7. From Random Walks to Random Forests: Analytics and data science on Wall Street

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. 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. 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.