And now, on to the show.
From Data Rails, this is Welcome to FP&A Today.
I'm your host, Glenn Hopper.
On this week's show, we're stepping just outside the usual walls of corporate finance.
I want to bring you a perspective that I think every FP&A professional should hear.
My guest is Akhil Kungar, a quant and modeling specialist who spent his career building risk and forecasting models for global banks like Barclays, Citi and HSBC.
And here's why I think Akhil is an interesting guest to have on the show.
We're in this era where complex modeling is growing ever more accessible and the lines between finance and corporate planning are kind of starting to blur.
Tools are evolving.
Expectations are shifting.
And if you're an FPA today, understanding how quants think about things like risk, uncertainty and modeling could be a real edge.
So in this episode we're going to dig into what FPA teams can borrow from the quant world, how external factors like macro trends and tariffs can be modeled more rigorously, and what the future might look like as AI and analytics reshape both fields.
So let's get into it.
Akhil, welcome to the show.
Thanks.
Thanks, Glenn.
Good to be here.
Yeah, I think your career journey is quite unique.
And rather than me trying to summarize it, can you kind of walk us through your path from engineering to financial mathematics, and then financial engineering, and all the way into your current role at Barclays?
Yeah, sure.
So I think like as an undergrad, I was like, didn't fully think what I was supposed to do.
I got into electrical engineering, which was quite exciting.
And then I had quite a bit of interest in finance.
And I thought of something doing something which is like merging my mathematical skills, quant skills, and something with finance and finance.
I decided to go for financial mathematics, which was quite like a technical course, more into very mathematical details about all the things we might be using, but things behind that, a lot of the models which we do.
So then I ventured into some trading, like I did work on a trading company for one year or so.
And then I thought I want to get more practical knowledge of like how to use things and not just have like more theoretical or like knowledge on like how to build things.
So that's how I decided to come to US and also US is a bigger market and you see more opportunities.
So I came to financial engineering and where I met a lot of good CPA people and then also learned a lot of practical things on how to use things for modeling for a variety of purposes, like could be from risk to front office, for trading, to research, and then, like ended up getting into risk modeling and through my statistical skills and mathematical skills, and then I learned more of coding skills on the job, which I also learned through a master's, and I have caught, like have seen a lot of things evolving over the years, like in different companies, depending on like a lot of regulatory requirements and a lot of modeling requirements have been changing and then new tools come into play, like from initially, even like change from programming languages, from MATLAB to Python.
And then now within that, like you're still using Python or something, but you're moving into AI stuff.
There were machine learning which came into, it's still there, machine learning is there.
But it's like five, six years ago, it just started as a bigger play.
And now it's going to be much more with AI coming into play as well.
And I'm guessing, even back in undergrad as an electrical engineering major.
I'm guessing coding was a big part of that.
Were you doing CS as well in undergrad?
Yeah, so there were a few courses which we had to do out of coding.
So we did a lot of C, C and then that was specifically coding courses which are computer science focused.
But then as part of electrical engineering I had to do a lot of MATLAB stuff for image processing and everything which is quite interesting.
And I think when I joined financial engineering or financial mathematics, I think MATLAB was the language at that time which was used a lot and which has evolved now to Python and R.
For our listeners who aren't familiar with MATLAB, can you explain what that is?
Yeah, so it is like a type of language similar to Python.
Like.
It's that in a way that you don't need to write like.
It's not like C or C, where you need to write everything from scratch, like define all the variables and things.
And it is like interpreted like a language, but it has more features in a way that, like it is more secure than Python or R because it's not open source.
There is a company like Mathworks, I think, or something like, which control MATLAB and any changes are done in a very specific manner.
So there's less risk of losing control, which is in Python.
But having said that, the problem is the cost because there's somebody controlling it.
MATLAB come with a cost and then advances are smaller because people are not able to freely contribute to that language.
Though still advanced, I think, because the cost would be the same starting point.
I think when people start migrating from that thinking like we can get a free language and then over time, like you, get comfortable with language, maclab is still pretty good language, i think.
When it actually goes into core electronics principles, like for image processing and everything uh, it is still one of the very good languages.
So you had this background in coding before you got into finance.
So I feel like that brings a different mindset of the way you look at finance.
And also you have this engineer's background and then you go into financial mathematics.
So you're doing some pretty intense math there.
And that's your first exposure to finance?
While you were studying that, were you also taking your just core basic finance courses so you'd have a better understanding of the rules around finance and what everybody was looking at and what was important? yeah yeah so i think the courses were like mix of mathematics and finance uh to be honest the mathematics courses were more tougher and it took me more time i i was not expecting i wasn't going to be easy for me but it like was quite intensive uh to just it was completely different thing uh but then finance was actually it's more intuitive i mean it is like you need to understand stuff but if you spend enough time like on financial analysis like something on accounting or something on risk management some measures like something on option pricing like not pricing i would not say like in terms of quant but like understanding what options are and as a basic things understanding what how the balance sheet is managed so those things are like conceptual and i think if it's just that if you spend enough time on them i mean you have to spend that enough time on them to understand them properly but there's like in some of the quant stuff sometimes can get like maybe still i don't understand 20 percent of the stuff right but because it's and there's always new things coming up yeah Well, in a few minutes, we'll get more into the difference between the financial mathematics and the financial engineering.
But I think, as we're talking about the education here, it's easy to see the skill set that you built.
But for our listeners, I'm trying to help them visualize the role that you're in today.
So maybe if you could just tell us about what your role is at Barclays, what a day looks like for you and what your main focus is today.
Yeah, so I'm into quantitative analytics team.
I work as a vice president there.
And so my main goal is to develop, like stress testing models and financial planning models for different businesses, like focusing more on markets and investment banking.
But I do focus some on cards portfolio and trying to get hedging costs of the card business as well.
Our broad day team covers a lot of other things like treasury cards and everything.
So then, like in terms of stress testing or business planning, like it's not just for us business, like we cover uk, or like parklays is like uh, uk bank like, so i would say, all the world's model, like global models for uk portfolio.
And then also there's a lot of push from Europe regulators that after Brexit that Europe regulators are doing things differently.
So they want some different testing, different business planning and different scenarios.
They have their own scenarios.
They want you to test things, develop different models for them.
So those are the things we cover.
And as part of the whole process, we have to do a lot of coding, apart from the model development, where we have to implement the models, make them ready, like for it.
Uh, you say so.
It is quite like.
Sometimes i do think like we are also doing a lot of more than i expected when i joined parkless, like in terms of the implementation work.
But i think it's good for learning that you learn a lot of new things, so you're not just doing development but you're learning like it core, it implementation skills as well.
Yeah, i'm wondering From your perspective corporate CFOs and finance leaders right now are kind of losing their minds around all the uncertainty around geopolitical issues tariffs, everything going on.
I feel like in your position yes, there's this uncertainty, but I feel like maybe you're digging into even more uncertainty than a typical corporate finance department is.
So I guess, with that, my question is does this feel like a more stressful year to you, or is it always something?
And there's so many exogenous factors going on and everything.
You're always modeling different scenarios.
So how does the environment feel this year for what you're doing?
It seems like everyone in FPA, our models are all over the place.
We keep doing new ones based on whatever's going on today.
Yeah, I think this is a very challenging time right now.
But, having said that, I think because we tend to use models that are built on longer history and try to cover a lot of different regimes, so they tend to perform well.
As of now, for this year, we have not largely seen too many differences.
I mean, there are some things which keep happening and you need to adjust your model, but I have not seen like some very big changes.
And the one big reason for that is that the implementation stuff I was talking about, that we have automated so much stuff like make it that our models can be run to different scenarios very quickly.
We were able to do that pretty quickly in one week or so.
Not just, I mean our model.
I would say like across Parkland we were able to test out whole models in a single go to see what the impact would be for capital RWA, income balance sheet, everything.
I think what prepared us well for this thing is the COVID period, because something similar happened to COVID.
And I think it's.
I mean, I was not in Barclays at that time, but I think similar thing we did at HSBC at that time, but probably Barclays.
That time, like, we just forced us to try different things.
And then people realize okay, that you have to be smart enough to update your models or make changes on the fly quickly.
You know governed, well-governed way.
And you should be able to run different scenarios quickly so that you're not scrambling for a lot of governance works and at the last minute.
So I think that time it was much more difficult.
And also like the situation was even much more difficult than right now, I would say at that time.
Yeah, and I want to get more into scenario analysis later, but I'm also I want to.
When I was looking at your background, I was trying to visualize the difference and understand the intricacies between the two.
And I think, because of the typical, our typical listener and the path we take, can you help us visualize?
Since you studied financial mathematics and financial engineering, can you walk us through the differences and then maybe tie them together and talk about how you're applying both of them today?
Yeah, sure.
So, financial mathematics is more like a research-oriented subject.
Like, if you like, kind of any models you're building using, like now, like or even simple like.
You come up with a like a machine learning neural network model or something and then apply it to finance and then you come up with some like for Stochast, or you use some black in schools model or something.
So that is more like you understanding the whole map behind it and try to come up with strategies by changing those founding principles.
Like coming up with some new models from scratch.
So basically, you're spending a lot of time on research.
Financial engineering has 25 of that that you need to understand the basics well, but then the more effort is on like trying to use those skills in a smart way.
Uh, what are the best way to use these tools?
Like more emphasis on finance, as you were saying that going through finance courses.
I think if i would split the finance courses like maybe i had like 20 percent finance courses in financial mathematics but 60 percent finance courses in financial engineering, so you get to see where you want to apply those principles and understand more about the real finance world, you can actually use those.
Yeah.
And I think that's why I think both of them get used.
When we are like financial engineering is very easy to see, like as you go through a lot of discussing with different businesses, we talk, we discuss with model businesses.
We have to understand the product and have to understand the like.
The simpler knowledge about finance principles like to even showcase that what we're saying like logically makes sense.
And then i think those skills are like if you're trying something new, if you want to try, it gives us more confidence.
Okay, like i know some more maths behind that i, i can, i can defend like that even to someone who's very technical.
That's just picturing the complexity of these models.
And even I think, today FPA models and we have help with software and certainly different, broader skill sets that are letting us do a lot more complex modeling than we have historically.
With all that, I'm wondering were you ever, did you ever consider just traditional corporate finance or FPA, or were you from the beginning you knew you wanted to go the quant route and have your focus there?
Yeah, i think i was.
In the beginning.
I was more focused on quant because i really wanted to use my skill set and then i learned, i think in recent times i have sometimes do think that maybe i should try something different, like because i now know more about like look watching, like about the businesses and understanding like products well.
So at times i do think that maybe i would do that, like in some years i don't know, like i've not really looked at it very seriously, but comes to mind like you want to challenge yourself, do things differently, see what challenges other people may face.
And I think maybe use your current skills in a way that improve the other process if you go other side.
Okay, let's dive into the models now, because I think this is going to be fascinating to walk through, because I think on the FPA side we know pretty much how we're building our models and could everything from everything happens in Excel to using planning software and all that to start it.
But with the stress testing and the scenario models you're doing, I'd love to hear maybe your approach to modeling and we can talk about kind of the fundamental differences in how quant teams and FPA teams approach forecasting.
So walk me through on the quant side.
You have a new model you're going to build for whichever business, what you're looking at.
And then, and maybe even I'm guessing that one big difference is because of the nature of what you're doing.
Are you waiting historical performance and all the external factors?
Because you don't know what the annual plan is going to be for the business necessarily, right?
Or you don't know what their goals are or all the internal stuff.
So I don't know.
I'm trying to answer the question for you now, I think.
But maybe I'll stop talking and turn it over to you and hear your approach to modeling. yeah i think you put it quite well that we we are like starting with a lot of historical data and i think we want a lot of data like to see different regimes or different periods and if it is not possible internal data we try to look at external data some industry data at times and try to model using that and then try to apply some linkages through that is where i think business intuition comes in like we work closely with the businesses like when finalizing like how to make that linkages so while developing the model like we can never develop like a future proof model right like if strategy changes or something so that is where one thing will fundamentally differ from fpna side and our because we are not like as you said we don't know the annual plan i mean Sometimes we do know, but then also it's difficult to incorporate that.
So what?
Sometimes we do try to like things like which are more stable, like balance sheet or something.
We try to model them in a way that your current value has an impact on the future value.
And I think that should be a way for, so that any recent strategy changes can be reflected in your forecast anyways.
Like.
So if you're picking from a starting point and you made some changes, and in the business, if you're starting from a new point, you're already taking that look on and sometimes you need to look at recent averages as a starting point or something.
But still picking back on the historical data for the model design uh, it's just at the starting point.
You may want to use it.
Sometimes it is not possible, like the way the data is and you may not be able to do that.
I think what FPA team are more focused on are very recent actuals plan and they can be optimistic at time.
It's also like they have been told okay to stick to this plan, like tragedy will not give you funding if you don't stick to the plan.
So they can't, they don't want to say okay, that I will show my lesser revenue or less balance sheet, that i will not get that funding.
So they're sometimes forced into making some decisions like based on the plans, and that is where i think there is like more and more need of like using kind of mix of both world.
I think, which was done like quite interestingly at city, like they were very focused upon definitely using our models for business planning directly in a very formalized way.
And any changes would have to go through an overlay or change process from the FP&A teams.
I mean, it is done everywhere.
It works also, but it's not that formalized process, especially when you're looking for business planning purposes.
And are you starting?
I'm wondering about the tools that you use.
Are you starting in Excel just because you can visualize easier in Excel, or are you just immediately?
Everything's in a data frame?
You're in MATLAB or Python or whatever for doing this.
What are the tools you're using when you're building out models?
I think it depends like business to business and sometimes what data we are looking at.
I think sometimes you do need to start with Python or like DataFrames, because the data is so big that you need to do some analysis and convert it into like a smaller version.
And then maybe yes, you will open that in Excel or even like, plot some charts in Python to see where you are right before making any modeling decision.
Sometimes you may want to create excel, create some data and then send it to your fpna partners like because, so that they can visualize.
I think that's another thing.
We do try to have all our results like in like.
We do want to put some like numbers in excel so that everybody can open it, like it's not that who don't have knowledge about coding or something they can't open it.
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Because you have such big data sets, are you doing things like looking for correlations between different categories, whether it's in the internal data or the larger macroeconomic?
Are you kind of trying to find signal or leading indicators in anything?
How much time are you spending trying to break down and figure out what's driving trends, if there's something you hadn't looked at?
Or is it pretty standard, you know, these kind of correlations are going to match in this industry.
What's your approach to that?
I think yeah, whenever we're starting some business from scratch, like some new requirement come in, then you do need to spend a lot of time on finding which, even like which, not just which correlation, but like whether, if it make logical sense right, you may see a very good correlation with some variable, but that might just be sporadic, like by chance.
So those things like that is where we need to talk to FA people a lot or like directly business a lot, like to understand.
Uh, I think a lot of time even goes into data cleaning.
I think, before any of this can be done, like to make the data reusable in the right format, like removing outliers or dealing with any missing data or strategy changes.
Like if something happened in 2019, for example, like you will not know, and you will say okay, you will try to fit them very well.
You say okay, it's very correlation, but that's just that they decided to wind up some business or try to grow some business like.
So those are things you need to get information from product control teams or fpna teams and incorporate them into your modeling.
Because i think blindly, if you put some correlation, you will get something, but that may not make sense.
Yeah Yeah, it's funny.
You don't want to p-hack your way through the data and come up with you know, assume causation because you know there's spurious correlations or whatever.
So, yeah, I totally get that.
And you can't I mean to your point on COVID or any of the other global financial crisis or any of the other stuff that's happened you can't model the black swan events that are going to come up.
Is there.
Since you do work with FPA teams, you know in FPA we are typical.
I'm thinking now of, not when we're redoing quarterly forecasts or whatever, but I'm thinking back on the annual plan.
I mean, we've got, we have our historicals and our trends that come from that and we'll have high-level driver-based models that are okay.
This is we know.
We're gonna hire this number of salespeople that should correspond three months later, or whatever it is, to this number of sales.
And they've got all those models and everything is kind of.
The most important sheet becomes that you know you've got your assumptions and then your drivers tab where you're just putting all this in.
And sometimes, while you're making the annual plan, you know what the target is for revenue, the target is for EBITDA or whatever.
So you can sort of tweak the plan to say, well, this is what we're going to do.
And it does make sense from a strategic perspective.
If you want to hit this revenue number, you have to bring the salespeople on three months earlier.
Let's see what that does to the model or whatever.
But are there some techniques that maybe you're using in quant that a typical FPA department is that, and we'll talk more about technology and the availability of these tools later but are there some techniques that you guys do that maybe internal FPA teams could consider and use in their modeling?
Yeah, i think like like, especially like not just permitting to very recent data right, like just don't look at the last two or three months or last two or three quarters.
Like uh, i see, like finance team, like they have a very tendency to stick to the most recent points and like try to model everything based on that, which is fine, i understand, but there could be things like seasonality or something which can be very important to incorporate as well, not just like macro drivers.
And then, understanding why something happened in the past is also important, not just assume it was just a strategic change.
So sometimes you may say okay, there was a strategy change to grow the business, but why did they do it at that specific time only?
Maybe the economy was such, maybe the whole situation was such that it actually made sense to grow that business.
So you could actually get a good macro driver like which is uh, which is needed like, and it could be both ways, as i said.
Like there's a other way, as i mentioned the previous answer, that you have to treat that with caution.
It could be something serious correlation, but it may be realistic.
So you need to discuss with the management and see, like business managers or like uh like, is that like, what was the reason that?
Why did you prioritize this business at that specific time?
Is there like anything economy driven or something?
So those are the things they need to probably consider and try to use, like trends or something right simple can be done in excel, like try to see trends and something.
So like visualize.
Visualization is always helpful.
I'm pretty sure they're already doing that, but maybe they're not doing that for long enough in history.
Gotcha.
Yeah.
This year, I do know that we are... It's just, you know better than I do, I'm sure.
But with uncertainty.
If things are headed down or things are headed up but they're predictable, it's a lot easier to model.
And when we don't know where things are headed, we're running more.
Everyone I talked to in the industry.
We're running more scenario analysis this year, halfway through the year, beyond halfway through the year at this point, than we ever have.
And it's still so much uncertainty.
So I think we're getting exposed to scenario analysis and stress testing more than we ever have.
And for us that usually means going back to the driver's page and changing the numbers and saying well, what happens if tariffs go up and we are paying 15 more or 40 more or whatever the number is for a product?
But From your world, stress testing I know is very important.
So could you walk through what stress testing is in your models?
And maybe there's something that FP&A teams could take from that.
Yeah, yeah.
It's about like to simple terms, I think, like how your business will react to extreme movements and things like spreads or volatility, market volatility or unemployment rate.
And then there are a lot of other drivers like specific drivers.
But there's also the point to it that how maybe you think you react to these, these factors in a certain way, but reacting to like, a 05 unemployment rate may be very different now than if it was at six percent, seven percent right, so you have to understand those levels, like not just in a one way okay, like if it goes by 05 percent up, one percent up, we will do this.
Maybe the impact will be much different if it happened from other level, right.
Like, if interest rates drop from 6 to 4 today, it could be very different from dropping from 3 to 1.
So that is the thing you need to be very careful like across all variables.
Interest rate is a very good example here.
So I think these are some of the things which you need to be very careful when you're looking at drivers like not just look at one dimension, like you could look at multiple dimensions there.
I'm sure your models get very complex.
And I think about some of the models I built early in my career I was so proud of and there were just so many stacked assumptions and formulas going through.
And then when you're trying to pitch that to management And they ask a question how did you get this number?
And you have this long route that you have to get through.
Obviously, you want to keep them as simple as possible.
But sometimes when you start putting in more factors and it's complex business and you've got all these different SKUs and they're all affected by different things, the models just inherently are going to get more complex.
And I think the interesting thing is, because of Excel it can be even harder to kind of trace back the roots to it.
Versus Excel, you can show the formula in Python or MATLAB or whatever where you can say this is what we're doing, this is the regression we're doing, or whatever.
But I think it's tough.
You want to have as detailed a model as possible, in fact, and put in as many variables and factors in so that you can stress test to do scenario analysis plan for different situations.
You're trying to build out this perfect model, but you also have to balance that complexity with usability and explainability.
And I'm sure you have this.
If you're pitching a model in your forecast to a business, they're not going to just say okay great, you waved your magic wand and shook up the crystal ball or whatever, and here's what it comes out.
So how do you balance that level of complexity with the explainability and usability?
And is there something that FP&A pros could take from that as well?
Yeah, I think that is something like I've learned over the years.
Like if you go to someone like business and just try to see like a whole mathematical equation, they will pretend to listen but they will not be listening to you.
So I think the PowerPoint became a key here.
I think the way you present results and I think in terms of relationship with the drivers, it should be clear relationship.
You don't need to go write the equations, but put something like this goes up or this goes down.
Something simpler if you have some triggers okay, this is the trigger point and like it just goes above that.
This happened, this happened, like sometimes you don't have to go into detail how you got there.
If you try to showcase how you get there, it can become more difficult to defend in terms of people who don't understand technical terms.
So focus on what you have and like why do you think is correct?
Like not because how you got there, and have like all the supporting slides in the appendix or somewhere.
Like how you get there if it is needed, and then show different sensitivity analysis, like if we change this, like this will happen.
Like show uh some benchmarking, like maybe with their results right, their their business planning model or something, uh fpa model.
Like okay, this is the difference we are seeing and this could be because of this tractor.
We are.
We are not considering any new businesses, we don't consider this from the plan and this might need like.
So those kind of things we need to.
We uh like list very carefully and and then like uh, not to.
Another thing is like not try to cover too much stuff in one go.
Like uh, try to focus on small things.
Like even if it's a 15 minutes meeting, have more 15 minutes meeting rather than like have more like one hour meetings.
Like that can either.
Like you can lose the plot, like and they can understand, they can have better understanding of small things like, especially when you talk about technical terms.
Yeah, that's great advice.
We talked a little bit before the show and this is I know you, in your typical modeling, rely a lot on historical the more you can find the seasonality and the trends and be able to have that as your baseline.
But in in corporations, so many times we're modeling something, whether we're going into a new market or we're launching a new product, or it's a brand new innovation that there's not really anything historical to base it on, or um, or even.
I guess this would apply obviously, to any startup that's coming into a space.
Um, but it can be very difficult to model that when you don't have the, um, that historical data.
And I'm wondering like, if we're considering launching a new market say, maybe that's a good example to use what kinds of like risk and return modeling techniques should we use when we're trying to figure out if that makes sense from the beginning, when we don't have that historical data for the market?
Yeah, i think one thing which i have done that in the past is like, for example, that product is new in us.
Like, does that product exist in your portfolio in other region?
Try to get the data.
I know it is not going to be exact same, like you have some difference, but it will give you an idea like how the business generally react, right like through the drivers at that business at that region.
That is like one way to go about it.
Uh, other ways to look at any industry references like you can find like uh, any industry data or something or something similar, like even not the exact product, but some different product, or that product might be similar to some other product in your business.
You can use that as a reference, like until the time you get enough data to model that separately, like so these two businesses are expected to move similarly, then you can use other as a pivot to model that.
Uh, if either the industry or the similar business in other region is not available.
And then, having said that like sometimes you really don't know anything and then you just need to probably follow the path of like fpna, like say okay, agree to business, like what you say, and at least for six months, that's what we're going to do and we're going to gather data for six months and then we will come up with a new model like that's just something you have to accept and do.
That Machine learning is so amazing at how, given enough data, how accurate it can forecast.
But I mean time series analysis is very difficult and that's why we're not all just letting robots pick stocks for us and cashing in on our great stock picking time series tool.
But there's no bigger proponent in the world for data-driven decision-making than I am.
But human judgment still plays a role.
So I'm wondering, How do you, well, there's kind of two parts to it.
One where do you think again, you can build the best model in the world and that's a great map, but the map.
You don't want to confuse the map with the terrain all the time, because things happen the black swan events, or just some assumptions, some base assumptions were wrong.
So one, where do you think the role of human judgment comes in?
When you are modeling and you are making determinations about which direction to point the model.
And then the second part of that is how do you then present all the findings from the model to people who are not technical and they don't want to hear about the whatever tools seasonal projection tools that you used and they don't want to know how the sausage was made?
They just want to know that they can trust it.
Yeah, I think that's like, I guess everyone judgment is very critical.
Otherwise, nobody will have any jobs like robots will be working there.
So it's.
I think machine learning just gives you an idea, like it gives you like some good candidate to choose from initially, and then they may not be like maybe the top five are not the best one, you never know so.
And then i think the first judgment is on your side, like based on the ideas you know about the business and your own knowledge and your own digging.
Okay, maybe even the first model looks statistically the best model, or machine learning is saying this is the best model, but then no, you think okay, this relationship maybe data is showing it doesn't make sense like.
Or if the relationship is right, like if you see the forecast scenario analysis okay, in this scenario it is showing like something like this will happen, but this is not going to happen, right.
Like to say the interest rate is going up and your revenue is going to really shoot up.
You think it can increase, but it is not going to like double, like if interest rate double, right?
New factors will come into play.
So, so those are the things you need to be careful.
And then that's where also like then you talk regularly to your business partners and get their feedback okay, what they think.
And in terms of that, I think, as I mentioned earlier, like don't need to mention about everything, like say okay, these are the candidates, show them, show them like the relationship, show them the forecast analysis, some sensitivity analysis, show them like changes in the outputs and let them see okay, what looks more reasonable.
And but yeah, having said that, like sometimes we do need to, they may say okay, they may just want to pick something and if you strongly see that okay, mathematically it will not make sense at all, then sometimes you have to really stand your ground and say okay, like there are some other implications because of the strict regulatory standards, like lot of validation teams, you can't just pick anything like it has to be a mixture of both.
So I think yeah, human judgment plays a role a lot in everything.
And on the human judgment, so domain expertise is so important.
We talked about democratization of data for years and how great that was that there's so much data out there and it's so much more accessible by more people, and that's great.
But just having the data wasn't enough to turn it into value.
The barrier to entry for being able to use that data was a background in BI or data science and understanding machine learning and knowing how to manipulate the data and what to do.
But even understanding all that, you also had to write Python.
You had to be able to write SQL queries.
You had to be able to speak the computer's language to talk to it.
And there have been for years development of a lot of low-code and no-code tools that give access to people who otherwise wouldn't have it.
And now with generative AI and vibe coding.
People can not have much domain expertise in data and engineering at all, but they could ask ChatGPT to write code that would do Monte Carlo simulations, do black shoals, whatever their, whatever they're building, and they could not really know what they're doing.
So I feel like with vibe coding you could go and just create an app in 20 minutes that is doing something for you.
But then you turn it over to people.
And when they ask where the numbers came from and this is certainly not for compliance and audit and all that you can't just say oh, the magic black box gave it to me.
And that's, you know, people can get in trouble from that.
But do you see, It just feels to me like the future of internal FPA.
They are going to be trying to model to the level that you guys do now.
And I'm wondering if I guess, from your perspective, having that deep domain expertise, if we were going to have access to these tools.
You've got to understand how they work.
So I'm wondering...
What would need to change in FPA hiring and upskilling so that, if we're using these tools, we're using them responsibly and we can explain it?
What does FPA teams, if they are going to have access to more complex forecasting?
What do they need to know and understand and be able to use and be able to talk about?
Yeah, I think they need to not think too much about coding part of it, because that Chennai and everything you're going to take care of, you can mention okay, I want to look at this and it will create that for them.
Or even if they have to write a code, they will be able to provide a code as a new copy paste and make minor changes.
So you may need some coding, but not that much.
But I think what becomes more important is understanding the math behind it, or like coming back to principles of statistics and modeling and also finance principles, not just math principle, like whether it makes sense in finance setting whether, like suppose you get like just a very billy example, like assets is negative, like whether it even makes sense right, something like that.
So you have to be like those kind of skills like become more and more important, like how to use ai more efficiently.
So i mean, i'm assuming like and i would have seen that people already have good finance knowledge but they need to brush up more on like have to get good statistic knowledge, statistics specific.
I will not say like pure math, but statistics knowledge is like very important and i think that might you see that as more and more i come into play like people who have good technical background, they may end up getting more into these roles.
Uh, but yeah, doesn't mean that finance skill will not be important.
They would be like equally important, if not more, than what they are right now.
What does requirement might reduce in terms of reporting or something?
The tedious task can be automated if you come up with an automation structure and use AI more effectively.
Yeah.
I think you're so right on statistics because that's the gateway drug into machine learning and into everything, because there's different levels of statistics.
There's the business statistics courses you take in an MBA program or undergrad.
But then if you go deeper into it, you start to see a lot more value in.
Oh wow, I really could apply this statistical modeling approach to my FPA.
And when you understand that and you don't have to go into RStudio or Python or whatever and do all that, then But if you understand it fundamentally, then you can do it very quickly and you can explain the model and you can explain how you did your model selection and the testing and the sampling that you did and everything.
So yeah, yeah, I'm right there with you on that.
For our listeners, maybe their early career, and they've started out in FPA and they listen to you and they think man, I really want to, I want to go be a quant.
If someone you know you came into it through engineering in that way.
But if someone say they have a undergrad or a master's degree or whatever in finance and they're thinking I really want to make this shift.
What do they need to start preparing for and thinking for as they line up their education?
In which direction should they go educationally to move into that direction?
Yeah, I think definitely have some statistics course in the background, like when they're doing their bachelor's or something, or even like some other degrees they might be doing on the side or even running on their own.
And then some coding.
I think coding, again, you don't need to do the course, but need to spend enough time on that.
I mean, I do think coding might become less important over time. spin these hours but for now it is still there like uh maybe it is maybe it will not be there in the actual world but in the hiring like people may still grill you on coding like uh for some more time uh a few more years i'm pretty sure about that so that's why you do need to like work on these two things like maybe not like too much like but yeah some of it is required yeah Yeah, so well, this has been this has been good.
I've loved this episode going in, diving into a little bit different world than where we normally spend our time.
As we're winding down, we have two questions we ask every guest.
And the first one is what is something that not many people know about you, something we couldn't find from your LinkedIn profile or looking you up on Google?
Oh, it's really fun.
I actually went one to Ukraine like before the war.
Oh, wow.
Yeah.
So it was quite beautiful.
And I did like in my undergrad.
I did like a social internship, like working with some steel factories and like trying to talk about pollution or some stuff and meeting with young children and like trying to teach them English.
Like that is like I did one of my summer like in undergrad, quite exciting experience.
Wow.
I'll avoid going into all the political and global ramifications of it, but I wish for peace in the region.
And I know there's been a lot of destruction.
So, all right.
Everybody's favorite question.
And I think this is going to be interesting from you because I don't know how much time you actually spend in Excel, but we love here at FPA today and at Data Rails, we love Excel.
So we ask everyone what is your favorite Excel function and why?
I think i use a lot of things in excel like.
Even we think like we do a lot of thing in coding, but we still use excel a lot.
And i think still for another favorite function everybody must have used, we look up, i think like, must like make some things very easy and like to do quick analysis, dirty analysis sometimes.
A lot of times you have to do a very quick analysis and setting up everything in the code and you don't want to spend that much time and you do in excel and these kind of tools like help you a lot.
So when I was a guest on this show before I took over as host, my answer was also VLOOKUP.
And since then, I just get railed on it.
Everybody's like, XLOOKUP, what are you talking about?
Or Index Match or whatever.
But yeah, I still, I have to admit, and again, I'm not deep in Excel as I used to be, but I'm still going to use VLOOKUP a lot.
I don't know.
It's hard to shift what you were raised on.
Well, Akil, this has been fantastic.
I really appreciate you coming on the show.
