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?
