to
apply a model developed for a specific task at the starting
point for a model on the second task.
By using deep-learning and human-in-the-loop techniques,
the new models for detecting market abuse with our initial
spoofing examples indicated usable results with 95 percent
fewer examples than typically required.
The inclusion of AI into the detection function will allow
us to focus the effort on in-depth investigations of potential
manipulative behavior instead of triaging a high number of
false positives.
But, to be clear, the human input is still of critical
importance, both in analyzing the output from the surveillance
system but also in continuously training the machine to produce
more and more accurate outputs.
The massive growth in market data is a significant
challenge for surveillance professionals. Billions of messages
pass through a larger market on an active day. In addition,
market abuse attempts have become more sophisticated, putting
more pressure on surveillance teams to find the needle in the
data haystack.
By incorporating AI, we are sharpening our detection
capabilities and broadening our view of market activity to
safeguard the integrity of our financial markets.
Surveillance is a critical use case for AI, but Nasdaq is
also looking to apply it in other businesses. For example, we
are using a version of AI, natural language processing, in the
listings business to facilitate the compliance review of public
company filings.
In closing, we are convinced that this use case for AI will
benefit investors and the resiliency of the U.S. market and the
other markets that we serve.
Thank you for the opportunity to testify, and I am happy to
answer your questions.
[The prepared statement of Ms. Rejsjo can be found on page
80 of the appendix.]
Chairman Foster. Thank you.
And I will now recognize myself for 5 minutes for
questions.
I should also mention to the Members present, it looks like
the latest estimate for votes is now 11:30, so we may, in fact,
have time for a second round of questions for Members who are
interested. We will have to play it by ear.
Dr. Lopez de Prado, you note in your testimony that today,
data vendors offer a wide range of data sets--and I think other
witnesses mentioned that--things that were not available a
couple of years ago. And not only the data itself but the
processing power to analyze it and the real-time delivery of
that data is becoming more and more important to successfully
trade on it.
Could you just illuminate for us what are some of the more
interesting data sets that you now see being used?
Mr. Lopez de Prado. Certainly. It is a combination of data
sets. On one hand, we have access now to credit card
transactions, geolocation data, satellite images,
transcriptions from earning calls, engineering data, and data
from engineering processes like exploration and production
companies that allow us to better estimate where the wells are
for extraction of oil or fracking--all sorts of data.
Keep in mind, please, that 80 percent of all data recorded
today was generated over the past 3 or 4 years. Going back to
history, going back to Mesopotamia, there is a lot of data
around, data that we aren't even aware of but is just being
scraped from websites and such.
So all of this data can be used to understand what is the
psychology of people, what is the state of mind of people,
understanding people are more inclined today to take risks or
to, for instance, relocate their assets to fixed income instead
of stocks; trying to understand from news articles, as one of
my colleagues mentioned, what are the narratives associated
with particular companies.
The amount of data today is staggering, and this is only
going to increase because the storage of data is becoming
cheaper every day and the processing power is increasing. So,
this is definitely a trend that is not going to stop.
Chairman Foster. Yes. And as I think I mentioned in my
opening remarks, that has a danger of driving monopoly, the
returns to scale--because you get more correlations to look at
with your AI if you have the full range of data.
And so, this will naturally cause those smaller players in
the market to be less effective, and less profitable. And I
think, that is probably what you are seeing in high-frequency
trading, the consolidation that you are seeing there.
Now, is there any way around this? And how hard should we
lean against the natural tendency to monopoly here in financial
trading?
Mr. Lopez de Prado. There are two schools of thought in
this regard.
Number one, there are a number of academics who believe
that this consolidation is not necessarily negative, in the
sense that the few survivors that are able to consolidate, for
instance, high-frequency trading, today are operating like
utilities. They are not making the kind of returns that they
were able to obtain 9 years or 10 years ago. Essentially, what
happens is that they break even. These technologies are
becoming so expensive that they have to spend this time and
money in order to achieve a profit that is dwindling.
There are a number of academics who believe that, actually,
consolidation is not necessarily negative. There is, on the
other hand, of course, the problem that a small number of
operators could have a grip on the market, and it also could
cause a domino effect if one of them fails to provide
liquidity.
So, there is a need to strike a balance between, on one
hand, preventing too much consolidation, and on the other hand,
also favoring competition between these operators.
Chairman Foster. Yes. Ms. Wegner, you mentioned that this
actually netted out--or, at least, electronic trading generally
netted out very positively for someone's retirement account,
that it actually, because of the lower bid-offer spreads and
transaction costs, that, actually, it was--I think you quoted
30 percent--
Ms. Wegner. Correct.
Chairman Foster. --more in your retirement account as a
result of this.
So, similarly, when AI is widely deployed, if it is very
effectively deployed, in principle we get a more efficient
capital allocation across our country. And so is, actually, the
best strategy to let a small number of very dominant players
have access to all the data sets to get a more efficient
economy?
Ms. Wegner. Yes. I think it is absolutely--
Chairman Foster. Or are we better off just letting a
thousand flowers bloom?
Ms. Wegner. Sure. I think it is absolutely vital that we
encourage policies that promote strong competition in this
space. And with high-frequency trading and automated trading,
we have seen such fierce competition over the past decade or
two that we are approaching near-zero latency speed, we are
approaching the speed of limits of--
Chairman Foster. But also more monopolization. I think my
time is up here, but this is something I intend to return to--
Ms. Wegner. Sure, absolutely.
Chairman Foster. --if we get a chance here.
Ms. Wegner. I am happy to respond.
Chairman Foster. Thank you all.
I now yield 5 minutes to the ranking member, Mr.
Loudermilk.
Mr. Loudermilk. Thank you, Mr. Chairman.
Ms. Wegner, as you know, the SEC has experienced some
cybersecurity difficulties, especially in the 2016 EDGAR data
breach. I think it is important for the SEC to only obtain
proprietary trading algorithms, if absolutely necessary, with a
subpoena. So I was wondering if you could discuss why it is
important for source code to be protected?
Ms. Wegner. Sure. That is a very good question.
The real lifeblood of automated trading and the, kind of,
secret sauce is the source code--that is the valuable
intellectual property that the different firms are competing
against each other with, not just domestically but globally.
And just like a self-driving car company needs to keep its
algorithms and source-code intellectual property protected from
misappropriation, so do algorithmic traders rely on government
protection for their intellectual property.
There was a proposal a number of years ago to perhaps
collect IP source code and put that in a government repository
just in case it was needed. That never came to light, but it is
still something we are absolutely educating policymakers on.
This should be, I think, a bipartisan area of interest, to
ensure that we have a globally competitive marketplace that
protects intellectual property rights.
Mr. Loudermilk. I appreciate that from my time in the
military working in intelligence. We had a principle we lived
by because of the sensitivity of the data that we collected and
maintained, which was, ``If you don't need something, don't
keep it,'' which means you don't have to protect what you don't
have.
And my concern is how vulnerable the industry becomes,
because, quite frankly, the government tends to be the weakest
link when it comes to data security in some aspects. So, I
think obtaining that source code is not only just a violation
of the privacy right of the business, the coder, but it could
also be a national security risk.
Ms. Wegner. I think that is right. If bad actors were able
to breach the source code, it would be presenting an
opportunity for manipulating the markets or cyber risks. So it
is absolutely vital that we protect the intellectual property
rights of source code.
Mr. Loudermilk. Thank you.
Ms. Fender, the adoption of artificial intelligence in
electronic trading can disrupt the job market and displace
floor traders, but technologies also create a need for more
workers in other fields.
Today, we have about a million people working in the
airline industry, but in the early 1900s, The Washington Post
led with a headline that said, ``Man Will Never Fly and
Shouldn't,'' and part of their argument was the displacement of
people in the job market.
Could you touch on the job fields that are growing because
of the use of AI in the capital markets space?
Ms. Fender. Yes. Thank you.
As you noted, there are many ways that jobs are changing,
and adaptation is really the key.
We surveyed industry leaders, the people who are doing the
hiring, and we asked, ``What are the most important skills
going forward? Maybe it is not necessarily in the job
description. What are the skills underlying who will succeed in
the future?''
And they talked about something called T-shaped skills.
This is an idea that, if you think about the letter ``T,'' you
have the vertical bar where there is deep subject-matter
expertise and a horizontal bar where you can cut across
different disciplines.
And if you think about fintech, we have big risk if there
is ``fin'' over here and ``tech'' over here, and they aren't
talking. So, the ability to connect the two is where there is a
lot of opportunity.
These are the innovators. This is an area where you will
see more research needing to be done so that we understand what
the trends are.
And the key thing is that people have to ask the right
questions. Firms are realizing you have to think about the
return on investment (ROI) of gathering this data. And many of
the machine-learning people will say a large percentage of the
data isn't that useful. So you have to be smart about how to do
that and start the process with investment professionals.
Mr. Loudermilk. Okay. So, what you are getting at is not
all the jobs are going to be just as deep intellectual, being
able to code and understand algorithms and all that, but there
are ancillary jobs that come about because of the development,
is that a fair statement?
Ms. Fender. Yes, definitely. We don't think, for example,
that all CFA charterholders need to become programmers, but we
think they are going to have data scientists on their teams,
and they are going to need to speak the language and work
together.
Mr. Loudermilk. Okay.
Ms. Rejsjo, I want to talk about the use of artificial
intelligence in fraud detection. I view cybersecurity as the
biggest challenge that we face in this nation, from a business,
government, and personal perspective.
Can you touch on quickly--I'm running out of time--how
algorithms are used to detect unusual market behavior?
Ms. Rejsjo. Yes. As I said, we really rely on the algorithm
coding to pick up on the unusual patterns that we see.
Everything needs to be compared to something that is usual,
right? So we program things to pick up on the unusual things
based on historical comparison on specific stocks, how they
have been trading in the past. So that is what we do already
and we have done for a long time.
The new thing here--
Mr. Loudermilk. Thank you.
Chairman Foster. At this point, I think we will leave that
hopefully to your next round of questioning.
The gentlewoman from North Carolina, Ms. Adams, is now
recognized for 5 minutes.
Ms. Adams. I thank the Chair very much for putting this
hearing together. We appreciate it.
And, also, those of you who have come to testify, thank you
very much for your comments and for your work.
Automation technologies, which enable the transfer of tasks
from human labor to machines, affect approximately 6.4 million
workers employed in the financial services industry. Specific
industries like credit lending and capital markets are being
affected by AI, as human tasks involving data analysis,
decision-making, and compliance are replaced by machine-
learning robots. This shift in job automation could predict
which jobs in financial services will be replaced and what new
jobs could be created.
Ms. Wegner, specifically examining loan underwriting
compared to the traditional methods of meeting a loan
application in person, to what extent does AI replace or
augment the work done by loan officers, credit counselors, or
other credit underwriters?
Ms. Wegner. That is a very good question.
In the consumer lending context, I think it is very
important that AI is the tool for humans when they are
extending credit and extending loans, that there are systems in
place to ensure that there isn't any sort of algorithmic bias.
And, in my prepared testimony, I noted some suggestions. Our
members are not engaged in the consumer lending context, but we
have our own insight.
I think that loan companies, individually or collectively,
could employ ethics officers to ensure that there isn't
algorithmic bias in the lending context. I think it is
important that industry members share lessons learned as they
explore how they are democratizing access to credit and finding
the most efficient ways to extend that credit.
I think it is really vital that we act now to make sure, as
we are building out this system, that we minimize the risk for
algorithmic bias in consumer lending. I think it is very vital.
Ms. Adams. Thank you, ma'am.
Is the U.S. properly equipped to remain competitive in the
financial services workforce?
This question is to Dr. Lopez de Prado and to Ms. Fender.
Mr. Lopez de Prado. The U.S. is the leader in the financial
services industry today. My concern is that this leadership is
being challenged by the fact that: first, we are not investing
as much in AI as other countries; and second, the fact that we
are educating our competitors.
In my remarks, I mentioned that I am very concerned that
the innovators of the future are attending today a class in our
universities but they will not be allowed to stay. And, as a
result, yes, we are very competitive, and this ability to train
these skills is going to turn against us if we are not able to
retain this talent.
Ms. Adams. Okay.
Ms. Fender?
Ms. Fender. We have seen that--again, it is early days for
how this changes our industry, with only about 10 percent
actually using these techniques. But what we are seeing is that
firms are doing AI labs, they are doing innovation hubs. They
realize that this is something they need to be proactive about.
And so we are seeing--out of our case studies, we had a
criteria that things in our case studies had to actually be in
practice. There is a lot of talk out there, but things that are
actually in practice, five of the nine are here in the U.S.
Ms. Adams. Great. Thank you.
Dr. McIlwain, are we adequately teaching the skills needed
for the jobs of the future?
Mr. McIlwain. Thank you for the question.
I think we are adequately teaching those skills; I think
the question is, who has access to that teaching?
And so, when we think about underrepresentation of certain
individuals and members of the workforce who are not getting
the types of education that are needed for the jobs that may be
coming online as a result of automation and AI development--and
so I think, if we are to have a full pipeline of folks who are
able to receive what it is that we teach in our colleges,
universities, even high schools and younger, then we have to be
more proactive about making sure that all people have access to
that teaching and that information.
Ms. Adams. No one left behind.
Mr. McIlwain. Absolutely.
Ms. Adams. Okay. I appreciate that.
I am going to yield back, Mr. Chairman. Thank you very
much.
Chairman Foster. Thank you.
The gentleman from Indiana, Mr. Hollingsworth, is now
recognized for 5 minutes.
Mr. Hollingsworth. I appreciate each of you being here
today, and I appreciate the chairman for holding this hearing.
This is an important topic, something I have been really
passionate about since arriving here in Congress.
And, Dr. Lopez de Prado, I appreciate your comments,
because what you have touched on is something that I have been
an ardent believer in for a long time, which is that the big
arm of the Federal Government isn't going to stop the growth of
this technology, isn't going to cease the investment in AI
either here or around the world. And while we can shape the
context by which that technology flows, we are not going to dam
up and stop that technology.
And so, when people say job losses may result on account of
this, there is a lot of fear and a lot of desire to put an end
to that and to stop that, but I like how you referenced a lot
of training and retraining that may need to happen--training
individuals who are graduating from school to ensure they have
the skills that are necessary in a 21st-Century workplace, but
also ensuring that those who are already in the workplace have
the opportunity to get the retraining to continue their
competitiveness. And as we see further growth and development
in AI, it will require more and more frequent retraining to
stay ahead of that, to stay relevant in that field. That is a
very competitive field, right?
But the second thing you touched on is something I am even
more ardent about. We educate a lot of kids in this country. We
do higher education in this country better than anywhere else
in the world. We bring a lot of talent into this country. We
invest a lot in those kids, and then we politely ask them to
leave at the end of their tenure here, right? That is
embarrassing, that is idiotic, that is stupid, and I hate that.
I want to find a way to attract talent into this country
and retain talent into this country, not because I believe it
is a zero-sum game but because I believe that this country can
provide a crucible for technological development that you can't
find elsewhere in the world. And I think that technology will
benefit humankind over all the world in the long run, and I
want to make sure we do that.
So, I really appreciate you touching on those topics, and I
really appreciate that investment of time.
Ms. Wegner, I know that you have a source-code event coming
up. Today? Tomorrow?
Ms. Wegner. This afternoon.
Mr. Hollingsworth. This afternoon, to talk about source
code again. And I really appreciate you continuing to educate a
lot of people about how important that is. Where I go, all the
way across the district in Indiana, I hear more and more about
how much technology, how much investment, how much IP is in
things that aren't readily seen, either in business processes,
in the source code, in the technology underpinning automation
itself. And so I know how important that is, and I really
appreciate you bringing that to light.
All that being said, I wanted to ask Ms. Rejsjo a question
that is maybe a little bit far afield from what we are talking
about today.
I had some people in my office earlier this week who were
very complimentary, frankly, of Nasdaq surveillance services.
They were very complimentary--they were public companies--and
how, when something seems amiss in the markets, Nasdaq was very
quick to pick up the phone and say, ``Something seems amiss.
Let's figure out what is going on here.''
One of the things that is very important back home is
biotech. A lot of biotech firms are based in Indiana. People
don't know that. We are trying to get the word out about it.
They are concerned about market manipulation, specifically with
regard to short-selling. And they are promoting this idea that
there should be more disclosure around short-selling, similar
to many long positions.
Now, they came in and said that disclosure around short-
selling would really help us, as a firm, better understand
those that might have interests adverse to us, because we can't
really track that right now. But the counter-argument that they
made was, gosh, Nasdaq seems to be doing a really good job of
figuring out when there is potential manipulation.
I wondered if you might touch on that. Is disclosure in
short-selling something that would benefit the market,
something that would benefit these firms? Or do you feel like
you have enough of the ability to track potential market
manipulation on the back end?
And, again, I am not pejorative against short-sellers. I
just want to make sure that it is legitimate action, not market
manipulation.
I wonder if you might comment on that in the last minute.
Ms. Rejsjo. I think disclosure is a big part of
surveillance.
Mr. Hollingsworth. Yes.
Ms. Rejsjo. Information is always needed to understand what
is happening.
Mr. Hollingsworth. Okay.
Ms. Rejsjo. I do think that what we have today is
sufficient. As you say, we have a lot of patterns that are
detecting manipulation such as short-selling, or, I might say,
the troublesome part of short-selling.
Mr. Hollingsworth. Right.
Ms. Rejsjo. I mean, short-selling is legal, right?
Mr. Hollingsworth. Right. Of course.
Ms. Rejsjo. So it is really to detect what is then being--
how it is used in an abnormal way or in a sort of manipulative
kind of way.
Mr. Hollingsworth. Yes. So you feel like you can detect the
activity that would be illegal or abnormal or different
adequately. The question is, what do we do with it after that
point, is maybe where we should focus public policy attention?
Is that fair?
Ms. Rejsjo. Yes. But to be fair, also, there are other
parts within Nasdaq that handle more of the policy questions.
Mr. Hollingsworth. Okay.
Ms. Rejsjo. But for me as a surveillance practitioner, I do
think that the disclosure we have and the tools we have to
monitor the markets are--
Mr. Hollingsworth. Are adequate.
Ms. Rejsjo. Yes.
Mr. Hollingsworth. Great. I think that is an important
question. Because when they were in my office, I think that is
the question: Where do we need to focus public policy
attention? And perhaps it is beyond surveillance, and focus
more on some of the penalties or some of the actions that
happen with the enforcement agencies.
With that, I will yield back.
Chairman Foster. Thank you.
And I am very encouraged that one of the areas of
bipartisan agreement here is the insanity of this business of
awarding people their Ph.D.s and pushing them back on an
airplane.
And so that is one of the reasons I was proud to introduce,
this session of Congress, H.R. 4623, the Keep STEM Talent Act
of 2019, designed to--it is a rifle shot to just exactly solve
this problem. And I really look forward to my colleagues'
support on this.
And now, I recognize the gentlewoman from Texas, Ms.
Garcia, for 5 minutes.
Ms. Garcia of Texas. Thank you, Mr. Chairman. And thank you
again for holding this hearing.
And thank you to all the witnesses. Good morning, and
welcome.
I wanted to focus on a couple of issues that some of you
have already talked about. Like Ms. Adams, I am particularly
concerned about jobs. My district is in Houston, and is 77
percent Latino. It is also working-class, so we are always
concerned about jobs. I am encouraged that you all seem to have
the consensus that, although there will be some job
displacement, there will be new jobs created.
My main concern, of course, is whether or not we do have
the skill sets, Mr. McIlwain, to transfer those skills or to
make sure that we can fill those jobs. Because, in the end,
that is what really matters to families in my district.
But I am also concerned with automation and the difference
between AI and automation, and how it can work together,
specifically in the area of regulatory compliance.
Ms. Fender, in your experience, has AI and automation
affected institutions' regulatory compliance? Is it improving?
Is it still a work in progress? Or how are we doing?
Ms. Fender. Thank you. That is a very good question. And,
again, I think it is about--it is still kind of early to know.
We hear so much about what is coming, and yet--so compliance
areas are growing in firms clearly. And now we have more and
more data, and regulators are going to be able to have the same
sort of data.
The question is, is there a greater risk, maybe, of insider
information now? You collect more data, and people can see lots
of different patterns out there. And if they see that and can
trade on it before the market, then you have challenges for the
SEC, I think, in terms of Reg FT and so forth.
Ms. Garcia of Texas. Okay.
Ms. Wegner, can that be used to simplify and ensure
regulatory compliance with the Federal agencies in charge of
supervising the capital markets.
Ms. Wegner. Sure. I think, as the data sets become more
complex, as you just alluded to, I think it is going to be
vital that the regulators have the resources to have their own
AI, either independently of the companies, or together with the
companies through public-private partnerships as the bad actors
become more sophisticated, and we are talking about global bad
actors. We need a strong cop on the beat here in the U.S. And I
think it is very important that the private sector work
together with regulators to ensure that they have those
resources, and that Congress really ensures that the SEC and
the CFTC have the resources they need, because the systems are
becoming much more complex and regtech is evolving, but needs
to keep up with the pace of that technology.
Ms. Garcia of Texas. I think that is a big concern of this
committee, those bad actors, as you have described them.
So how can AI assist us with anti-money-laundering
compliance's suspicious activity reporting? Are we prepared for
that? I know we did a codel to several countries. And things
are getting more and more sophisticated, and it seems like the
bad actors have more money and better things, to find ways to
hide the money. Do we have what we need to detect it and to
ensure that we can catch it?
Ms. Wegner. It is vital that we focus on this. And I would
say Haimera Workie, who is the new head of innovation at FINRA,
has an excellent group. They just established themselves this
year. They are a fantastic resource. They are working together
with other regulators, with private sector participants to
gather information about best practices, and to really make
sure we have the best technology. This is 100 percent something
we need to be focused on.
Ms. Garcia of Texas. In your opinion, do you think that our
regulators and our oversight entities are well-prepared in this
arena, or what else should we be doing?
Ms. Wegner. I think we need to be investing in technology.
There is always room for more technology with the regulatory
agencies. I think MIDAS at the SEC has been a very positive
example of the SEC using very sophisticated technology to
surveil the markets, but I think this is a constantly evolving
space, as everyone here has noted. We have to just keep very
much on our tiptoes on this, and keep on investing in this
area.
Ms. Garcia of Texas. Okay.
Ms. Fender, did you want to add something?
Ms. Fender. I think the more data we have, the more complex
it gets, right? And one of the other things that we are really
concerned about is the investor protection side too.
If bad data goes into these models, they can be marketed in
many different ways. And so, disclosures are really important.
Understanding your clients, understanding where the money comes
from, and understanding what clients are really getting all
kind of goes together.
Ms. Garcia of Texas. Thank you. Thank you, both of you. And
I yield back.
Chairman Foster. Thank you. The gentleman from Virginia,
Mr. Riggleman, is recognized for 5 minutes.
Mr. Riggleman. Thank you, Mr. Chairman. I want to thank all
of the witnesses for being here today. I am so happy that all
of you are here, so I am not showing any favoritism. I would
particularly like to welcome Ms. Fender from the CFA Institute,
which is located in my district in Charlottesville, Virginia.
The CFA Institute provides a host of resources for
professionals who work in the financial services industry, who
are among the most qualified and adhere to the highest codes of
standards in the financial industry. I am honored to have such
a distinguished group reside in the Fifth District. Although
Ms. Fender is not a constituent herself, her organization
employs many of them, so I am thrilled to see you here today.
Welcome, and welcome to all of you.
I will start with you, Ms. Fender. You probably knew that
was going to happen. Can you talk about how CFA is adapting the
charter to these AI and machine-learning innovations in the
investment industry?
Ms. Fender. Thank you very much. And I'm pleased to be here
representing Virginia.
The CFA Institute is really the global standard for
investment practitioners. The people who have our credential
are the portfolio managers for your 401(k). They are the chief
investment officers at the public pension funds. They are the
people who are really safeguarding the financial futures of so
many people. And so, it is imperative for us to keep up-to-date
on what we teach. I mentioned earlier in my testimony that we
just added machine learning into our curriculum. And this is a
significant indication that we are seeing the market change.
And we need to prepare people.
We have a group called our practice analysis team. And they
are out there all the time going to these conferences, figuring
out what is the next thing that people need to know, because
global demand for investment management is growing, and
especially for those who really combine both competence and
ethics.
Mr. Riggleman. There really is a reason I asked that
question. My prior job, and we talked about monopolization of
the data and things of that nature. I wanted to monopolize as
much data as I could for data interactions when looking at sort
of critical infrastructure analysis when I worked for the
Office of the Secretary of Defense. We had about 40 people
looking at this, so we had to look at all data, multi domain
across stovepipes, and see how that actually includes that data
or to analyze or aggregate that data, consolidate it, aggregate
it, analyze it, and then execute using that data based on how
we templated human behavior.
So looking at AI and ML rules, I guess that I will start
with Ms. Rejsjo, I am going to ask a few of these, because this
is the exciting part for me, is the technology part. When we
did this, we had multiple data sets that people had never seen
before. We talked about the challenges of data. We had multiple
data sets. We had data we had never sort of aggregated, and
combined with other data sets.
So we thought we had the right answer, and we found out we
didn't, in trying to template human behavior analysis. Do you
think that is something you are going to see more of in the
future, is that there won't be a human in the loop, and there
will be more sort of human templating, or machine-learning
rules to sort of mimic what human behavior does with certain
rule sets? Do you think we are going to see more and more of
that, taking humans out of the loop, looking at actually any
type of analysis, or fraud, or anything of that nature?
Ms. Rejsjo. I think that we are a long way from that. I
think that for now, the way that we do it is really to have the
data that we have. For us, it is really much more the order and
trade data that we already have and that we analyze. Now, we
are just applying a new technique to give us a better overview
that is not that parameter-driven. But for us, still, I really
think that the human in the loop is the way to go, because
there is much more analysis that needs to be applied after the
output has come. And I think that is going to be there for a
while.
Mr. Riggleman. It is interesting that you said that. We
actually thought we could take the human out of the loop in
some of our processes, and found out it was not a good idea,
with some of the things that we did. I see some heads nodding
back there. We tried to do that.
Dr. Lopez de Prado, you were talking about, there could be
some advantages to sort of aggregating as much data in one
place as we can, right? And then looking in the gaps of that
data. That is the thing I have been trying to wrap my arms
around. My whole job was not competition. It was to monopolize
all the data. And then to use competition to give us the best
algorithmic solutions that we could first for first, second,
and third order effects of what happened to a specific part of
the network.
This is a tough question, because to be this objective in
40 seconds is going to be probably ridiculous. But when you are
looking at this, do you think--and I know this is a tough
question--with all the proprietary technologies out there, do
you think there will be a voluntary sharing of that data if we
find something that is very good, across multiple sets? So, for
example, another company, do you think we will have that type
of sharing for proprietary solutions based on algorithmic types
of analysis? Do you think that will ever happen? Or do we think
we have to sort of force that to happen when we monopolize that
kind of data, if that is makes sense.
Mr. Lopez de Prado. Are you referring to sharing these
technologies--
Mr. Riggleman. Yes.
Mr. Lopez de Prado. In private and public companies?
Mr. Riggleman. Yes.
Mr. Lopez de Prado. When you look at the NASA model,
actually, there has been a lot of transfer of IP between the
agency and various contractors. So that could be a model that
could work for the CFTC and the SEC. In particular, in my
remarks I mentioned the crowdsourcing of investigations, how
the companies or private participants could establish
tournaments to help agencies identify market manipulators.
Mr. Riggleman. Thank you very much. And I yield back the
balance of my time.
Chairman Foster. Thank you.
The gentleman from Illinois, Mr. Casten, is recognized it
for 5 minutes.
Mr. Casten. Thank you, Mr. Chairman. And thank you all so
much for being here today.
Back in my prior life, I had a head of engineering who had
a theory that I have yet to prove wrong. He said, every advance
in technology gives us more precision and less knowledge. This
was a guy who started with slide rules, where he had to know
the order of magnitude of his answer. And now, he got 16
significant digits, and can never remember whether it was
millions or billions. And, of course, in my lifetime, we have
gone from foldable maps to GPS that can give me the exact
latitude and longitude. And I can't tell you whether I am
north, south, east, or west of where I started.
AI has always struck me as sort of putting that
acceleration on steroids. At one point, I built a genetic
algorithm to predict the revenues of our utility business, and
it was amazing. I cut our revenue forecast variance by 90
percent, and I have no idea how it worked.
And that is the power and the frustration. I mention that
because I think most of you have talked about the consumer
benefit that comes when we get all these AI algorithms out in
the markets, and we get lower trading costs, lower bid ask
spreads. And that is all terrific.
A lot of you have also talked about bad actors and we can
put up monitoring for that and that is also great. The concern
I have is this tension between the transparency of the model,
and whether the model can actually effectively replicate a bad
actor that we don't understand, because it is fairly easy for
me to imagine the trading algorithm that is tracking a bunch of
data and has figured out how to bet on one country invading
another and making money. I can imagine a trading algorithm
that is looking at changes in currency flows for illegal
activity that is not in itself illegal, but is arbitraging some
spread that results from that.
So Ms. Wegner, I wonder if you would comment on that
tension between transparency and algorithm robustness? And to
what degree we have or need regulatory tools to stipulate where
we sit on that continuum?
Ms. Wegner. Sure. I think transparency is absolutely vital.
I think it is also very vital that regulators and the exchanges
have the resources that, if they note any sort of irregularity
in the markets, they can immediately identify that. And to the
question of whether or not one needs to get source code, if
there is a detection of some sort of illegal or irregular
activity, then the regulator requests--
Mr. Casten. But if I could just clarify, first, would you
agree that the more transparent the algorithm, potentially the
less powerful the algorithm?
Ms. Wegner. I think to the extent that the algorithm is not
subject to intellectual property rights, that transparency is
absolutely vital. If we are talking about intellectual property
rights in this source code of algorithm, that is proprietary
information that if it was--
Mr. Casten. By transparency, I am not referring to whether
or not the public has access to the algorithm. I am referring
to whether or not our human brains can understand how the
algorithm works. I could give you the genetic algorithm I
wrote. You couldn't understand what it is doing.
Ms. Wegner. Sure. That question becomes more complicated in
the machine-learning context, especially. You point to an
interesting question, as the commands become self-acting in a
way, they are basing their analysis on the existing data sets.
I don't think we are totally there yet, but I think that is
something we definitely need to explore, what does our policy
answer to, because that is an interesting balance.
Mr. Casten. This question is for you, but really for all of
the panelists. I think thinking about that problem before it
gets there, because it strikes me that there will be pressure
for every trading firm to develop the most powerful algorithms,
which, by definition, at some level, are going to be the ones
that we have the least ability to unpack and understand.
Ms. Wegner. I think this is an important question that the
industry should get together on and share their best practices,
how do you balance that push for trade?
Mr. Casten. For anybody who thinks they have a great answer
in this, number one, how should we do that? And number two, to
what degree do we need to coordinate internationally? Because
even if we do everything in our country, since all of these
markets are so interlinked, is this a U.S. problem, or is this
an international problem? Does anybody have thoughts on that?
Mr. Lopez de Prado. If I may, this is a very important
distinction. Black boxes in finance tend to be less reliable
than transparent solutions, particularly in finance, because we
are dealing with problems where the signal-to-noise ratio is
very low. Unlike, for instance, in astrophysics research, why
is the signal-to-noise ration low in the finance world because
of competition, because of arbitrage? Otherwise, everybody
would be able to extract profits from the market. So because of
that, when we deploy black box solutions in finance, the
solutions can identify patterns that are not real, they are
just balancing the noise and confound these patterns with these
noise patterns with actual signal, leaving to investment
studies that will fail. So one solution would be for in
investor to understand very carefully when a product is based
on a black box solution as opposed a transparent machine-
learning solution.
Mr. Casten. Thank you. I yield back. I would welcome any of
your comments. If you have any follow-up in writing, please
share.
Chairman Foster. As I mentioned, we are likely to have
another round for Members who are interested here.
The gentleman from Missouri, Mr. Cleaver, who is also the
Chair of our Subcommittee on National Security, International
Development and Monetary Policy, is recognized for 5 minutes.
Mr. Cleaver. Thank you, Mr. Chairman. And I really
appreciate you calling this hearing and we appreciate all of
you giving us your time.
I don't know how we are going to deal with AI and human
beings. Long before we had flip phones, Captain Kirk had one,
and long before we had the smartwatches, Mr. Spock had one. And
a lot of attention is always paid to Hollywood, particularly in
science fiction, and the military, our own military.
So a lot of people have their eyes on a fearful future, as
it relates to AI. And to be straight, I am one of those, I am
conflicted. I know we can't hold back the wind. It is
inevitable that we are going to see more and more of this in
the future. And I am not sure that we ought to try to hold it
back. But to the degree that we can control it, that is what I
think we ought to do. And that is where I am concentrating most
of my answers.
Dr. McIlwain, first of all, thank you for being here. But I
am wondering how inclusive this new technology is right now,
and what can we do to make sure that in the future, every
component of our great mosaic in the United States is a part of
it?
Mr. McIlwain. Thank you for that question, and I share a
little bit of your fear, because what we know persists as
technology changes, as technological advances are made is that
some people, and typically, the same groups of people, are left
out, left behind, disadvantaged. And so, even as technology is
unpredictable, some of those exclusions are very much
predictable.
I think those exclusions are present in our current market,
as most of the folks in this panel have at least alluded and
nodded to; that is, when we look at our technology sector,
those who are prepared to be part of that sector, those who are
currently working, building the technologies of today and
tomorrow are tremendously unrepresentative of our full
democracy of all the citizens of our country. And I think
representation makes a tremendous difference. I think the place
we are in today with respect to some of the inequalities and
devastations that technologies, AI and automation included,
have wreaked, because not everyone has been included in the
decision-making about what technologies will be built, why, for
what purposes, who they will advantage and disadvantage.
So I think moving forward, we have to change that. That is,
we have to invest strategically in building a more inclusive
workforce in these sectors that are growing. That is the
technology sector and the financial sector as well.
Mr. Cleaver. What do you think we should do, or any of you
do right now, if we--we have young people interested in and
committed to the future, and AI is an inevitable part of it.
What should they do next week? What should young people be
doing? How should we direct young people right now, who are
scientifically gifted? What should we do?
Ms. Wegner. I think we need to promote responsible
innovation. I know our members support trying to get out there
to the middle school students geographically across the
country, a diverse population of people, and get them
interested in STEM fields. I think there is a lot of
opportunity for companies to partner with some of the public
schools in a geographically diverse part of the country and
help fund that, and just recruit now. Kids get interested in
these fields from a young age. And we just have to get in there
early and make sure that people see role models at the firms
that we promote those public-private partnerships.
Mr. Cleaver. My time is up, so thank you very much. Thank
you, Mr. Chairman.
Chairman Foster. Thank you.
And, I guess, we have time for a brief second round of
questions here.
We have had sort of two different narratives that have been
going on here. One is the, sort of, optimistic narrative of
the--well, I guess the T-shaped skills or machine intelligence,
human intelligence paring, albeit second, augmented human
intelligence. And then we also have the sort of intermediate
way of transfer learning, where you would actually use one
field of expertise and transfer what was learned there to
another field thereby replacing multiple machine parings. An
example of that was the example from the geniuses at Goldman
who were analyzing satellite imagery of quarry activities to
predict cement pricing, and so on in the future; and then,
potentially, using transfer learning so that knowledge could be
transferred to copper mining or whatever else it was.
On the other hand, there is an alternative narrative that
you just aggregate all the data you can, and just say, I want a
general purpose, learning, trading algorithm to look at all
satellite data and look for all market correlations. And that
would detect not only the cement market, it would look at the
parking lots of Toys R Us to predict that Toys R Us was going
bankrupt because they didn't have many cars on Black Friday.
And so this sort of thing could be written once and
deployed to replace tens of thousands of machine-human pairings
on here, and, obviously, with much, much smaller labor input
and need for humans. So which of the two narratives is going to
end up winning, and how is it going to net out for human
participation in this? Anyone who wants to tackle that tar
baby?
Ms. Fender. I can start, and just say that one of the
foundational concepts in investing is that correlation is not
necessarily causation. And so, we have a lot of data and we can
see these patterns, but you need a human to ask, what is the
right question? I mention also the example of going through the
news stories with Bloomberg. And they said, the key question
there was to go through the news article and not say, what do
we think the author of this article wanted to get across, but
what do we think people are hearing?
So there are a lot of nuances really about how this is
going to play out. And that is why, again, having sort of the
collective intelligence and diverse perspectives is going to be
important.
Chairman Foster. Dr. Lopez de Prado?
Mr. Lopez de Prado. Yes. I think that the two narratives
have some part of truth. I think in the short term, we have
reasons to be worried in terms of the transfer of knowledge,
and the potential displacement that will occur as these
technologies are more broadly deployed. But I think in the long
term, we have reasons to be optimistic, because the next
generation would be better prepared than our generation, or
previous generations. It is very important that we give equal
access to education. It is very important that we encourage
kids to learn how to program, participate in math and
engineering classes, and that we form the flexible workforce, a
workforce that in the future, we don't know what these
technologies will do in 20 years, that they are able to engage
proactively.
Chairman Foster. Is there a danger that this is going to
squeeze all profitability out of financial services? That if
you had complete knowledge of everything, and very efficient
algorithms immediately trading on that knowledge, the 30
percent improvement in your retirement savings, all of that
money used to end up in the pockets of people with nice homes
in Oyster Bay, and that is sort of the nature of things. And it
may be that when we get this much more efficient economy with
extensive deployment of AI, just the total amount of money left
to be extracted will continue to go down the same way high-
frequency trading is sort of suffering that decline in margins.
Mr. Lopez de Prado. One view, if I may, is that, in fact,
having such a perfect market is not necessarily bad for
society, meaning that the day that we go to our financial
adviser and we receive the same treatment that we receive when
we go to the doctor essentially, there is a product goal of,
this is what you need to invest to achieve your retirement
goals. I think that is a good outcome.
Ms. Wegner. And as we see greater efficiencies, global
advisors and other more efficient, I would say, asset managers,
we will be able to deploy that efficiency to the masses. But I
think it also raises a global competition question, because we
are not just talking about competition domestically, we are
talking about internationally, and we are not going to stop
time all across the world, right? Other countries are
innovating in AI. So it is inevitable we are going to be
competing in that space and we want to keep the U.S. markets
the envy of the world, I think.
Chairman Foster. So if the future of financial advising is
conversations with Alexa, I guess it comes down to, is the
objective that a function that the AI running Alexa is
maximizing, is that Amazon's profit? Or is it some linear
combination of Amazon's profit and diversity inclusion, a
secure retirement rather than steering people into products
that are profitable for Amazon?
Ms. Wegner. Right. I think the vital part here is, as you
mentioned, we have competition, that there is not too much
aggregation of power in one entity. We need to have policies
that promote robust competition amongst, let's say, the robo
advisors that make sure that data is accessible at competitive
prices, so there is not a barrier to entry. This is going to
be, I think, an exciting space where a Financial Services
Committee meets a Judiciary Committee on antitrust issues, and
meets a Commerce Committee. Finance is becoming more
technology, and technology is becoming more finance. So, those
are the right questions.
Chairman Foster. Thank you. And I will yield 5 minutes to
the ranking member.
Mr. Loudermilk. Thank you, Mr. Chairman.
Ms. Rejsjo, I would like to go back and kind of continue
our conversation that we were talking about, cybersecurity, and
using AI, and fraud. And I wasn't managing that time very well
before, so could you explain further how Nasdaq is using AI in
fraud detection?
Ms. Rejsjo. Yes, I think it is important, just to start,
that--I mean, the future is here, right? We have billions of
data points. It is a massive amount of data that needs to be
analyzed to capture anything that is fraudulent or manipulative
in the market. So, we have that environment already. And what
we have been doing so far is deploying algorithmic coding to
sort of be able to process all of this data very fast. Our
real-time surveillance is picking up on unusual behavior within
seconds after it has happened in the market.
So there is, really, a fast and efficient way to go through
the data, but as it is growing and exponentially growing, there
is the need, of course, to continue to invest in other ways of
looking at it, where AI then comes in. It is a broader
approach, and it doesn't have to be those parameters specific
that we are today, so we can capture more things that are more
sophisticated. Because as we have been talking about, it is not
only us using this technique, the participants in the market
are using it as well. So I think it is important for us to
match their technology with our technology, when we are look at
the types in the market with the behavior.
Mr. Loudermilk. Thank you.
Dr. Lopez de Prado, can you touch on the differences
between automated trading, algorithmic trading, high frequency
trading, and computer trading, how they are not all the same
and what differentiates each of those?
Mr. Lopez de Prado. Yes. Algorithmic trading consists of
following some rules. A computer follows some rules in order to
achieve a particular outcome. It does not require machine
learning. Machine learning is the learning of patterns from a
set of data with us directing that learning. Essentially, what
happens is, you give to an algorithm a data set, and the data
set identifies the pattern we were not aware of. So, that is
machine learning.
What was the third one?
Mr. Loudermilk. The automated trading and high frequency.
Mr. Lopez de Prado. Yes. Well, high frequency trading is
algorithmic trading at a fast speed. It can happen with or
without machine learning, so in the earliest stages, 2005, the
high frequency trading or core without intelligence. Today,
what we see is liquidity providers, market majors, hedge funds,
deploy high frequency solutions with machine learning embedded.
Mr. Loudermilk. Thank you.
Ms. Wegner, we have had some discussion on the cost savings
that have resulted from AI and automation in the capital
markets. Do you see that these efficiencies are a significant
reason behind the record returns investors have enjoyed in the
last decade?
Ms. Wegner. This has definitely contributed to the returns,
every reduced incremental cost of trading adds up with
compounding interest over time. So as the markets become more
efficient, investors are going to have more in their
pocketbooks, whether half of America invested in a 529 plan or
otherwise for the net positive.
Mr. Loudermilk. Okay. Thank you.
I have no further questions, Mr. Chairman. I yield back.
Chairman Foster. Thank you. The gentleman from Missouri is
recognized for 5 minutes.
Mr. Cleaver. Thank you, Mr. Chairman.
I am interested in, how do we do planning now for the
future? For example, we just updated our anti-money-laundering
deal, or the Bank Secrecy Act. And I am sitting here now, and I
introduced the bill, so I have been feeling pretty good about
myself until you guys came up today. And I am thinking, why did
we go through all of that? Because the bad guys are out there
trying to figure out how they can exploit whatever we pass
legislatively. How do you see an AI involved in anti-money-
laundering efforts, like the legislation that we hope the
Senate will take up during our lifetime? Is there any way you
think that can play a role, that AI can play a role in our
money laundering bills or how we are trying to reduce it? We
know we are probably never going to eliminate it.
Mr. Lopez de Prado. This is a gargantuan problem. We have
to tackle tremendous amounts of data, terabytes of data, and
identify this needle in the haystack. I think a practical
solution is for regulators to work together with data
scientists, with the entire community, and crowdsource these
problems. We need to anonymize this data, and give this data to
the community so that the community help us enforce the law. Of
course, they could be rewarded with part of the fines levied
against wrongdoers, but I think that is a very doable approach,
given: number one, how difficult it would be for the agencies
to develop the kind of techniques that the wrongdoers are
developing for bad purposes; and number two, the amounts of
data that we need to parse through.
Mr. Cleaver. We had the Treasury Secretary before our
committee yesterday. I, of course, didn't even raise this
issue. We have an agency, FinCEN, which is an investigatory
part of Department of the Treasury. So, I am here wondering
what they are doing to try to keep up with the technology, and
what challenges they are going to face in the future. And you
all have destroyed almost everything I was proud of, but we
appreciate you coming here anyway. Thank you very much.
I yield back, Mr. Chairman.
Chairman Foster. Thank you. I would like to thank our
witnesses for their testimony today.
The Chair notes that some Members may have additional
questions for this panel, which they may wish to submit in
writing. Without objection, the hearing record will remain open
for 5 legislative days for Members to submit written questions
to these witnesses and to place their responses in the record.
Also, without objection, Members will have 5 legislative days
to submit extraneous materials to the Chair for inclusion in
the record.
This hearing is now adjourned.
[Whereupon, at 11:05 a.m., the hearing was adjourned.]
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