In the financial services industry.
It is not so much your IP or your technological Edge, but it
is the ability to day after day year after year.
Deliver consistent, good quality service to your best customers.
Yes. The focus is always on solving
the problem as opposed to dumping or bringing in the most
sophisticated method. Let's start by saying that
typically, when Allah Large company and established form
sets up a data science team. Write the objective is not to
solve a problem per se hit. The objective is to have a data
scientist. Hello and welcome to data
chatter the podcast, on all things data.
This podcast is a series of conversations with experts and
Industry leaders in data. And each week.
We aim to unpack a different compartment of the data
suitcase. I am your host got the chassis
that I want. Logger newspaper columnist book,
author and a former data and sadly consultant at currently
head, analytics, and business intelligence for delivery.
One of India's largest logistics.
Companies. You can follow me on Twitter at
Karthik s that is Kar Phi. K s and read my blog at no
Intruder.com. That is n 0e.
N th you be a.com or opinions expressed in this podcast.
Belong to me and my God. His and they do not reflect the
views of any organizations. We might be Associated.
Nothing discussing. This podcast, will be taken as
fine, a cheap, but England rice. One of the first industries that
started using data in a big way was Financial Services sometime
in the 1970s soon after the black-scholes model got
developed wall, State started hiring phds in maths physics and
Engineering in a large way in order to build models to price
derivatives and help trade better.
However, over the last 10 years, especially after the global
financial crisis. It is appeared that Wall Street
doesn't have the same pole position as it did in terms of
the use of Maths for making money.
So how did this happen? Why is it that the financial
services industry is not in the same poll position as it was, in
terms of use of maths, what has changed with the Financial
Services Industries and what are the incentives that are
different here from Silicon Valley in order to discuss all
of this today. We have hurry balloting.
Honey. Is a co-founder of Romulus and
award-winning unstructured data automation platform for
financial services firms, right? Found.
Tell me this. How do you spent a decade in
Quantum data rooms at Goldman Sachs?
Come back, Singapore. What is data science and what is
artificial, intelligence? How would you describe these?
Two? Sure.
So let me start actually with the, with the more contentious
of the two terms I had, which is basically a, I, you know, I
think this is. So I was actually listening to a
podcast a couple of weeks ago on on cryptography.
Okay. So, the interesting thing there
was that the word crypto was being sort of, obviously, you,
Just in the context of crypto, meaning, cryptography.
And, but every time someone said crypto in my head, it was
actually cryptocurrency. This is a, this is a common
problem where and I think somewhere in the middle of the
podcast, you know, the host talked about how he owned a
shirt, which says crypto means cryptography.
Okay? Okay, and I think the similar
problem exists in the world of AI today where, you know, while
They might have been, let's call it a computer science or
mathematics related discussion around what is artificial
intelligence and all the concepts around strong and weak,
Ai, and sort of, you know, what is really intelligence.
What is happened, is that because of the overuse of this
term, it is now come into common parlance to mean, what we would
probably call we Ka. Okay, which means, you know,
let's actually sort of, you know, junk On a couple of levels
and just start by talking about intelligence, right?
Yeah, the goodness. So, you know, I think there are
many different definitions to this.
And in, in my book, it's basically at some level.
It's the ability to learn, right?
And it's the ability to learn not just from instructions.
And I think that is, that is probably one type of learning,
and you can obviously have a discussion about, you know, is
following instructions really learning or is it really sort of
intelligence? But basically something a bit
deeper where you're able to take past situations, past experience
and map, the down to new situations and come up with some
useful hypotheses or attempts to solve new problems.
And I think at a very sort of high level, this is what I would
call intelligence. Right?
Yep. And if you're able to sort of
demonstrate that machine is able to do this, then you can call
that artificial intelligence, right?
Now I think this is really sort of the core sort of idea that
differentiates. Let's say, you know, AI from
something like bubble sort, right?
Yeah. Where you're able to sort of
write a bunch of code, which are basically an explicit set of
instructions, which can be followed without any
understanding of the context of the underlying problem and still
achieve the result and we want to sort of differentiate.
So I think at the very Basic level in common parlance today.
When people say I they mean something which is not, this is
something which has some semblance of being able to
autonomously solve a problem based on experience rather than
based on instructions. Right?
I think that's sort of the core idea.
Now where that experience comes from is an open question.
You can simulate the experience by, for example, in the case of
alphago, a machine playing. Itself, right?
So it can argue that there is no real world experience here or
you can sort of show. Let's say a machine thousand
images of a cat or a million images of a cat and a militant,
which is a dog and and build a classifier which distinguishes
between what a cat and a dog look like.
So the, but the basic thumb rule is that you need to be creating
some form of experience for the system to learn from as opposed
to co defying rules. And and sort of creating if
something is closer to bubble sort, then it is to sort of, you
know, showing examples to distinguish between a cat and a
dog. Then I think that is probably
not a right. But once again, the term has
become very murky because we are sort of it's become sort of this
lowest common denominator term because it's being bandied about
so often. And which is I mean, you can be
I'm not much of a silly saying it's a bad thing, but
essentially it is It is now driven by what a large portion
of the population understands this to mean, right?
As opposed to what you know, it can the discussions around what
AI is in the you know in the halls of Academia and sort of
all these ideas around strong which is weak, AI think these
things are all sort of now sidelined and it's become this
catch-all term, which is I mean, I'm not going to comment on
whether that's a good or a bad thing, but that's what it is
today now. Now data science, which is the
other term. You brought up is perhaps a bit
less contentious. And once again, I think in my
book the way I think about data science is it's basically the
ability of ability of someone to build a mathematical model that
can interpret real world data right now, is that definition
very close to statistics. Perhaps I don't have a better
answer other than possibly data. So that first two, you know,
performing this activity in a practical situation as opposed
to in a more theoretical situation, right?
And I think that also brings to data science somewhat of an
artisanal quality wherein, you know that you need to have, the
more you do it probably the more intuition, you develop around it
and the better you get. Yes, and also there are many
many instances. Has where, you know, there is no
one answer, right? And I think that's what makes
that I think that's what gives our data Sciences artisanal
quality in that, you know, every person I have met who professes
to be a data scientist, has their own favorite approach or
their own favorite model or their own internal mental sort
of prioritization of what they would like to try first given a
problem. So I think that's what makes it
very interesting. I think I'm different.
And yeah, so I think that's probably how I would you know,
let's say summarized data science.
Okay. Thanks.
I mean, so while you were describing data since I got
reminded of some old Jokes which was like data Sciences
statistics done on a Mac or data Sciences statistics done in
California or a data scientist knows more statistics than a
software engineer and more software engineering than a
statistician and so on. This is a whole bunch of jokes
that it came out back in 2013. 14.
I think when data science was just taking notes, but I think I
like your, I like your answer which says that like, I mean,
that it is like more of a artisanal quality that each
person did has their own pet methods, of approaching a
problem and things like that. I think I completely, I sort of
subscribe to that as well. So, now, now that we talked
about, so what do you were, what do you do?
Whatever we discussed AI, we discussed data science.
Do you put yourself in any of it?
All about you and your firm. Do you put yourself in any of
these buckets? Or like how do you describe the
what you do? Sure.
So let me, I think, I think that what should have, I think
requires a lot of background, but yeah, but basically, a very
high level, right? So we are basically, I would do
if I had to summarize, what we do.
We are a software firm that's building software products for
financial services and at the highest level.
Yes, right. And I do not want to bring in
words like Ai and data science into the picture because I think
those Our Legacy tools, or those are expressions of what we do as
opposed to defining qualities of our form, right?
Okay. Exist in.
So, the company that I founded in 2017, and now run is called
egregore labs and we built a wide range of software products.
And I think the core idea really is to solve a set of financial
services problems. Each we think married solving
and which are probably we think, you know, not so sexy or hot but
at the same time are incredibly valuable to solve, right?
And I think that is sort of been let's say the driving force or
the principle behind what we have tried to build and what we
have, what we continue building within the company.
So that's like a very sort of high level that.
I would say, that's what we do. Now.
We do end up using elements of, you know, natural language
processing. Some machine learning and deep
learning within the company, as well.
As, you know, a lot of traditional programming and
traditional software building. So all of that comes together,
but I think we are where I think what we have learned the hard
way and when I say we I mean me as a Founder is that the focus
is is always on solving the problem as opposed to dumping or
bringing in the most sophisticated method, right?
If so, you're not ashamed of your humble enough to admit that
there are situations where addicts will do and complex LP
is not required and we are quite happy and humbled to sort of
take that approach. Okay, right.
And I think that's, that's really sort of been the big
learning from the last couple of years for me.
Which is that the problem comes first and the method is really
whatever solves the problem the best.
And you must have a Swiss army knife mentality, where you're
willing to learn, pick up things as required, to solve the
problem in the most, you know, economical / complete, you know,
customer satisfying fashion as opposed to Sort of you know
approach the problem in a more of a screwdriver, a hammer and
nail fashion, where you have this particular tool you're
excited about and now you are going around applying it to
every context. I agree with a lot of it.
I mean, I strongly believe believe that you need the
scissor mean I for a toolbox kind of an approach to other
than a hammer and nail kind of an approach which again.
So what are you? And I completely says subscribe
to that. So but how do you let's say your
customers take. How do you are the sort of
competitors? Take it and so on, like what is
there? How do they approach the problem
in the financial industry. What is it?
Like now in terms of like use of use of data?
If I were to call it that is in terms of solving the non-sexy
problems that you that you mention, right?
So I think, right? So to answer that question.
I think we kind of need to sort of spend some time going down
the The the roads of History. A little bit.
Right? Right.
So if you sort of looked at what you know, let's give are.
Let's call it interesting. Math for the lack of a better
word was being applied in the financial services industry for
for let's say from the 80s or probably earlier.
So from then to let's say, you know, the first decade of the
2000s, I think it was largely around being able to model.
Six places better. Yep, and and so, when we say
asset prices here, we mean, you know, things that are real,
which could be, let's say the price of an insurance policy,
which is, which is what, some whatever tangible nature to it
to something. That's a derivative, to
something that's a derivative derivative and so on.
So there is basically a world of underlying assets and then there
are derivatives piled onto that and sort of add infinite right?
There are sort of as you say tortoise is all the way.
Down in many ways. I think this is also
interestingly enough. I think some more over Zero Sum
game, right? Where there's a very competitive
nature of this particular to this particular industry, which
is you know, and remember that when we are talking about asset
prices and we are talking about derivatives.
We are not talking about what we would call the primary markets
as yet, right? So, you know, we're not talking
about pricing a stock into IPO. Which one?
What would fall? Very neatly into sort of the
investment banking. Origination Mna words, all of
that. I'm kind of removing from this
conversation because, you know, I so we're talking really about
secondary markets and there is a bit of a competitive nature to
it because you know, you are competing with every other bank
on the street in order to be able to sort of price and asset
better or more accurately portrayed an asset better and
effort. There is a certain, you know,
darwinian nature to this. What would, what we could call a
zero-sum game because there is just so much money to be made in
the market through trading, and you have all these competing
players. And the interesting nature of
this problem. Is that anyone?
Someone I met very early in the journey, in my journey in the in
the financial services industry said that, the the one of the
things that makes the financial services industry.
Very interesting is the proximity to money rate.
Where, you know, if you, if you make, let's say $100 trading
today, right? You know, there is some slippage
to let's say, you know, some brokerage house some
commissions, this possibly some taxes to be paid.
If you're, if you're working within large form.
Then, you know, there is some element where the form dig some
of that away, but essentially, there is a very sort of simple
straightforward equation to how much of that lands in your
pocket. That right.
There is no, there's no complexity in terms of how do
you draw the translation of your skill or effort or your result
into a financial gain? For you is very linear in many
ways. Right?
And there is no marketing element.
There is no sort of, let's say there is no fuzziness and then
attracts a lot of people to the industry.
Because, you know, very quickly you can sort of make a lot of
money. Simply by performing by having
one skill value, very good at or like a set of skills, which are
very good at and you're very tangible results to show and
therefore, you know, argue that you should basically be rewarded
in a certain fashion. So in many ways, I think it's a
sort of very short distance between result and instead of
personal outcome for you. And the other thing which makes
the industry. Very interesting at least like,
you know, from the 70s. To the Greenspan years is that
we were we were in a world where I would say possibly cost of
capital was sort of going up at least through the Greenspan has
always expected to go. And basically, how much you
could get paid for an incremental amount of risk was
quite huge, right? And that manifested itself in
various ways. Starting from, you know, what is
the rate differential or what is the sort of?
Referencing even between an emerging market and develop
Market asset, right? Quite quite massive, right.
And to a whole host of things like carrots, and generally
Market, volatility was also very high.
And you could say that we're going through a period where
there was a lot of action. There was a lot of willingness
to participate, that created, liquidity markets, interesting.
And broadly, there was money to be made from Financial
engineering, right? And you know, we sort of got
into this arm Arms raised for yield especially in the private
wealth and fixed-income world. And also, you know, you had gone
through this very interesting fails in the early late 90s
early 2000s where there was there was sort of this one
opportunity when you know, the the equity markets.
Specifically, let's say that the tech world you had this had this
sort of ability to suck away all this liquidity.
Um, into sort of tech Investments.
And for whatever reason, that had not really sort of panned
out the way people expected and the tech bubble had burst,
right? So there was sort of a let's
read employee Capital into other assets.
Let's sort of find a new sort of a thing.
Fixed income is very interesting at that point, for some reason,
which is that people wanted safety.
People want to guaranteed returns.
So, for for a bunch of various different reasons, essentially,
you kind of ended up in this interesting decade where Are
there was a progressive complexification of financial
products. Yeah, and people were quite
happy sort of, you know, buying trading complexity for yield.
They were willing to sort of purchase things that they didn't
completely understand because they were yielding of better,
giving them better income and through the world was going in
One Direction. And you had sort of a series of
rate hikes things seem very predictable housing market.
Booming and you know, frankly regulation was not that intense,
right? Yeah.
And so you had everyone sort of a very sort of large intense
population of folks who are letter broadly, good at math,
kind of accumulate in this one industry and sort of, you know,
competed each other find excitement happiness, in sort of
building more and more complex models and coming up with new
products and sort of essentially, the, the going was
good. Great.
And then we Had, of course, the GFC and you can sort of,
obviously argue. And I don't think too many
people disagree with you that in some ways.
It was pretty, it was precipitated by, you know, these
complex products and and sort of, let us say the way they
behaved and the way they influenced the underlying assets
instead of a market where conditions are quite different
from what they had been before, you know, JFC commenced.
Yeah, and, you know, People sort of had to sort of then agree and
eat Humble Pie and agree that they had misplaced assets.
And not really model things correctly and gotten too caught
up in the math and let the whole bunch of things happened, right?
Which is that, you know, the first thing that happened was
that you had in the short run. You had basically this flight
away from risk and complexity, where people were unwilling to
touch things that were complex, which they didn't understand.
And they sort of Went in the opposite direction.
The second thing was that the financial Regulators came in and
said, you know what like, you know, we need you to make sure
that we don't have to bail you out again and you need to sort
of show your BMO Prudential in the way, you manage risk and the
kind of products you sell and you know, a lot of the Caveat
Emptor approach where, you know, the person who's buying knows
what they are buying and it's all the risk.
If it goes south that It's to sort of selling or financial
products kind of went away to some extent and progressively we
sort of ended up in a world and obviously then you know, rates
came down and that led to sort of a completely different
approach where, you know, it was very clear that we were going to
be sort of, you know, we had multiple multiple rounds of QE
and differentials in rates between M, and DM, go down a lot
of, let's call it macro factors. Ders, you have slowly made this
industry progressively less interesting.
Right? I mean, yeah, number of reasons.
The most interesting aspect of that, was that we sort of moved
away from Financial engineering to what we call, you know,
rattle based engineering regulatory accounting tax and
legal, right? Okay.
So how do we sort of create
products that, you know, and and, and sort of do business
that falls squarely and clearly within the boundary conditions
created by Regulations accounting tax and legal.
And all four were sort of, you know, thing rethinking how the
lens from which they were viewing Financial products in
the financial industry as a whole.
Now. These I would say are all the,
let's call it the headwinds in some ways, right?
Yeah, and now, let's talk about the Tailwind, which sort of led
to a drift away, right? And and sort of created new and
interesting things for the financial industry.
To look at. So, I think the first thing I
would say there is really at some high level digital
transformation, right? Yep, where you had a number of
Industries companies, Etc, move in the direction of recording
and measuring things and putting them inside, you know, a
database. Yep, which they were not doing
before. So people were getting more data
Centric. Broadly.
I think this was one Sort of, let's say a market wide
phenomenon. The second thing that happened
was I would see the Revival of Silicon Valley in some in some
way where, you know, you had, you know, the the rise and Rise
of fang. Plus the creation of
interesting, you know, jobs rolls ideas things to work on
for people who are let's say math oriented or Computer
science oriented and coming out of colleges and universities in
the US and elsewhere. Yeah, I think that created a
second change and then you know, the suddenly you're sort of
ended up in this world very quickly.
Where Capital was broadly accessible to everyone, right?
Yes. So it no longer became and I
think we're seeing that today where you know, it's not Capital
Access to Capital is it might be a differentiator for a But for a
large company, it's no longer a differentiator, right?
Yep, and the things that you needed to do.
The story is you needed to be able to tell and how you
convince investors to give you Capital changed right there.
For a couple of changes started to happen.
First of all, you know, people started looking and equities.
I think far more closely than they did before GFC and focus.
I came especially in Tekken Healthcare to sort of find
assets, which truly had an edge right now.
If you have to measure loyalty, for example, a great way to do
that is reviews, right? Yeah.
So if you have, you know, a product that is directly being
sold to a customer and at the same time, you have, you know,
Facebook, Instagram, Twitter, and all of these widely
available as well as a number of forums that existed.
Before of Hardcore aficionados their response and reaction to a
new product launch became very important, you know, 100 other
of these data sources. I mean, ranging from you know,
satellite images parking lot pictures of parking, lots
infrared, cameras whole sort of flurry of information, which you
would call all data, right? And which basically then start
to become interesting sources of Information, right?
And for the first time, you know, post your the Advent of
big data and you know certain let's call it breakthroughs in
the area of NLP, broadly, right and everything associated with
neural networks. All of a sudden if you had data
and you could throw it at a neural network, you had the
potential to basically make some sense of it.
Yep. Late and, you know things that
were not interesting since the 70s and which were actually Make
ideas suddenly became reality in the current in the last decade,
right? Yep.
And so, you had, you know, you had compute.
You had the, let's call it the data ology you had data.
And most importantly, I would say that a lot of the
breakthroughs and ideas were in the free and open-source
software Community, then I think Typically Wall Street is never
sort of a good first mover but a great, you know, follower,
right? So so I think you had many of
these Trends becoming mainstream and interesting and you know,
frankly, I would say the the how useful is all this data and
like, you know, as all data really taken over and have
people made money off of it and has it been sort of has the The
of this been proven I would say it's quite hard to say.
Okay, obviously the very systematic and you know,
long-standing players in this market.
Like, you know, whether like The Medallion fund you have
consistently shown themselves to be all-weather winners, right?
If I can use that term but as is the average fund really been
able to has the electron really been able to extract Alpha from
these data feeds. It's unclear to me.
And from the all data perspective and you all data
Universe. I do think that if you sort of
look at the curve, right? And one possible, an example of
this of a company that has lived through the entire cycle would
be Kwon Do, Right. Yeah, which sort of, you know,
was birthed, I would say at the Advent of panel data was still
sort of, you know, let's call it a niche idea.
Yeah. Found a way to create a
Marketplace soft. Nominal growth was sold to a
snack and now just this week or last week, the founders of spent
the time as NASDAQ and I are have left to sort of, you know,
take on other, do other things. Yeah, I would say that, you
know, in some ways, it's the end of an era, right?
And, you know, I would be lying if I said cuando was not one of
the things that inspired me to get into this because it was
such an interesting idea. And frankly, it did seem like
Like, you know, something that would be phenomenal and
explosive and it was, but now that we are sort of, you know, a
couple of years in, I, I'm, I would say that while it sits at
the end of the day. This is a zero-sum game, right?
So there are people who have learned, you know, through
through this Red Queen Race of, you know, Finding better faster,
better quality data and analyzing it better there have
emerged somewhere. But the majority are losers and
there's no two ways about it. And, you know, and so I think
that's kind of it's gotten. It's gotten to a point where
people understand the market and this is steady state to the
world of all data. And, you know, and and, and sort
of that's where it is. Okay.
Now you bring me to another
question. So you while talking about a, I
know you were talking about, like, you mentioned this court
that rules Plus data is like, whatever software engineering,
but But Discerning the rules Based on data and outcomes that
say I now. So now let me ask you what is
the difference between AI + ml? This is again, something that a
lot of people conflate. So.
So so so frankly in today's day and age, it's like asking,
what's the difference between let's say crypto and you know,
cryptocurrency, right? Okay.
Yeah, to go back to the analogy. So frankly, they've been used
interchangeably today and you know, I Sort of come up, try and
come up with a definition that, you know, some Venn diagram,
which says okay, you know, these are all the things that belong
to the world of machine learning but not AI.
Yeah, but frankly, I think this is a, this is not a very useful
discussion to have the under point and we have gotten to a
place where a lot of these words are being used.
So interchangeably. Yeah, that while they may have
had distinct meanings five years ago, they no longer do so.
Okay, right. So it It will be it will be sort
of you know, let's say a very theoretical argument at this
point in time to try and create distinction between these words.
You say it's pretty much. It's your interpretation.
It doesn't matter. What is what?
Yeah, I mean you can you can you can look up Wikipedia and you'll
get some very distinct some more somewhat distinct definitions,
but frankly people are using these words so interchangeably
today that it doesn't, it doesn't matter, right?
Like so for instance, you know, it brings back that idea that,
you know, if it's in Python. It's machine learning.
If it's in PowerPoint. It's AI stuff like that.
Right? So we have that world today.
Yeah, right. So yeah.
Okay. So I again want to bring you
back to another thing that you had maintained off hand in the
middle and then like we sort of. So you mentioned about how for
example that your company and you philosophically opposed
philosophically look at sort of data science as a sort of a
toolbox kind of a thing where we right where you were.
So how is it generally in finance nowadays?
As a luckier? I know that all all big Banks
now have massive data science teams.
They are, they, they employ lots of machine learning people and
things like that. Is it, is it generally?
But one thing I've noticed in the industries that like
especially among people who call themselves data scientists.
Is that like they tend to be more of the hammered kind of
people rather than their toolbox kind of people.
But what is it? What do you notice in your
industry as in like, how is it now?
There is Italy if you. Put your competition clients and
so on sure. So let's start by saying that
typically when a large company and established firm sets up a
data science team, right? The objective is not to solve a
problem per se. The objective is to have a data
science team, fair enough. Yeah, and you could say the same
thing for, you know, typically, when you could extend the same
logic to let you know, if I have a large established company is
setting up a Bitcoin trading desk.
Yeah, then your objective is not to make money.
It is to set up a Bitcoin trading desk.
Yep. 8 it is to service some customer need or some customer
asked saying, hey. You provide me Bitcoin Services.
Is that something that you do and you want to be able to say
yes, yep, hate. And likewise.
I think so, in many sense, in many cases.
I think, at least when look, I have, I'm not sort of in the, I
have not been on the, in the sell side for about, you know,
close to four years now. Yeah, so all my opinions need to
be sort of treated with a big spoonful of salt is not a pinch
of salt, right? but I think now we are at a
place where There are dedicated data science teams, perhaps
working on problems of Interest. It could range anywhere.
I think the one of the core problems of interest for all
firms, irrespective of financial services are not is are they
doing a good job of resourcing? Their customers with the right
sales. People with the right?
Let's call it. Research with the right
resources and This is something that I would say is an
interesting scale, data science problem, which I am sure most
Financial Services firms are already working on.
Yep. For instance.
Let's say, you know, I have a customer.
I know who they sales coverage is.
I know the kind of questions, they ask me all the time.
I know the kind of Trades they have done over the last ten
years. I have a general area of our
general knowledge understanding of what their areas of Interest
are and today. Let's see.
The customer who I make two million dollars a year from now
an interesting data science problem could be what are the
five things that we could do or let's say we have these limited
set of resources. How do we allocate them?
It could be, you know, time that my research person spends with
this customer. It could be, no.
I have built this IP or this toolkit, or this toolbox, which
helps understand X. I have this webinar which has,
which I can only give out 50 exclusives.
It's yep. Right home should who should be
given the right to sit in this webinar.
Yep. These are all I would say
optimization of effort of resources to maximize Revenue
that you get from your customers.
I think this is a fairly Universal problem.
It's a fan, it's a problem that is Broad large wide enough and
has a sufficient impact on bottom line.
That is something that I'm sure every data science team is every
bank has a data science team of some sort or someone the data
science skills working on this problem.
Yep. Yep.
Now let's drill down massively. Right?
Like for example, we had let's say model which let's say was
used for option pricing. Okay, great.
Yep, and you know and at this point we've moved, you know,
five six Generations down from Lexia black-scholes model,
right? Yep.
You've got, let's say, two Factor model.
You've got some Sable. You've got a bunch of other
ideas in there. And now the question is that,
you know, hey, can we sort of? Throw away these models.
And we have enough, you know, data out there of underlying
parameters and of, you know, option prices on the market that
can we replace this with a machine learning model.
However, if we have Machinery at least like a statistical model
of some sort, right, so I'm sure this work is already also being
undertaken. I'm sure that this is also
something that most people are have done something about I had
you know, it may have worked in some situations.
It may not have worked in certain other situations, but
this is probably something that also people have worked on.
So these are all probably instances where, you know, let's
say there is a data science team or someone would dedicated data
science, background thinking about these problems are working
on them. Yeah.
Now, the bigger change I would say and You have sort of you
know been on LinkedIn and looked up your Goldman colleagues.
Who are still at Goldman. You will notice that data
science or let's see machine learning has become mainstream.
Most people have done some Coursera course or some have
threatened to have played around or our weekend.
Let's say machine learning folks, right?
Yep. Yep.
And so that I think is the most significant change where people
are given sufficient. Amount of familiarity with this
toolkit that they're able to incorporate it into their
everyday sort of work, and there is a strong interest need.
I would say, you know, compulsion almost to sort of get
up to speed these kind of ideas and to find situations at work
where these can be applied and that's what leads to the sort of
the hammer problem that you spoke about.
Let's say they're someone's day job.
Does not really require them to apply machine learning but they
are still sort of saying, you know, what?
I've learned about this new thing.
I've got some awareness and familiarity with it, you know,
can I find a way to demonstrate that?
I know this is well at work. Yeah.
I'll sort of. close all this by saying something else, which is
Going back to what we spoke about I mentioned earlier, which
is that Wall Street or the financial services industry as
such the established players are not first-movers.
They are good s mobiles. Yep.
They do good for followers fast followers.
I suppose is the word. So when something becomes
established, you will see that they're sort of very quick in
adopting that. And that also means that You
know, the the base at which things are getting adopted
moving changing within large firms is relatively glacial
versus what you would see inside.
Let's see the startup world or the tech World in general,
right? I mean, the last five years have
been like mean the the rate of change in terms of the ability
of what neural networks can deliver has been like nothing.
A short of phenomenal. I mean, if I were favoritest I
would use the word miraculous. Yeah, and but this is not
something that lets say you could have ever come about
within a large established player tinkering by themselves
with this kind of Technology, right?
I think the fact that A lot of these changes were happening in
the open source community and where code or ideas were openly
being exchanged by means of papers and archive or, you know,
simply code available on GitHub. Everything has been is, no.
I mean, I think that's been sort of the core contributors to
where, you know, where we are today.
And I think that is something, which is very hard to recreate
within a particular industry, which sort of We don't values
highly and secretive about their models or there.
Yeah, you know, ideas, right? And frankly, out of necessity,
right? Of course, and I think that is,
though, that is the challenge here, right?
So, just to summarize the very likely that every large form
out, there has a data science team, who's working on some
firm, white problems, very likely.
That every individual Quantum is applying his kind of self-taught
in data science. And applying some ideas and
thirdly, you know, if you look across these two teams, the rate
of change is perhaps or the the amount of let's see.
New idea is and create new ideas.
New development that's happening is probably not at the same Pace
that we are seeing elsewhere, right?
Because it is it is the kind of environment where which sort of
encourages being of very good. Follower as opposed to a first
mover. Is that a bit clearer?
Right? I think that.
Yeah, I mean, I think just two clicks plane, the last bit.
It's a feature and not a bug. Yep, right because I think, the
tech World operates, very differently as a very different
appetite. For risk, has very different
appetite for, you know, they're sort of that not star if you
will, in terms of what should and should be done, is quite
different from the way the financial industry operates.
Yeah, right. We're in a world where, you
know, it's it's not a shoot. First.
Ask questions, later World. You have Regulators all over the
place. You have reputational risk.
Your typical customer is someone you know, who's looking to
conserve wealth first before creating wealth.
Yeah State don't lose my money. That's the first rule, right?
So it's a very different. Industry with a very different
sort of value system. And therefore, I think that is
what sort of, you know, creates this very cautious approach
towards risk-taking of all forms.
Yeah, I mean B8 B Financial Risk taking but also technology
technological risk, right? In terms of, you know, adopting
a new system or you know, migrating to a new framework.
All of these things have to be done very prudently.
Okay, and so by, and if they don't sort of one way in which,
I think a lot of firms they say from the Google Cisco, probably
have grown. Is that like they may not
innovate so much internally, but as soon as they see somebody
outside who's innovating they quickly go gobble them up,
right? So, do do for large Financial
firms. Also, like have they also got a
reputation for that, we hiding, or, is it just a sort of still
the they try to replicate it, rather than sort of Xh, so, I
would say that to answer this question.
We have to go back to what is the core asset in this business?
That generates value for a Google or a Facebook, or anyone
else that core acid is typically you can argue brand and so on,
but it's IP. Yeah.
Yep. It's basically being able to do
something quicker faster better than someone else being first to
Market, capturing the customer before with a better experience
before anyone else can. At.
Yeah, in the financial services industry.
It is not so much your IP or technological Edge, but it is
the ability to day after day year after year, deliver
consistent, good quality service to your best customers to be
able to sort of it is it is basically, you know, being
reliable. It's a big being sort of trust,
is a big, big element of the traditional banking industry.
Sort of a something that, you know, also is something that's a
value that that exists, even today with the financial
services industry. And it's, and, and now that's
something which is very difficult to earn and very easy
to lose. Therefore, while your customers
might be asking you questions. Like, you know, hey if I want to
trade Bitcoin, can I do that with you?
They're not asking you. Are you sort of using the most?
Latest most sophisticated, you know, machine learning Tech and
more than your customers. That's not what the shareholders
are looking for. Shareholdings shareholders, are
looking for you to beat earnings estimates.
The shareholders are looking for you not to get find you not to
get sued. They are not looking for you to
have the latest and the greatest IP.
Yep. Therefore, the expectations from
Wall Street Forum are vastly different from the expectations
that are on the shoulders of a valley company.
Thank you for listening to data shatter.
If you like this show, please leave a comment, share and
subscribe to the podcast. You can find this podcast on
Apple podcasts Spotify or wherever else you go.
To get your podcasts. Once again, this is Karthik
signing off. Thank you.