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 Hong Kong University of Science and Technology

FinTech Foundations and Overview

41 оценки

Курс 1 из 4 — Specialization FinTech: Finance Industry Transformation and Regulation

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 No.1 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 "FinTech Foundations and Overview" 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 learners 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.

Из урока

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.

3.2: Identity, Privacy, and Anonymity (Part 1)9:35

3.3: Identity, Privacy, and Anonymity (Part 2)8:56

3.4: Blockchain and Encryption (Part 1)8:23

3.5: Blockchain and Encryption (Part 2)8:28

3.6: Big Data and Analytics (Part 1)8:03

3.7: Big Data and Analytics (Part 2)8:40

3.8: AI, DSS, and Automation (Part 1)8:46

3.9: AI, DSS, and Automation (Part 2)8:16

3.10: Smartphones & Customer Tech (Part 1)8:54

3.11: Smartphones & Customer Tech (Part 2)6:36

Познакомьтесь с преподавателями

Theodore Henry King CLARK
Associate 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?

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