Welcome back to our course on FinTech Foundations and Overview
In this session on FinTech technologies
we're going to look at the issue of big data and data analytics
And we're going to take a look at what does this mean
both in terms of the opportunity and how
this technology or capability enables FinTech applications to develop
With me I have Dr Hilton Chan
adjunct Professor of HK university and CEO of his own company and the FinTech space
So, as we talk about big data
how important is data
data mining, data analytics to fintech?
>>Yes, I mean in the old days you know
a lot of the data residing with the government or some of the big companies
they have control of all the data like the banks
the telecommunication companies et cetera
But now with Internet
a lot of the data are being uploaded onto the Internet
including news, a lot of articles we read about a person, blogs you know
There comes a wealth of information
wealth of knowledge that is out there
>>And sometimes the data is with these Internet companies like for example
Alibaba knows a lot about you
Uber knows a lot about you
Airbnb may know a lot about you
and so they may have the data and other databases
that the big banks or telecoms or insurance companies don't have
>>Yes, that's right. I mean nowadays everybody will use search engines
Obviously, you leave down a digital trail on a lot of the search engines
Before I want to visit Australia
I probably would key in those information
and you know as for what are the scenery sports in Australia, hotels, et cetera
So, that is kind of so-called for knowledge before I actually do the planning then
I'll really release some of my quoted data on the Internet, on the search engine
So the search engine will be able to pick it up
and know that how many people in a summer are planning to come to Australia
So, those information are not even available to
the government's visa office in the old days
>>How does that help FinTech?
>>Yes, for example, if those information are available
then you will consider some of the stocks you know
So, whether those informations will affect
the tourist industry in Australia or whether let's say
if there is a certain disaster in a particular country
would that have any impact upon the tourism
impact upon certain stocks in the market?
>>Maybe, impact on the currency valuation of the country's currency
>>That will be as well. Yes
Yes
And because we know that in some of the countries that'll mean
Tourism is big
Tourism, foreign students, immigrants and
then all these are things that they all have impact upon the currency as well, yes
>>We may be able to gauge public sentiment based on data mining as well
>>Yes
>>Okay. So, that can matter but one of the challenges of data mining
is that data is often of poor quality
>>Yes.
>>So, what do we do about this problem of data quality
or unclean data if you will or chaotic data?
>>Right. Now very often that on the Internet
right now there is what we consider as not only factual data
There is a lot of opinions
a lot of rumors and a lot of kind of quote and quote
And then you don't even know what is the original source of that
And then also, stated bill point at other data
and then once you find out the original source
the story could be totally different et cetera
Now these are unavoidable
>>So lots of fake news
>>Lots of those information being unconfirmed and that are happening
>>Right
>>Of course there's the good and the bad part of it
I mean, the exciting part is for technology people is that
how could you design a search engine or design some sort of engine to analyze
the ocean of data to identify some of the good ones or
the quantum ones that fits your problem solving requirement
>>So, we have more and more data
but that data may not be easy to use?
>>Yes, and that's why it comes up with a lot of data analytics models
a lot of engines and people apply statistics
on different models to try to find out, for example
I mean one something very simple basic you may say
if different sorts of data are pointing to that direction
we consider that a piece of data will be something closer to factual
But on the other
when people understand that and people trying to create a so-called quoted
a fake fax from different source so that it would see the engine, right?
>>Right
>>So, it's not always the good and evil is always playing against each other in real life
>>Some people argue that the meltdown in
2010 financial markets with nine percent drop in one day was due to
some attempts to manipulate the market with
false signals or information that caused algo-trading to go around
>>Yes, I mean some say it is, some say it's not
But obviously, we will realize that the program training having been here
it's almost like somebody hurts us, you know
>>You get the wrong signal
>>Yes, and say a fire alarm
an alarm bell is on whatever
it just like screaming and everybody panics and then everybody react to the market
And also, research discovered that especially in sentiment analysis is
negative news caused people to react faster than positive news
So, it seems that people are more paranoid than
being optimistic in making decisions on certain things
>>Now, if we know that we can design that into
our models and we can say that's part of our systems
Yes
Part of our data analytics
>>Yes, some people even kind of classified these sort
of tricks or techniques to use under information warfare
So, people were saying that is not
before you want to actually create these sort of threats or
rumor or disturbance in a particular place, right?
I mean, very often that they create a lot of negative names
It seems that negatives are from a different direction
Let's say all the property market is going to be in bubble or whatever
And people take advantage of the short response that they don't have to
the market could have a short response and bounce back to normal, right?
But the people creating these noise just need to make money off that short response
>>Right
>>So the slight drop or slight increase especially a slight drop
Increase is probably difficult to push the price up but drop is easier to do
So that's why a lot of these people are purposely using different techniques to kind
of influence the people's behavior
so that they can take advantage of that in the financial market
>>Can we use this in other contexts besides FinTech?
Could we influence politics?
>>Oh yes, that's a very good example
Because, I mean people are so remote from the candidate, right?
They don't personally know the candidate, right?
>>Yes
>>So, it's through, you know, kind of rumors
opinions and other people what they are saying
and then by seeing what is the video clip, you know
on only part of his life or whatever
a snapchat of something and then just kind of exploded
>>An example of using social media in an interesting way with data is bombarding
the Russian people with data about how wonderful
Vladimir Putin is reading his own song, giving his own hype
And you got a great candidate
People think he is wonderful
not everybody but a lot of people do
>>Yes
>>People I know who are from Russia will say
"no he's really pretty cool.
You should see him wear a shirt on."
>>Yes
>>I don't know if I want to see President Trump without a shirt on but okay
maybe some people have
But, at any rate
so we can get a lot of hype and buzz
How important is this increasing amount of data
this better tools for data analytics in FinTech markets?
Theodore Henry King CLARKAssociate Professor
