Class 1: Intro and Key Technological Trends Affecting Financial Services

[SQUEAKING] [RUSTLING] [CLICKING] GARY GENSLER: So
this FinTech course– this is for those who
want to explore FinTech, how the technologies are
disrupting financial services. That's the core of it. Technology is
disrupting finance. And we'll talk a lot about this,
that finance and technology have lived in simpatico,
in some relationship together for thousands of years. In fact, money and ledgers were
initial financial technologies. And we'll talk about
what makes something in this faculty's mind a
financial technology that's really changing the
world and then just the technology that exists. The telephone, for
instance, at one point, in that time, in the
1920s, was, in essence, a financial technology
that rapidly changed the world of finance. Or even in the 19th century,
the telegraph rapidly changed parts of finance, when
you could send your first money gram– or in those days, it was
called something different, but it was a telegram
attached with money in the 1870s and 1880s. But this course is going to
be about the cutting edge. We're going to be
talking about business models and the like
around AI, deep learning, blockchain technology, OpenAPI.

10 and 20 years
from now, OpenAPI will not be taught, by my
view, in a FinTech course. But it's the relevant
topics of the day. And we'll be looking at
the competitive landscape. Those of you that have decided
to take this course on top of consumer finance course,
the half-semester course that was at the same
time, you will know that I usually teach in the
concept of business strategy. What is the strategy that these
startups which big tech, which incumbents are looking at this
point in time, in this day and age, in this sector? And this course is also, I
should say, being recorded. It's being recorded
for some students who can't join us
simultaneously or what's called synchronous learning. And these recordings
will be posted on Canvas within a day or two. Lena and Romain and I
just have to remember how to do that and actually
post each of the recordings. They also might be
shared, just to alert you, in OpenCourseWare in
the fall or later. I've chose with MIT that if
we're recording them anyway, maybe if they come out
being anywhere valuable, that we would up to
the broader community.

So these might be shared more
broadly come the fall as well. It's also really to gain
critical reasoning skills around the ground
truths of FinTech, separating hype from reality. Every week, there's a posting
of three or four readings. I understand that even
if we were all on campus, you might not read
every word of that. But they really are
sort of the foundation. And I hope that in each
lecture, in each class, we can go beyond that. But this week, the Bank of
International Settlement Working Paper and the
Financial Stability Board are two papers that a
lot of people turn to. The Financial Stability
Board is a group of 20 countries, the
G20 countries, that have banded together and their
treasury secretaries or finance ministers and central banks
and securities regulators have formed this thing called
the Financial Stability Board.

And they publish very good work. This paper came out in 2017. It feels a little
dated right now. But it still felt
quite relevant. And then, of course, the Bank
of international Settlement is 60 or 70 central
banks out of Basel. And they write very good work. I thought it was also
interesting to take the current chair of the US bank
regulator, the Federal Deposit Insurance Corporation, Chair
McWilliams, and her view as to the future of banking,
what's going on right now. So that's why I grabbed
these three as an intro. If you've not yet
read them, I think go back, try to at
least skim them, get your sense of what they are.

And each class, I will
also list study questions. And the goal of my
listing study questions is not just for you to
think about these questions beforehand, but you
will also see where do I want to land the class? Where do I think these are the
central learning objectives? And this is usually in
a classroom setting, where I'll say "let's
pause here and I'll engage in some conversation." Now, I do cold call in
the regular classroom. I don't know if anybody wants to
raise their hand now and answer any one of these
questions, but it would be great to get a little
bit of life and community in this, if anybody
can address themselves.

What are the major technological
trends materially influencing finance right now
that you think about, whether it's in the US or
anywhere around the globe? And this course will be
taught from the perspective around the globe,
even though I'm more knowledgeable in the US. We will be talking about Europe,
Latin America, Asia throughout, a little bit about
Africa as well. Romain, I'm pausing
for you to do your– ROMAIN DE SAINT PERIER: We
have our first volunteer. Thank you very much, Luke. The floor is yours. AUDIENCE: I'll answer
the first question. The main technological
change that we see in US and outside of US is versus open
banking, use of a lot of APIs. They can be applicable
to other websites. GARY GENSLER: All
right, and we're going to spend a whole class
on OpenAPIs in two weeks. But this is an important
part of marketing, opening up the banks'
ledgers and their data.

And data, as people
would like to say, is sort of the new
oil in the business. It's very valuable for us. Anybody else, Romain? ROMAIN DE SAINT PERIER: Yes. AUDIENCE: The natural
language processing so that we can have the robotic advisors. GARY GENSLER: Right. So natural language
processing is the concept that you can take something
that's in human language and put it into machine
or computer language or go the vice versa. And it's not
actually new in 2020. Some form of natural
language processing has been around for decades,
just in terms of reading– reading computer code and
putting it into an audio voice or going backwards.

Or every postal service of a
major country around the globe has had something to read
our scribbled handwriting and trying to read
that handwriting and then put it into
something where they know which post box to send it to. But natural language processing,
we'll spend a fair bit of time, and robotics. Romain? ROMAIN DE SAINT
PERIER: And now we have Ivy, who raised her hand. AUDIENCE: Yeah, so I think
we've seen a lot of digitization in the e-commerce space
as well, especially in places like China. And you see this kind of
divergence between China and, say, the US and the
way we use mobile pay and the way that they've really
adopted like Alipay and WePay as well. GARY GENSLER: And
Ivy, why do you think it happened so, as
you say, at this divergence, why it happened maybe a
little faster in China? AUDIENCE: So I think it's pretty
interesting, because I think a place like China,
as an example, is probably less
developed in terms of even just their financial
structure, whereas the place like the US, it's
quite dominated.

And it's really competitive. But it's also
really consolidated. So you see these
countries where– I mean, I think the
way I think about it is the subway systems
in China or Taiwan or a lot of these
developing countries are much better because
they were just– they came a little bit later. And I just look at that
analogy similarly to kind of where payments are,
because you kind of go from 0 to 100 versus we
are kind of something– GARY GENSLER: No, I think
Ivy's raised a good point.

There's times when a
country is growing rapidly. And China, for instance, had
been growing at 8% to 10% GDP growth a year. And before corona, it had
come down to still a robust 6% a year. But within that
context, many things leapfrogged incumbents in
Europe and in North America. And in the payment space
in particular, two big tech companies– Alibaba, that really is the
dominant online retailing company, and Tencent, which
was the dominant online sort of social networking
and messaging company– leapfrogged the banking system,
the traditional banking system, and now with WeChat
Pay and Alipay control well over 90% of
retail payments, small dollar, and small and medium-sized
enterprise payments. They don't dominate
large wholesale payments, but put in the retail
space, absolutely. And I would agree with Ivy. They kind of leapfrogged us. But even Kenya leapfrogged us
with M-Pesa, a technology that was pushed forward by a
telephone company, Safaricom, when they noticed that folks
were trading mobile minutes as a form of money.

ROMAIN DE SAINT
PERIER: Gary, we have two more hands that are up. We can start with– GARY GENSLER: All
right, why don't we do those and then move on? So who are the two people? And they can just go in turn. ROMAIN DE SAINT PERIER:
So we had Laira, but she just disappeared. So we'll go with Alida. GARY GENSLER: All
right, one, thank you. AUDIENCE: Yeah, so I, to kind
of add on to Ivy's point, is a lot of financial
institutions in emerging markets did not typically
cater towards the mass-market consumer population. And so it really allowed very
quickly for these big tech companies to jump in a way that
you couldn't do so in the– in more developed markets,
where the financial institutions already were catering to the
large majority of the consumer population. GARY GENSLER:
Right, so it's about actual financial inclusion
and reaching out and so forth. So what I'm going to do today
is try to cover, in the minutes we have, a little bit
about the financial world.

What do we mean, FinTech
shaping the future of the financial world? What do I what do I think of it,
having spent my life– first, I was 18 years at Goldman Sachs. Then I worked in
the public sector, but always sort of around
finance, with the US Treasury Department, with Paul Sarbanes
doing Sarbanes-Oxley, and then later running a market
regulator, the Commodity Futures Trading Commission,
in the Obama administration. What do we mean by
the financial world? A little touch on FinTech– that's the whole
class, of course, but just a little
touch on FinTech. Thirdly, again,
just a little review of these three big trends– of AI, open banking,
blockchain technology– what do these trends mean? And then the actors– and the actors, I think that
some people will use the word FinTech to mean these
disruptors, companies like Toast getting into the
payment space for restaurants or Lending Club and peer-to-peer
lending or Robinhood, an app you can download
and trade stocks.

A lot of people constrain the
study and the topic of FinTech just to the disruptors. I think that that's too narrow. I think that we
really need to think of the actors and
the field, more broadly about the incumbents. This is sort of big
finance, we might say, the Barclays banks and the
JPMorgans and so forth. And we need to
think of big tech, as we just talked with Ivy
about Alibaba and Tencent getting into this business. But we see Apple Credit Card
and others and Facebook trying to stand up a world currency. And then it's the disruptors. So I think it's a much
more robust conversation and an important
conversation, the strategy amongst these three pieces. And then, of course, we've
got to do a little bit on our teaching team, our
schedule and assignments and so forth. So what do I think of is in the
financial world and what it is? Well, finance basically
stands, like this hourglass in the top-right-hand
corner, stands right at the neck of an
hourglass, intermediating, standing between people that
have money and need money, people that have risk and want
to get rid of it, lay it off, and somebody that
wants to pick it up.

And I have for decades,
since I was at Goldman Sachs, thought where we were, we were
at the neck of the hourglass. And for good or for
bad, that's also part of why finance
in many countries is able to collect
economic rents. Economic rents is that
classic conceptual framework of collecting profits
or revenues in excess of what classic
economics might tell you would be a competitive
supply and demand space. But if you stand at the
neck of an hourglass, between trillions of money
flowing from those who want it and those who have it,
and effectively trillions of risk between those who have
risk and want to lay it off and others who are
willing to hedge it– if you're at that neck of
the hourglass, so to speak, if you just collect
a few grains of sand for the trillions that go
by, it can collect a lot. In the United States,
for one, for instance, our financial sector
takes about 7 and 1/2% of our Gross Domestic Product.

Nowhere is it written it has to. In fact, in the 1950s and
'60s, it was more like 3 and 1/2% to 4%. But persistently, it's
grown as a percentage of our economy, standing,
intermediating money and risk. Let's just see if I
can get this to work. There, so the
functions, the functions are intermediating credit. That's lending. Investments, we all know that. Risk transformation–
think of any time one of us buys insurance on an automobile
or a car or on our life, but also risk transformation
that investment banks do, even between somebody that's issuing
stock and somebody that's buying stock. That's a transference
of risk in terms of whether that
startup will do well. Of course, there's
the capital markets. And at the center of
the capital markets is the price, the
money and risk that's flowing through the system. And there's plenty of
advice to go around. Now, we usually think
about it in sectors. And every one of these
sectors, whether it's commercial banking, asset
management, insurance, investment banking,
advisories, we will touch upon
during this semester.

And please, interrupt. If your keen interest is
about insurance companies or your keen interest is
about investment banks, then pull the community into
that in these discussions. But we're going to try to
talk about multiple sectors, multiple functions,
as contrasted to the half-semester consumer
finance course that was really just about one slice,
household lending, and largely the commercial banks and
investment banks around that. This is a much broader topic. And I hope the learning
objective is ultimately to understand how technology
can transform finance at any particular given time. Romain, are there any hands up? I'm sort of every once in a
while looking to you to see if I keep going or pause. ROMAIN DE SAINT PERIER: No
hands at the moment, Gary. GARY GENSLER: All right.

The financial world, in this,
I think, four key things to think about. And this is sort of in
a framework of thinking about financial technology. Data– of course, data is
that new oil, so to speak, for investing, for
market-making, for marketing, trying to get new customers. I mean, how many times
do we get a pop-up ads. I found in teaching
about student loans, I was researching student loans. And my god, the
last six weeks I've got more advertisement
for student loans, even now I'm a professor at MIT. It's because I was researching
the topic of student loans, and all of a sudden,
now I am getting a lot of unsolicited ads and
emails, even, on the topic. The financial
world it always has to think about the management
of their balance sheet. And if you're starting
a FinTech company, are you using your balance
sheet or somebody else's balance sheet? And just as there's
cloud computing, that today, cloud
computing has dramatically shifted the ability of
startups, a disruptor can come into a business and
basically the rent versus bill decision changes.

I can read somebody else's
data storage capacity. I can basically rent the cloud
instead of building my own data warehouse. That was a big change
about 15 years ago. And it's made startups
more viable in the 2020s. Also, the ability to raise money
in the capital markets, what's called securitizations, which
started decades ago, really took off by the 1990s, also
allows a startup like Lending Club and others to say I'll
raise my money elsewhere. I don't have to have
a balance sheet. And then there's the various
risks that you have to manage. And most importantly, we're
going to spend a lot of time doing this class on
this fourth point– user experience, user interface.

Much of what's happened
with mobile phones, with apps that you can download
for free have given all of us a better user experience
than online banking. Online banking has been
around for over 20 years. Most of the major
banks around the globe had a viable platform for
online banking by the naughts, whether it was 2005 for some
or 2008 or others or so forth. But by the naughts,
most had online banking. And yet, their user
experience wasn't what we all wanted, and certainly
wasn't what– maybe if the newer users,
as millennials, were coming into
the marketplace, to do it on your mobile
phone, to do it conveniently. So the user experience
and the user interface is the critical
competitive place. So what do we mean by FinTech? I like going back to that
2017 Financial Stability Board report. That doesn't mean that
they have it quite right. But they basically
talk about that it's technology-enabled innovation
in the financial world that's going to some new business
and it's material. So, as I mentioned earlier,
the telegraph in the 1830s comes along.

And by the 1860s
and 1870s, we're starting to see digital money. The first form of digital money
is already about 140 years old. I mean, we think we're living
in this digital age, and we are. And I would note this
about the corona crisis that we're living in– the corona crisis,
in my opinion, will accelerate
this digitization. We've already, in
the last 30 years, significantly
digitized our world.

And if we were all
together in a classroom, I would ask by a show
of hands how many of you have used paper money or
currency in the last two or three days. And maybe a quarter
of you might have said you had used paper money
in the last two or three days. But let me ask you in the
middle of the corona crisis– and Romain, you can tell
me if any hands go up– we'll you use the
blue hand, anybody that's used paper, physical
money in the last two days. ROMAIN DE SAINT PERIER: Thank
you, Alida, for volunteering. The floor is yours. AUDIENCE: I have for grocery
shopping and so forth. GARY GENSLER: So you actually– and they'll still take the
paper money behind the counter without wiping it down
with some cleaner? AUDIENCE: Yes. And I took out a lot of
cash before everything happened, so– GARY GENSLER: Ah, ah.

Alida, may I ask, was that
just sort of insurance, managing risk, as we say, as
part of finance, where you're– AUDIENCE: Yes. GARY GENSLER:
–managing the risk that the banking sector and the
ATMs and things might not work? AUDIENCE: Yes. GARY GENSLER: And do
I see Devin's hand? ROMAIN DE SAINT PERIER:
That is correct. AUDIENCE: I can only
do laundry using cash. So I use it for now, but– GARY GENSLER: But see how
few of us, of 80 plus people, two people. Now, we're in the middle
of a corona crisis. I would predict that as we come
out of this, whether this is– whether this is a matter
of a couple of few months or whether this
is, unfortunately, as long as couple of years,
wherever we come out, that the corona
crisis will accelerate an already existing trend.

And that already existing trend
distorts further and further digitalization of commerce,
that that laundromat that Devin goes to will take a
QR code or a swipe of a phone more readily. And yes, I think it was Anita
said that she took out cash before the crisis. And that's true. There is still going to be
some people that probably also went and bought gold. And we should respect
that, that that's sort of an insurance policy
against the digital world collapsing. ROMAIN DE SAINT
PERIER: Gary, it seems like Devin has a follow-up. GARY GENSLER: Yes, please. No, and maybe Devin
just left his hand up. AUDIENCE: I apol– GARY GENSLER: Maybe
after you speak, you actually then
take your hand down. AUDIENCE: All right. GARY GENSLER: So what are the
technologies of our times? What's sort of rolling with it
now, no longer the telegraph or so forth? Well, I put up my favorites.

And then I kind of
think, well, maybe I shouldn't have put
the cloud there. Maybe the cloud really
isn't a technology that's pushing us forward right now. And yet, what's interesting
is finance uses the cloud less than most industries. And this is particularly
the case of incumbents. Country by country– it doesn't
matter whether we're in Brazil, whether we're in Europe,
whether we're in the US– big incumbents, like
JPMorgan and Bank of America and Barclays, tend
to still have their own data centers.

Now, that's shifting. I think the 2020s will
see them shift over. But they've tended to want to
sort of control their destiny. I think they will shift because
it's lower cost and better cybersecurity generally
if you have it, frankly, at Baidu in China or Microsoft
or AWS and so forth here. But startups dramatically
use the cloud. This is this buy versus
build sort of scenario. Why build my own data center? I can rent it. I can use somebody else. But all the others we're
going to talk about– and I should say that
there's a little– a little sleight of hand by
my list, because many of these are actually artificial
intelligence and machine learning, which is part of
artificial intelligence. Natural language
processing, in essence, is artificial intelligence. Chatbots are sort of that
as well, and robotic process automation. But I split them
out because I think it's relevant that we kind
of have a chance to talk about each of these as we go. I'm pausing every once
in a while, Romain, but you can let me now.

I like to think of this
in the context of history. Maybe that's just because I've
always loved studying history. But what's the
customer interface that we've seen all the way
back from sort of prehistory, that the customer interface was
in tents or bricks and mortar later, all the way
up through where we got credit cards invented
in the 1940s and '50s? That was a FinTech. That was a new
financial technology. Visa, here in the
US, was a network of banks started by a California
bank called Bank of America.

And they wanted to have
a nationwide network. And other banks joined it. That became the Visa network. But where are we now? What are the
technologies now that– I want to make sure I'm
clicking on the right. So the base that you
might be thinking about– mobile payments, the internet,
even contactless cards– I would say that was
kind of a last phase. That was the phase that
really shifted finance in the naughts and early
teens, and that we really are in terms of
customer interface is now conversational
interfaces, chatbots and conversational interfaces.

That's the cutting edge. Yes, contactless
cards are important. Yes, of course, we
need mobile payments. No doubt about it. But if you're starting a
new business right now, you're going to
start a disruptor and say I'm going to do online
banking, that's yesterday. You have to find a way
to do that online banking in a way that has such a
better customer interface. And you might be using
some form of OpenAPIs or robotic process automation
and chatbots to do it. But these are all kind of
build up on each other. Then there's risk management. So one is the customer side. One is the funding and
risk management side. And you can look
throughout history. And I spent a lot of
my life in my career around the capital markets.

I was at Goldman
Sachs for 18 years. And I had to think
a lot with hundreds of other people
about risk management and what we were doing. And in the 1980s and
the 1990s, a big thing was asset-backed securitization
and interest rate swaps and even, yes, credit
default swaps that helped bring down several
economies in 2008. But those innovations
of the 1970s, the 1990s were dramatically
shifting, dramatically shifting what was going on. But I would say now, it's
really about machine learning. Machine learning is an ability
to extract correlations, extract patterns from data in
a way that we couldn't before. And many of you have
studied linear algebra. We're blessed in this class
to have remarkable science technology and math folks. Not all of you– we accept all
the English majors and history majors as well. But I'm saying that you know
that from linear algebra and basic statistics, you can
do a lot of regression analyses.

Machine learning is going
a little beyond that and extracting
patterns from that which was more difficult to
extract when you were just doing regression analysis. And so there's
patterns that we might see in the data that says you're
a good risk or a less good risk that you wouldn't quite say in
a traditional credit scoring systems. I think that's where
we're going right now.

Romain, anything
in the chat rooms? Or I'm going to
pause for a second. ROMAIN DE SAINT PERIER:
Nothing so far, Gary. GARY GENSLER: All right. So it's fertile ground. And we talked a little
bit about this before. But the fertile
ground of finance is we've basically digitized
money securities and credit over the last 30 years. The corona crisis is just going
to push that further faster, in my opinion.

We have this vast
amount of data. And we're starting to
see patterns in that data that we didn't recognize before. I mentioned this in the consumer
finance course that some of you were with me on, but somebody
has actually studied something like this to say
that if you charge your phone overnight
every night, if you charge your phone every
night, you apparently are a better credit
than if you don't charge your phone every night. You might think, oh
my gosh, I'd better be charging my phone
because Apple is watching. They're going to be watching
whether I charge my phone. And in China, there's
a whole social credit system that takes data from
many of your online apps. It's not just taking
your payment information from Alipay or WeChat Pay,
but that social credit system is checking even
your participation in dating websites. I kid you not. I kid you not. So that social credit
system in China or the private sector's approach
to collecting data in the West have sort of the
same goal in mind– to understand the customers
more and to basically provide more marketing,
but also to assess risk.

And, by the way,
the corona crisis might change even the
West's view of data sharing, because when we have Google
and the US partnering up to say we can track everybody to
see how this disease, how this disease is propagating
through society, that we need the modern technology of
our position location trackers. A position location tracker
is called a cell phone. A smartphone is
tracking where we are if you go running with
it, if you go driving, hiking, all of that data. But now here, even in
the US and in Europe, we're starting to
say do we partner, as the official
sector, with that data to keep our societies maybe
a little safer, and then, of course, the rapid
expansion of computation power and so forth. Now, I think it will
change something. This disruptive potential, I
think, has a dramatic change. We'll be talking about this
slide the whole half semester. But managing risks, updating
the customer user interfaces, and financial
inclusion are the three that I want to focus a
lot on, managing risk.

And AI is also about
targeting products as well. I think the interesting
challenge is, near the bottom, will some revenue models shift? We saw a company called Credit
Karma start here in the US. Credit Karma started basically
with the idea to get a free– free is the operative
word– a free credit score. The founders of Credit Karma
couldn't get their credit score from the traditional companies– TransUnion, Experian,
and the like. And so they started an app
just in the last 10 years. And in January, they sold
Credit Karma for $7 billion. Now, Credit Karma
is still a free app. And you might say how can Credit
Karma commercialize a free app? In 2019, there was $1 billion
in revenue for Credit Karma because they basically had built
files on 106 million Americans. They built data files on 106
million Americans providing a free app. And, by the way, they do not
have 106 million customers. They even built
credit files on people that weren't their customers. And they were able to
commercialize and build revenues around that data stream
and, in essence, referencing. They get referral fees when
somebody then does buy a loan or takes out a loan through
the Credit Karma platform.

ROMAIN DE SAINT PERIER:
Gary, we have a question from [INAUDIBLE]. GARY GENSLER: Please. You might need to
unmute yourself. We– ROMAIN DE SAINT PERIER:
So I am normally unmuting? AUDIENCE: I'm just– ROMAIN DE SAINT
PERIER: Yeah, OK. AUDIENCE: I'm just
wondering, what is the difference between
big tech and FinTech, and why it is important
preparing and learning the FinTech now? GARY GENSLER: So I
understand the second part of the question. You said the difference
between what and FinTech at the beginning? AUDIENCE: Big tech. GARY GENSLER: OK, big tech. So by that, I think of– I use the word FinTech in
the broad way the Financial Stability Board does. I use it as is the intersection
of finance and technology where the technology is new– so not the telephone,
not the internet, but it's something new, like
AI and machine learning, that may materially change
the provision of finance. So I use it to capture
the whole field. Why it's important is I think
it's important every decade.

I don't think it's
just important now, because I think technologies
will come along each half decade, each decade
that will materially change finance and provide the
entrepreneurs in this class an opportunity to break
into the wide margins. In the US, 7 and 1/2% of our
economy or $1 and 1/2 trillion is the revenues of finance. So if you're an entrepreneur,
you want part of that $1 and 1/2 trillion. You want an opportunity. And usually, it's
technology that's changing business models. When the internet came
along in the 1990s, that provided an opportunity for
PayPal to start and say maybe we can provide a better
payment solution. And subsequent
payment solutions, like Venmo and
TransferWise and Square, have all been opportunities
to chip away a little bit. I would say only in
the opportunity– the opportunities came
because technology was changing the field. What's the difference
between big tech and FinTech? Big tech, to me, are
big platform companies– in the US, the Facebooks and
the Amazons and the Apples, in China, Baidu, Alibaba,
Tencent, in Africa, even, Safaricom got in.

We could go country by country. India, the big tech has
gone into payments as well. I think that big tech companies
have dramatic advantages. And then I separate what I
would call FinTech startups or FinTech disruptors. I always put the second noun
in there, disruptor or startup, because to me, the word
FinTech is the whole field. I hope that helps. ROMAIN DE SAINT PERIER: And
we have another question from [INAUDIBLE]. GARY GENSLER: Please. AUDIENCE: Yeah, hi, Professor. One question that
I have is actually one concerned with privacy. At some point, do
you think that we would have up give up to some
extent our privacy in order to– for me to get a
credit from a startup or get a credit from somebody? GARY GENSLER: Well,
I think you've raised a dramatic issue
for society at large.

And finance is
one example of it. But yes, we have– we have shared our personal
data much more broadly in the last 10 years, and
certainly in the last 30 years, than we did in
societies before that. And in terms of getting
credit card, yes, we've been sharing data for 50
years through various credit– consumer credit companies. The Fair Isaac Company was
founded almost 60 years ago by two people out of
Stanford, actually, one named Fair and
one named Isaac.

And that led to something
called FICO scores, which are used in over 30
countries around the globe. These FICO scores took
some personal data, as to whether you were
paying your bills on time. But now, we can go beyond that
and capture alternative data. We can capture somebody's
digital footprint. And in China, they are doing
that with social credit scoring and Alibaba is with
Alipay and WeChat Pay. But Amazon is capturing
our data as well. Amazon captures any
Amazon Prime customer. And I'm sure amongst at of us,
there are many Amazon Prime customers. That data is being
captured in some way. Now, it leads to more
financial inclusion. But it also raises all sorts
of issues around privacy that we're grappling
with as societies. In Europe, they passed
something called the GDPR, Global Directive
on Privacy Regulation. Here in the US, only
California's stepped into this and passed something that
went into place last year, the California Consumer
Protection Act.

So these are things that
society will grapple with. Technology can enable privacy
as well as take away privacy. Technology actually can
enable us to keep our privacy. But the technology companies
and the financial companies want our data. So they're not going
to necessarily want us to keep our privacy. So it's an interesting– technology can enable
it, but technology can take it away as well.

So just moving on a little
bit, this three big areas– we're not going to
spend a lot of time, but artificial intelligence,
machine learning. I love to give a
shout out to another MITer, or Lex Fridman,
who has a wonderful set of online courses, if you
wanted to take Lex's courses. This was online before online
became such the vogue now that we're all doing it. But Lex has this
wonderful course. And I captured his one slide. What is AI and machine learning? It's extracting useful
patterns from data. You don't have to be
a computer scientist.

But it's basically that's
the key thing, and something that might not be
linear, something that might not fit into that old
statistics class and regression analysis or linear algebra
that we think about. It all relies on good
data, cleaning up data, and good questions. Where do we see it? We see it in facial recognition,
image classification, speech recognition, et cetera,
this list from Lex's list that you can see online,
medical diagnoses– in the midst of
the corona crisis, a lot are turning to AI
and machine learning to see what patterns can we see
beyond the patterns you can see by classic statistics? It's going beyond that. But in this field,
in this field, in finance, we're seeing it
in every one of these areas. And we have two classes, so
I won't dive into it now. But whether it's asset
management, where BlackRock is literally taking– and all the news items for the
top companies, every quarter that a company
reports its earnings, BlackRock is listening digitally
to their shareholder meetings and their shareholder
conference calls and seeing which words in there,
which words relate to stock markets going up
or stock markets going down, using machine
learning and asset management.

We talked about call centers
and chatbots and so forth. I don't know how many of you
are Bank of America customers. But bank of America has millions
of its customers using Erica. Think the Siri of banking. Think the Alexa of banking,
a virtual assistant called Erica– Bank of America, get it? All right, that was their
marketing thing, I guess, now. So AI and machine learning
is dramatically shifting. The question in FinTech
for the big incumbents, how do I do it to
raise my revenues, lower the amount of
capital that I have to use, raise my profits? The question for big tech
is how do I do this maybe to get into the business,
to leapfrog, as Ivy said, that Alibaba and WeChat Pay
leapfrogged the Chinese banks and payments? If I'm big tech, can I
leapfrog big finance? Because, frankly, if I'm Google,
I'm better at it right now.

Google has a
comparative advantage. Can I maybe use my comparative
advantage and AI and leapfrog? If I'm a startup, maybe I
can give a better customer interface. I can do something with
this to better manage risk with alternative data. So that's how I think of it. OpenAPI– I should pause. Romain, any questions
or hands up? ROMAIN DE SAINT PERIER:
Yes, we have one from Laira. GARY GENSLER: Please. AUDIENCE: Yeah, so I was
just curious to know how, internationally
speaking, regulation hampers the capacity of FinTech
companies to expand, just on an international level. So for the US, is it more
regulated and, hence, more harder for FinTech companies
to expand than for in China? GARY GENSLER: Great question. And again, who asked? I just didn't rem– is Leia? AUDIENCE: Laira. GARY GENSLER: Laira, all right,
Laira, good to see you again.

I'm sorry that I don't
physically see you. But good to see you remotely. Every country is taking a
little bit different approach. And the range of approaches
here could be from you're a– let's call them a
startup or a disruptor. You're starting something. You'd better just
come into whatever our traditional
regulatory framework. If you're taking deposits
and offering loans, you've got to be a bank. If you're just
doing payments, you might come under a European,
US African e-money law and just have to do the
things around money laundering and anti-money laundering. If you're like Robinhood
here in the US, you would need to register
as a broker-dealer. And there's been this big
debate around cryptocurrencies. Are they securities or not? And in some countries,
like the US, they generally
are, unless you're like Bitcoin and Ethereum. But the debate, Lyra, is
really country by country, is are these startups
and these technologies, do they fit into the
current regulatory regimes? By and large, if you take
deposits and you make loans, you're a bank pretty much
anywhere around the globe.

If you facilitate the
movement of money, you're probably in some
e-money laws around the globe. Securities, if you're
actually facilitating the raising of money and
the selling of securities, you usually have to register
as a securities broker-dealer somewhere around the globe. But a lot of places
also have this concept of some form of regulatory
forbearance called sandboxes. The idea is let's promote
some innovation, whether it's in Hong Kong, whether it's in
Asia, whether it's in the US, promote some motivation by
saying if it stays small enough and it's new enough,
you might not have to comply with all
of the regulatory regimes.

The other interesting challenge
is sometimes things come along that don't fit in a box. They don't quite
fit in to something. So the internet came
along in the 1990s. Internet in the 1990s
was facilitating a rapid change in finance. The internet comes
along and then the question is
literally a question that the securities
regulators around the globe had to deal with– what if I put a
bulletin board up on the internet that offers
people to buy and sell securities on the internet? Now, it wasn't a
traditional exchange. It didn't look like the Tokyo or
Shanghai or London or New York exchanges of old, where
there were humans yelling and screaming on the
floors of stock exchanges around the globe. It was just a
bulletin board where buyers and sellers could meet. And usually they were insurance
companies and various asset managers.

That question was a ripe
question in the 1990s. And over time, we ended up
with two tiers of regulation for exchanges. We had fully-regulated
exchanges, and this was true in Europe
and the US at the time. China sort of got
there a little later. But in Europe and the US,
it was like all right, there's going to be these
fully-regulated traditional exchanges. And then there would
be another tier. In the US, we called
them broker dealers, alternative trading
systems, ATS's. In Europe, there
were various rules that became known as MiFID,
which now I can't remember what it all stands for. But electronic trading platforms
were regulated a little differently. So I hope that gives
you a sense and puts it in a historic concept. I think over the
2020s, AI and machine learning will lead to
tremendous challenges around regulation, about if
you see a pattern in the data but you can't explain
why the pattern is there, you fall into some
challenges of explainability.

And for the last 50 years,
or in many countries, if you deny somebody
credit, you're supposed to be able to explain
why you deny them credit. We talked about privacy earlier. It bumps up against
privacy issues. And a third area it bumps
up against is biases. What if there is a
bias in the data, like when Apple Credit
Card just rolled out and it seemed like husbands were
getting more credit than wives in the same household? So biases, privacy,
explainability are the three sort of cutting
edge, when I call the big three public policy issues around
AI and finance, though there are other issues as well. Romain, did I see
you– were you– ROMAIN DE SAINT
PERIER: Yes, sir. We have Carlos, who
has his hand up. GARY GENSLER: All
right, and then I'm going to keep
going, because I want to talk about where we're
going in this class as well. Carlos? AUDIENCE: Hi, how are you? Just a comment on regulations,
sort of to build up on that. So I think ironically,
for example, in Latin America,
a lot of countries have an issue where
the big banks have a massive concentration
of deposits.

But, for example, if you look
at the Mexico FinTech law, which was passed end of 2018, it
actually raised the barriers to entry for other FinTechs. So sort of ironically– GARY GENSLER: I'm sorry, Carlos,
it raised what for FinTechs? AUDIENCE: The barriers to entry. GARY GENSLER:
Barriers to entry, OK. AUDIENCE: And so– GARY GENSLER: I think our
faculty member, Luis Videgaray, who helped work on that when he
was finance minister of Mexico, we should ask him. And I'll see if I can get his
answer for the next Wednesday or next Monday's class. But keep going. AUDIENCE: OK, that
would be great. But the question is
do you think there is a risk that new regulations
in the FinTech scope are actually going to
perpetuate some of the problems that we saw before with the
more traditional banking sector? GARY GENSLER: Great question.

Carlos, can I hold
that for a minute, because I'm going to do
that when I do the actors? But I think yes,
incumbents in every field– and these would be incumbents
in the pharmaceutical field, in the tech fields, in
airlines, whatever– incumbents tend to be able to deal with
regulation a little easier. They're big. They've got great resources. They can build systems to
comply with those regulations. Now, they don't necessarily
embrace new regulation. But once those regulations
are put in place by an official sector,
they tend to have the resources to embrace them. And startups sometimes
have more challenges. And thus, you may be
seeing that regulations become a barrier to entry. They're kind of grains in the
sand of innovation at times. So there's always a
public policy tradeoff– protecting the
public, whether it's protecting the public against
consumer fraud or investor protection or protecting the
public against systemic risk, that big banks will fail and
hurt the rest of the economy, also comes with some
tradeoffs, that it could raise the barriers to entry.

You're absolutely right there. We're going to talk a lot
about OpenAPI and open banking. We have a whole class on that. So I'm going to keep moving
on just so that we finish by our 10 o'clock deadline. Blockchain technology–
you've heard about it. We're going to have
a class on this, about cryptocurrencies
and blockchains. Some of you actually were in
our fall blockchain and money class.

Some of you are in the
crypto finance class that starts in about 30 minutes. But we will talk about
blockchain technology. I want to say and
lay it out right from the beginning,
these two issues, AI and machine learning, in these
eight areas are dramatically more relevant. And OpenAPI and open banking,
dramatically more relevant than blockchain technology
potential use cases as of 2020. The interesting question
is will that shift? Is there an overabundance of
investment in AI and machine learning and not enough
in blockchain technology? You get to decide
for yourselves. But I'm saying as of 2020,
sort of the real potential that we're seeing and the
dramatic changes around user interface an OpenAPI and machine
learning and natural language processing are more
dramatic in this space.

And yet cryptocurrencies
have dramatically changed what central
banks are doing. And we saw Facebook trying to
stand up a worldwide currency last year. So I don't think
you can adequately discuss and have a course
on financial technology without really having some
slice of blockchain technology. And it is shifting. Everything that's on this list,
it is a catalyst for change. I would say my takeaway
on blockchain technology, it is definitely pushing
the financial sector in places that wouldn't
be pushed otherwise. I guess that's
really saying OpenAPI and artificial intelligence,
machine learning are so much bigger in terms
of what's happening in 2020.

Romain, unless
there's a question, I'm going to do
the actors quickly. Anything? ROMAIN DE SAINT PERIER: We
have one specific question from [INAUDIBLE] on whether
machine learning and AI can cause a dark box, or creditors
could deny and approve credits based on unreasonable grounds. How do you see that? GARY GENSLER: That
absolutely is a risk. Our first big data
revolution in the US, and then it was about a
decade later elsewhere, was in the late
'50s and early '60s, credit cards came
along, invented really in the late 1940s and then
popularized by the mid 1960s, came along. And then the official
sector passed laws. And two of the first
laws we passed in the US was something called the
Fair Credit Reporting Act and the Equal
Credit Opportunity Act. And why I speak about
those two and this question 50 years later is
this was a question with the first big data
analytics at that time. And the idea was you couldn't–
you couldn't use data analytics to deny somebody credit
because of their race, because of their color,
because their ethnicity, because of their gender and
other protected attributes.

And that was called the
Equal Credit Opportunity Act. That same act is important
now as we move into machine learning credit decisions. Fair Credit Reporting
Act also said in the US, and Europe did some similar
things elsewhere later, said if you deny credit, you
have to be able to explain why. So to this question, just
because you have a black box, you still need to have those
basic tenets of explainability and fairness or lack of bias. And that's why I say the
three big challenges are explainability, bias,
and then privacy. There's also challenges of
robustness and so forth, but great question. The actors– I think
of the actors– we've talked about this a
little bit in several buckets.

I think of big finance– I apologize, I sort of
borrowed this a little bit from the central bank
governor of Brazil. I met with him last year. And he and I were talking
about Brazilian banking. And he said they're
like fortresses. So this was actually his
kind of articulation of Ita├║ and the others in Brazil. And I said how do you
see them as fortresses? And he says they all
have their moats, towers, and they have
sovereign protection. And to him– and I liked
it so much I repeat it– their towers, their
sort of basic tenets of sort of financial
strength is around payments. They usually control payments. They have big balance
sheets that they can use. And balance sheets allow you
to lower your risk, frankly, and leverage.

They have a lot of data. And yes, they have
hundreds of legal entities. Their corporate structure is
one of their both complexities but benefits. A company like a JPMorgan
or a Goldman Sachs or Barclays at a minimum has
probably 1,000 legal entities and might have 3,000 or
5,000 legal entities. When I left Goldman Sachs,
which was already 22 years ago, I was the co-head of finance
with David Viniar, who went on to be the CFO, we
had 700 legal entities.

But if I recall, in that quarter
of a trillion balance sheet that we had to sort of
help manage and fund, only about 70 or 80 of those
were regulated companies. So we had a lot– we did everything legal,
I just want to say– but we had a lot going
on amongst those 700 legal entities. That's kind of big finance
from a sort of central bank governor of Brazil's point
of view, like fortresses. But then big tech– the Bank of
International Settlements did a wonderful
report last year. And they said it's like DNA– data, networks, activities. And if you're Alibaba
or you're Facebook, you want to layer another
activity on your network.

Facebook already has
2 plus billion people in their network. They have a lot of data already. They are supposedly a free app. They are free if
you download it. But it's data for services. And if they can put another
activity on top of it, that means they get more data. So every time they add
an activity, more data. And data they can commercialize. And so that DNA network is
why you see big tech trying to get in payments
around the globe, and then adding credit
products on top of it. Startups– startups
have advantages. Don't count them out. Some people would just
call that the FinTech. But they're flexible. They're disruptive innovators. They can sort of rent their
data storage on the cloud. In some circumstances, they
can rent their balance sheets by doing securitizations.

They also have some
asymmetric risks. And that asymmetric risk we'll
talk about all half semester. The important asymmetries
they have is one, they're not protecting
a business model. So let's just talk about
payments and credit cards for a minute. The big banks are protecting
a very profitable credit card business. And there's seven
big banks in the US. There's seven big actors
in the credit card space– Bank of America and Chase
and Citi, of course, but also American Express and
Discover and the like, Cap One. They're protecting
that business model. But then somebody comes along. Maybe it's a small company like
Toast in the payment system space for restaurant payments.

And before corona crisis,
Toast was doing pretty well. And they did a C or
series C or D round at $4.9 billion valuation. Well, Toast can come along
and provide a payment product for the restaurant business. They're not protecting
any business model. Or even Lending Club
that came along 10 or 11 years ago can come
into the personal loan space and say we're not
protecting wider profit margins and interest rate
margins in the credit card space. You can come into the
personal lending space, which is growing dramatically. Personal lending in the US is
about $170 billion asset class. Credit cards is $1 trillion. So it's only one sixth the size,
but the personal loan space is growing rapidly, in
part because those actors in the disruptive
startup space are not protecting the trillion-dollar
asset class, which is called credit cards. And then there's
the official sector.

So I think of these actors
as, importantly, all of them. And just some pictures,
just for fun– we don't need to
stop, but big finance. And, of course, I
left companies out. But to give you a sense,
it's an asset management, like BlackRock and
Fidelity and Vanguard. It's in banking. It's in investment banking. It's global. If I left your country or
your favorite company out, I apologize.

But I could have put 200,
500 companies on this page. But then there's also
big tech, which I only picked six or seven at the top. And then the
startups– and we're going to talk about startups
in every one of our classes. But I think you've got
to sort of bear with me and think about it
more broadly as well.

And then I'm just going
to close before we talk about our actual
course and so forth, is where's the investments? Accenture puts out
this wonderful report, I think on a quarterly
basis, as to the number of deals in different
sectors and then the funding. And I don't ask you to study
this on your screen now. But think about it. Maybe pull up the
Accenture report itself. But the big bulk of it is
in payments and credit. If you look at the kind of
purplish blue boxes, nearly 50% of the funding is
in those boxes. Insurance, pretty
good size, too.

But it gives you the sectors
that actual funding is going on in this marketplace. Romain, questions? AUDIENCE: No questions. GARY GENSLER: My god,
Romain, where did you get this picture taken
anyway that I grabbed off the internet? ROMAIN DE SAINT PERIER:
That was when I was working in the Middle East for BCG. GARY GENSLER: I see, I see. All right, so you've met Romain. You've met myself. Lena is the course
administrator. If you want to set up
office hours– and, yes, I am committed to office hours– it's great to also copy Lena. You can probably
do it with me, too. But Lena is going to be
better to sign it up as well. The course– so
after this intro, we're going to take
the technologies. We're going to take two classes
on artificial intelligence, machine learning, natural
language processing and the like, and then talk
about the customer interface on April 8 and then
blockchain and technology. These three slices– now,
there are other slices we could address as well.

So then we're going to
go through the sectors. We're going to talk
about these sectors. And what I could see is payment
and credit, trading, a little less on the risk management. So maybe on May 4, we'll squeeze
that down a little bit as well. And then, of course,
we have the intro. I've just, [INAUDIBLE] and
I, revised the syllabus this past week. I want to take the
next-to-last class and just talk
about corona crisis and how it might be
shifting the landscape. I already said, I really
do think this shifting, this deep trend towards
online from bricks and mortar, that was already happening. But we've even seen in
the last three weeks, we've seen winners and losers. I asked each of you
to think about this. Who are the winners and losers
within the financial sector, big tech, and the
financial startups? I talked a little
bit about Toast. Toast is a very
successful Boston company that is providing
payment services and credit to restaurants.

For a moment, that would
have to– you'd have to say that's a company that's taking– taking it on the chin,
so to speak, not as badly as all those individuals that
have health care worries, that are ending up in the
hospitals and the families losing loved ones. But I'm saying from an
economic perspective, there are some
winners and losers. Robinhood, an online app,
mobile app for trading, has crashed several times
in the last three weeks. And there's some
data out of Europe already that online FinTech
apps as a sector have seen usage up 70% and 100%,
and some apps up 700%. But not all will be winners. Not all will be losers. And so I thought we'll take
one class towards the end and just discuss it and get
your feelings and thoughts as well going forward
how this might play out.

So MIT chose this as pass-fail. So welcome to not only
remote learning MIT, but technically pass
emergency, no-credit emergency, incomplete emergency. Just so that you understand what
this is, it's almost pass-fail. Pass emergency, PE, will
be on your transcript, hopefully for all of you. I can't commit to it,
because it's up to you whether you pass. There are assignments. And no credit emergency,
you can think of is an F, but it's not going to
be on your transcript.

So this is an emergency
circumstance right now. There's still
assignments because we want to give you
the most learning experience you can have. You might say wait a minute,
wait a minute, I thought– I thought assignments were
just about so that a faculty member can decide who gets A's
and who gets B's and the like. I look at assignments in
a different way than that. I look at assignments
really as a way to help you engage
in this subject. And so in this class– and those of you
who know me, I do this in a couple of classes– is
one individual paper, one group paper. And it's all geared
to writing a group paper for either a big
incumbent, Bank of America, a big tech company, Jeff
Bezos and Amazon, or kind of a startup company, a big VC
company, Andreesen Horowitz, that you form groups and
you decide on a sector.

You can decide on credit
or payment or trading. You choose, and it helps
you engage in the subject. And then I ask you to split,
if it's a three or four-person team, or even if it's
a one-person team, if you choose to do that,
because we're all so separated, then split up and write a
three or four page paper, 900 words or so, on one of the
topics that I lay out here– the traditional competitors,
the startup competitors and so forth, the technology
that you're interested in. Why do I still do
assignments when it's PE NE? It's to help you
engage in this subject. And Romain and I are committed,
even if we slap a PE on most of these– hopefully
all of them– we're trying to
give you feedback so you engage in a subject. And yes, I'm willing to do
Zoom group meetings, Zoom individual meetings. Look, this is not an
easy time for any of us. But I want to make sure that
I deliver as much as I can and that MIT
continues to deliver for you as we're going through
this sort of challenging time.

Class participation
is still important. If you can't sign up, if it's
the time zone doesn't work or there's something going on
in your family, it doesn't– if you're interviewing
for a job, God bless, then listen to the recording. We'll put the recordings
up on Canvas as well. And so professionalism, I
just want to say something. ROMAIN DE SAINT PERIER: Gary? GARY GENSLER: Yeah? ROMAIN DE SAINT PERIER: Excuse
me, just on the assignments, I'm being asked
whether listeners also have to comply with
these assignments. GARY GENSLER: No. ROMAIN DE SAINT
PERIER: Thank you. GARY GENSLER: I'm trying to
get rid of the poll here. OK. I've never been
asked that question.

A little word on
professionalism, just for my, God knows,
30 plus years in business. And this is just sort
of my closing thing, is my advice for
all of us is success goes for those prepared,
curious, and self-starters. If you read the
assignments for this, you'll do better in this class. But if you go into a meeting,
if you go into a pitch, you go into an interview, if
you read about the person, you're going to learn
more about them.

And, by the way,
if you're curious, when you walk into
somebody's office, whether it's a video office or a
real office, look at the walls. What does it mean
that Gary Gensler has this stuff behind him or not? I'm not asking you to
analyze me right now. But I'm telling you, if you
walk into somebody's office, whether they're a US senator,
the president of the United States, some job interview,
a colleague, and you look at their walls and you ask
them about their families, show some curiosity. You'll do better off. As I said, respect and courtesy
builds reputation and networks and so forth. Engage. It's going to be hard with
this many students online, but engage in this class. You'll learn more if
you engage with me also offline, Gensler@MIT.edu,
but also engage by setting up office time. I also think understanding both
strategy and detail matter. Some people are really
good tactical people, really good detail folks. They'll do fine
in their careers. But if you step back and
understand the trends as well, you'll do better. Some people are really
global strategists and not really good
at the details.

They'll probably do OK. But I'll tell you,
my experience, whether it's in finance,
whether it's watching people at MIT and the faculty, or
my time in public service, the people that can
merge both broad strategy and can execute on the
details, those folks are unstoppable often. Those are folks that
you really– you want them on your team, that
they can do a bit of both. So we're going to talk a lot
about strategy and the trends. But we're going to get into
the granular details as well. And then lastly,
it's always better to stay true to your values.

Now, I do say this– it's going to be a little– we're in this pass-fail thing. But if you want to know
one way that Romain and I, it will drive us a
little nutty, we're going to try to put your
papers through some plagiarism software. I use Grammarly. I actually check, yes. And if you have one like
eight or ten-word section that you pulled
off of Wikipedia, and I've had students grab the
first 15 words off of Wikipedia and put it right in
their paper, that's kind of a sloppy thing to do. And usually, if I was
grading, that paper would get like a C or a D and
it wouldn't get an A or a B. And I'm not going to be a
hard-nosed guy about 10 words. But if you extensively
plagiarize a couple hundred words in a 900-word
paper, you're going to get an F on the paper.

Now, you can recover
on the group paper. But don't plagiarize
on your group paper because you're also going to
bring down your colleagues. I don't know in pass-fail
land what I will do. But if somebody really is
trying to test the limit, you would test it by
extensive plagiarism. Enough on that. I've had it in the
past occasionally. So I got to say it. I would say speak up
in class if you can. Keep your videos on,
as most of you have. Keep your audios muted. But please speak up. I mean, don't be
hesitant at all. And then I do have office hours.

And the crazy thing was
I set up office lunches. So on those following Tuesdays
and Thursdays, I had– there's a spreadsheet. Romain, maybe we should send
it around to this group. There's a Google Spreadsheet
if you wanted to sign up. And right before we left, before
SIP week, a student said hey, I'm signed up for the 31. Will you still do the lunches? I laughed. I said listen, I'm
willing to do it. I'm willing to do
remote lunches. It's crazy. So if you want it– if
nobody signs up, I'm fine. I'll go for a run. I live in Baltimore and
I'm still able to run here. They might shut that down,
too, at some point in time. But for now, I'm
able to do my runs. So I think that's
it on the slides. And then I think I'm supposed
to finish this class right now.

I went over. I want to thank you all– ROMAIN DE SAINT PERIER:
Maybe just one– GARY GENSLER: What's that? ROMAIN DE SAINT PERIER:
Just one clarification, because I'm getting a lot
of questions on the group formation. So groups will be from
three to four students, and you are supposed
to group yourselves, right, through the
Canvas function. If that doesn't
work out for you, we're going to send
out a link where you can find other
team members who also are looking for team members. So do not worry about
the group formation. GARY GENSLER: And
I thank you all. I know this is unusual. I see you on Wednesday
morning, 8:30 AM. And please stay
safe and be well.

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