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How important is this increasing amount of data,
this better tools for data analytics in FinTech markets?
Now, a lot of those data are actually useful for analyst.
Now, it's really to look at the pendulum.
Do you prefer less data or more data?
A lot people probably say that more data.
More data.
Yeah. At least, I have the choice,
I have more information to confer,
to clean it, to filter out the irrelevant part, or the noise part.
At least, I have a pool, instead of have nothing
or have a just only one or two source of data,
then it's no way for me to know the true picture of it.
So, I think people, in general,
still prefer more data.
Also, ones with more data,
then more people will able to be build up a lot of analytic engines to analyze,
to clean the data, to look at the quality of the data.
But I have heard it said,
at least in the context of regulators,
that regulators are used to thinking we need more data,
we need more data, we need more data.
And it overwhelmed with too much data today.
So, what do they need when we get so much data,
so much big data,
so many sources of data.
What do we need now?
Yeah. Okay. I think the next thing is data cleaning.
It's really a tedious process, and very often,
a lot of the managers will say that,
"Yeah, we do a lot of big data analytics, etc."
They overlook the importance of cleaning the data.
That can cost more than the analytics itself.
It could be. Yeah. I use porting example,
for example, on the exchange rate.
Now, in some countries,
if the government decided to dis-encourage high frequency trading.
Right.
So, instead of releasing tech data,
they could release second data.
Every second they release,
within the second there are let's say 100 transactions without any time series sequence.
So, it's up to you to figure it out.
Okay. Or they purposely randomize it.
Okay. So, now, it's up to you whether you consider this to be
a fair approach because for people
that doesn't have super computer or advanced computer, because it is fair.
They don't care.
Right. That's okay, right? To me,
whether the data coming take up microseconds or
seconds to some of the general retail investor then it's-
Frankly, give it to me in days. I don't care.
I'm not going to make a decision in less than a day if I'm a small traders, so yeah.
So, that's why regulator do play a role in terms of data quality as well.
So, it depends on how they regulate.
Also, for data cleaning or data, so-called ''reconstruction'',
then it's up to the data analytics to look at that seconds and try
to find out the sequence of those tech data within the second.
A lot of companies talk about big data today,
and a lot of companies talk about analytics.
One of the things that surprises me is how many companies are talking
about big data that actually have small data.
They don't have that much data.
So, a client may come to me and say,
"I have data on
all the hotel stayers and all my hotels who are members of my frequent flier club."
And I'm thinking, your whole dataset for 10 years is
less than one hour of stock trades on the stock market for one exchange.
So, the amount of data we're talking about that's already pretty clean in
financial markets is huge compared to what it is for a lot of companies.
Right.
So, I would say that makes data analytics
incredibly powerful for financial markets because we have such clean data.
We have such high quality data,
that it's the collection,
the coalition, and the analysis of that data that
starts to create a lot of the value in finance markets.
Yes.
Now, that's not true with peer-to-peer lending.
It's not true in some other things where now we're looking for social data or insurance,
we are looking at risk data.
So, it depends on which FinTech market we're talking about.
Right. Yeah. Because especially in the finance market,
we talk about time is critical in terms of the data.
Also we're talking about the real time data.
Now, even comparing in finance market,
even compare the insurance data and the so-called the stock data. Right?
Right.
I mean, you don't, for the insurance quote,
you don't change your auto quote or
your parents life insurance quote every second, right?
Nor me changing my medical prescriptions very often.
Actually.
So, that's small data by comparison to stock quotes.
Right. For in a stock quote, every seconds,
the trading price of the stock could fluctuate,
ups and downs within 00.1 percent.
But if you could do arbitrage,
you could do a lot of other techniques and
that 00.1 percent could make a lot of difference.
Also, in the foreign exchange market as well.
There is something that could make a difference in big data
in health insurance or life insurance,
and that is if we get wearable technologies that are more mature, Internet of Things,
I could know what have you ordered,
what have you eaten,
how much have you exercised,
how much insulin did you inject over the past year.
Many other things that could affect my analysis of your risk.
You raised a very good point, yeah.
For insurance data, on that particular prospect,
is that we always consider data privacy issue.
Yes.
It always involve personal data as compared to the financial data on the stock market.
So once it involve personal data,
basically, it's two approach.
One is the person willing to give you
his or her data because you are offering him or her some incentive.
Let's say if I offer my health data or I'm going to wear something,
then you give me a little premium.
So that's incentive, so that I'll be able to, for example,
if I let you put in a device in my car to monitor my driving manner,
then probably you would reduce the insurance,
and I believe I'm a good driver,
then I don't mind you to capture those information.
The other part is I probably don't want to release
my personal information as much as that of exactly how I drive in details,
but rather I'm still releasing some of it for you to do analysis. For example I mean-
Especially, if you give me a 10 percent discount.
Yeah. Some of it, yeah. Exactly.
If the price is right,
maybe I'll give up some privacy.
Right. Yeah, and some of it and also you could use those data
to do other further analysis of coming up with new insurance model.
So, you may have a particular insurance model for
particular type of people in a particular occupation. Okay, maybe teacher.
I don't know, I'm just putting a hypothetic maybe after you study, oh!
Teachers and professors, they tend to drive safely
or whatever sort of types of people working in a particular occupation.
I don't know about that.
I'm not so sure that that's accurate.
But okay.
So, into summarize,
we have a lot of data, particularly in financial markets.
Data analytics matters.
This is one of the biggest opportunities for data analytics because we have clean data.
We have high quality data for stock trading and that
makes up big opportunity for data analytics that's interesting.
But we also have a wealth and breadth of data in many other areas at FinTech,
and if we can do better data cleaning and get the quality of that data up,
we may be able to do things that we could never do before.
Right. Yeah, and also the dissemination of data is very important.
Like every morning, you turn on TV,
there are a lot of so-called financial analysts.
Right.
They are talking about these and all that.
They are actually influence the data.
It's a circle, right?
They influence the data that your are collecting that date.
So they may influence the sentiment of people. True.
They influence a lot, yeah.
So, that's why the whole data life cycle also include the dissemination.
Once it disseminate, you collect it again,
clean it again, etc.
So, even if it's fake news or neutral news,
just the reporting of the news can influence people's sentiment,
and therefore, influence later news.
Right.
Okay. Well, thank you very much.
We'll look forward to having you in our next session when we talk about
AI, DSS, and automation.
Great. Thank you.
Thank you.
Theodore Henry King CLARKAssociate Professor
