Our primary goal is to help you to understand FinTech and to become more confident and persuasive in your ability to analyze and make recommendations to executives within the finance industry regarding how to react to these changes. This FIRST MOOC ON FINTECH IN ASIA-PACIFIC offered by HKUST presents the views of several professors from the top business school in Asia as well as perspectives from industry professionals. HKUST has been ranked for many years as the number one EMBA program in the world, as the number one Finance program in Asia, and as the top MBA program in Asia by multiple independent rating and review journals and surveys. This course offers the combined strengths of HKUST in Business, Finance, and Technology as one the world's leading academic institutions on an important area of technology and business innovations. For students from outside Asia, this also gives an insight into changes happening in the most advanced FinTech markets of the world, since Asia leads worldwide in FinTech adoption and creative innovations. This course (FinTech Foundations and Overview) is part of the HKUST FinTech specialization, there will be 3 other courses in the specialization including FinTech Security & Regulation (RegTech), FinTech Risk Management, FinTech Innovative Disruption: Implications for Society. The FinTech specialization will be launched in Fall 2018, stay tuned!
3.8: AI, DSS, and Automation (Part 1)
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Из курса от партнера The Hong Kong University of Science and Technology
FinTech Foundations and Overview
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The Tech of FinTech
The Technology enabling and supporting FinTech is important to understand, and this module provides a brief introduction to each of the areas of technology that enable FinTech business changes, including identity and privacy technologies, blockchain and encryption, big data analytics, AI and automation, and consumer tech innovations. This introduction is designed to be at a level appropriate for business managers or students of business, and is not intended to excite or engage tech geeks.
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Theodore Henry King CLARKAssociate Professor
Information Systems, Business Statistics and Operations Management Department
Welcome back to our course on FinTech, foundations and overview.
In this session, talking about FinTech technologies, we're going to look at AI,
which is artificial intelligence, DSS, decision support systems,
and automation, various types of automation,
including artificial intelligence enabled automation.
And as we look at this, these matters for FinTech,
we have with us Professor Hilton Chan, Adjunct Professor at HKUST,
and CEO of his own company in the FinTech space.
As you think about artificial intelligence,
decision support systems, and automation as technologies,
how do these technologies influence the developments of FinTech markets?
>> Yeah, in fact, a lot of these technology tools have
been used in the past in different industry.
It's just that finance and banking is a regulated industry.
And in the past, data is not available.
When data is not available, even though I have the best AI engine, without the data,
right, the engine won't function.
It's like a car without gas, right?
>> Right. >> And now with so many open-source data
and also with a lot of data cleaning tools, a lot of data and tools,
I'll be able to sort out the data in a way that I could fit it into the AI engine.
So that an AI engine could do the processing,
to come up with something that has value, could help me to do decision-makings.
Or they could make the decision themselves.
>> Does increased computing power play a role in AI and DSS and automation?
>> Yeah, definitely, definitely.
The hardware improvement and also the memory capacity increased
in past years actually provided the infrastructure for AI and DSS.
>> Now I've been around awhile.
When I started learning about computer science as a young man,
we used punch cards.
>> [LAUGH] >> And we had mag tapes, and
this was kind of a long time ago.
>> Yeah. >> So I'm revealing my age a bit.
But I was also, I was young when I started college, I started at 16, so.
>> Yeah, we did that in primary school, right?
>> Yeah, I was using punch cards in high school when I was 12 and 13.
So I don't know, we did some advanced things.
And both my parents worked for IBM at the time, and so you know,
grew up with technology.
But we were talking about AI back in the 1970s.
>> Yes.
>> We were excited about AI and we thought it would change the world.
But nothing happened.
It was much to do about nothing.
>> Yeah, I think a lot of the machinery in AI, firstly,
is a lot of those models at those days, it depends on computing power.
>> Right.
>> If the computing power is not ready, it will take you weeks.
>> Yeah.
>> [LAUGH] Months, to come up with results, especially in the finance market.
>> We used to say- >> What would [CROSSTALK] is
that if you tell me the market is going to increase two days later?
>> Yeah, we used to say we could predict the weather tomorrow.
>> Yeah. >> The only problem is it would take two
days to run the analysis.
>> Right.
>> So you would know the weather yesterday, as predicted from two days ago.
>> Yeah, exactly, exactly.
>> Yeah, thanks.
>> Yeah. >> But what's happened is computing has
increased in power by doubling every 18 months or so.
So what used to take 2 days now takes one day, then half a day, then 15 minutes.
>> Right, and also it's getting your smart phone, which is your personal device.
You don't need to.
>> You've got a super computer in your hand.
>> In your hand, right?
Some people even saying that is part of you, electronic organ, right?
[LAUGH] So it is part of you that is next to you that helps you to make decisions,
right, and do things, right?
>> So when you get a new phone, that's like a transplant?
[LAUGH] >> [LAUGH] So basically,
the interesting part.
And also, the other part is, even though you have computing power, but
let's say you don't got field.
Data is the field.
So when we have open-source data with tons of data, right?
>> So we didn't have enough data then.
>> Yeah, in the old days, think about it.
There's no Internet, no web, and
then the only data we could get is through EDI as well, electronic data interchange.
There's no APIs.
>> Yeah, yeah.
>> So things are not in real-time.
So things are very slow, and then it's a download,
it's a backup thing that- >> So what's happened?
We've got more computing power.
What's happened?
What's different?
What's new?
What's amazing about AI today?
>> Now, I think the technology, the computing model,
a lot of the mathematical algorithm already exists in there.
Now actually, we could implement it.
And then we have the data available so
it would help us to make better decisions than before.
For example, let's say if I built a robo trader, an algorithm.
Now, in the old days even if I could come up with an algorithm,
I may not be able to get the data from the exchange instantaneously through the APIs.
And I don't have the computing power, I don't have the storage space.
And then a lot of these, then even though after let's say, obtaining those data,
doing data conversion, and then putting it into the machine, crunch it, and
then get the result out, then it's gone.
The thing's already happened, right?
But now it's not.
I could really get it up to speed.
I could do the prediction kind of before the market actually happen, right?
So- >> Let me mention something I find
fascinating about AI.
because I've worked with expert systems of variation of decision support
many years ago.
I've worked with AI and people said, you know,
we'll be able to get chess right because it's a brute force game.
And we can do all the analytics.
But Go, the game with black and white pebbles that's popular in Japan and
Korea, sometimes in China, we'd never be able to do that with a computer.
Because it's too complicated with too many variables.
And yet recently, AlphaGo beat the world's best chess players.
>> Yeah. >> I mean, the Go players, in the world.
And that was pretty amazing.
And then, AlphaGo 2, the next generation of that, without any programming,
self-taught, self-learning, within less than a year and
a half, was able to beat AlphaGo, hands down.
>> Right.
>> In almost no time at all.
It learned itself.
That's new.
>> Yeah, okay, think of, okay,
I can even propose something called a very stupid algorithm.
But still be able to beat a human, for example.
If I've enough computing power, I have enough storage, and
my IO time is fast enough, basically, I store all the possible moves.
>> Right. >> I store,
just store all the possible moves.
Whenever you make a move in my database, I would have to sequential three or
four steps after that, then I just saw everything.
>> But the interesting thing about AlphaGo is it doesn't do it that way.
[CROSSTALK] >> They are much, much smarter.
>> It's much more sophisticated and much more adaptive in its ability.
[CROSSTALK] >> In fact,
a Korean master who was beaten by AlphaGo said that it cheats.
>> [LAUGH] >> It doesn't play the way normal
people do.
It doesn't play like a human being.
It changes the rules of the game.
And you can teach AI now almost any game, including stock trading.
>> Yes. >> We got a mathematical genius that
developed algo trading models, got himself incredibly rich.
>> Right, right.
>> Doing that, using better decision support systems for better trading.
And that was proof of mathematics and
programmable trading beating the best hedge fund managers in the world.
>> Yeah, people use AI and algorithm in horse betting.
>> Yeah. >> In sports betting.
>> Right.
>> In a lot of areas.
Even though people used to be doing weather forecasting,
they just turn it into why not stock market forecasting, right?
>> Why not?
>> Yeah, so. >> If you've got the data,
might as well do the analytics.
>> Can't tell you.
>> What about automation?
I mean, we've been automating things for a long time, since 1950s in finance.
We automated check cashing.
We automated credit cards, ATMs.
Described by Paul Hooker as one of the biggest
FinTech applications in the history of banking, it's automation.
It's automation of bank tellers.
What's being automated today using these AI tools or DSS, which is new in finance?
>> Yeah, I think in the past, right, a lot of the automation,
a lot of the so-called computerizations,
they're all focused either the back end or the middle office of a bank.
So it helps banks to improve their operational efficiencies.
Now, nowadays, right, I mean, we are talking about a lot of the front end,
end user computing, and the user want to get control of the banking data.
Now of course, the money that I deposited in the bank is definitely mine, right,
I want to have control of it.
But in the past, people don't realize that actually by spending your credit card,
okay, by doing things online, I leave a digital trail.
Now, those digital footprint, does it belong to you or me, right?
Does it belong to the Internet service provider [CROSSTALK]?
>> So you're saying this is empowering end users by automating their
[CROSSTALK] banking systems.
>> Even the banks realize that if they could capture all the digital footprint of
all their customers, then they may be able to understand the customer better
than the customer himself, right?
>> Let me share something I find interesting about automation.
When we talk about automation in the old days, the back end,
the things you described, who's losing their job due to the computer?
Who's getting fired?
>> It's basically the bank.
The staff in the bank, right?
>> Which staff in the bank?
The VP?
>> No, no, it's the one- >> Clerks.
>> The clerks, yeah.
The clerks- >> The people that don't make much money.
>> Yeah, the reception, yeah.
>> Okay. >> Or maybe some middle management, right?
>> Maybe a few.
>> Yeah, a few.
>> Who's getting replaced when we are automating our mutual funds?
When we're automating our hedge funds?
When we're automating our investments?
Who is Goldman Sachs laying off today?
>> Yeah, fund managers, right?
>> How much do they make?
>> Wow.
>> A lot, right?
>> Much more than you and I.
>> Yeah.
>> [LAUGH] We're professors.
>> These are people making $1 million plus a year losing their jobs because of
automation.
This is different from a bank teller losing his or her job.
>> Right, right.
>> And so we are now seeing automation not just in banks and other industries as
well, but more and more high value workers are beginning to be replaced by systems
which are doing things that we always used to think only a human could do.
>> Right, right.
>> Only a human can fix that.
It's only a human who can do trading.
>> Right.
>> Goldman Sachs laying off hundreds of traders and
hiring many hundreds more programmers.
>> Programmers, yeah, yeah.
>> So a lot of jobs for IT people, a lot less jobs for people who used to
think they could make good decisions about finance or placing their bets.
We don't need you to place the bets anymore.
We'll place the bets on the horse ourself, or
the stock ourselves using the algo models.
>> Yep, yeah.
>> That's interesting.
That's a new type of automation that reduces cost, but
some of it is a very high salary people.
>> So that means that even people at the top are being threatened now, right?
>> Yeah, yeah. >> Because of being used to be
they are sitting at the top because they are basically generating revenue for
the company.
>> Yeah. >> Now people consider that
a robot is doing better than me.
>> Yeah. >> A robot could be smarter than
me, right?
>> Robo traders beating the best hedge fund managers.
>> Yeah, yeah.
>> Wow, you know.
And people like Warren Buffet who's really a great stock picker are saying,
you know what, for most of my investments, I'd use an index fund.
That's impressive, you say, give me a robot.
>> [LAUGH] >> It'll do better than most of the humans
who are advising you.
>> Yeah, yeah, but also, I mean,
some people will take it from another perspective.
Let's say, if there's more robots and everybody is using robots,
in the end, would human being be able to advance, to manage those, right?
>> Right. >> Or they actually,
they are the designer of those algorithms, right?
I mean, they also catch up as well, right?
Or instead of some of the fiction movies say that a robot is controlling
human, right?
So now that's something I think that's still arguable.
I personally believe that despite that there's a lot of robots,
a lot of algorithm around, I think human are still smart enough
to be able to pull- >> Well, in the bottom end of the day,
these robots are programmed by people.
>> Right, right.
>> And you cannot delegate your stock choices or
your decisions to a computer science guy who doesn't understand finance.
>> Right.
>> We still need people who know finance and finance markets.
>> Right, right.
>> We need people who know finance, who know lending, who know banking,
who know insurance, and programming.
>> Right.
>> Because we want to automate the best experts and layoff the mediocre ones.
>> Correct, because I think human at the end of the day, you see even today,
right, a lot of these systems, they have backdoors.
They have backdoors, 15.
Now- >> Which can be risky.
>> It's risky, but
a lot of people design it in a way that because when they do system support, when
they lose the super administrator's key- >> Yeah.
>> They could go through the backdoors to access to the system to do things, right?
Now of course- >> Sometimes you need that.
>> Yeah, exactly, this is not known to a lot of general people, but they have that.
Especially, even for very high, high secure system, right?
>> Right. >> The one of the reason you trust
those high secure system,
because there is another control system to control the backdoor.
But if that system has no backdoor, what happen if some of the superusers keys,
laws, or whatever?
>> So- >> Then
you lose total control of the system at all.
>> Before we leave this topic, let me put in a plug for another course.
We have a course in this series on FinTech talking about compliance and assurance.
>> Yes, yes.
>> And when you're automated,
when you're using technology to replace people, you better be able to audit that.
You'd better be able to keep compliance high, security high.
>> Right, it sounds like recovery, business contingency plan.
[CROSSTALK] >> You need something like that because
otherwise, you're in deep trouble.
>> Yeah.
>> And so that's one of the four courses in this series on FinTech.
This course is the foundations and overview.
We have another course on regulation.
We have a course on implications for society.
But that compliance and assurance is one of the critical things that banks do well,
traditionally, and FinTech companies probably ought to worry about.
>> Right, right. >> [LAUGH]
>> Yeah, that's
why some people also propose that it's because it's just an emerging field,
the regulators just need a bit of time to get, and
then gradually regulation will comes in, directly to FinTech company as well.
>> And companies that are startups, who don't don't compliance and assurance,
are going to be dead.
>> Right.
>> Something's going to go wrong and they're not going to be able to handle it.
>> Right.
>> So it's just a matter of time, and you don't want to be the investor in that.
>> Right. >> And have your company go under.
>> Right, right.
>> So all right.
So in summary, AI matters.
Decision support is something that leverages some aspects of AI.
It's a little bit less advanced than AI.
Many people used to talk about expert systems,
we try not to use that term anymore.
Often we think of assistance or
intelligent assistance to help you in your business.
But they are to help you make better decisions.
So you may still use a person, but
that person says, is this a good credit risk or not?
The computer says, watch out.
Okay. >> Right.
>> No, thank you.
And automation which is changing because the levels of jobs being replaced
tend to be higher paid,
higher value jobs than the ones we were replacing in the 1950s and 60s.
>> Yes.
>> So that's exciting.
It's also challenging.
But it means, if you want to make the big bucks,
you better know not just finance, but also technology.
You must know tech as well as fin if
you're going to play in this financial market.
>> Right.
>> All right, well thank you very much for your time.
>> Thank you.
>> Okay.
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