Navigating AI Adoption in Finance with Finpilot

Because this is Trading Tomorrow navigating trends in capital markets where the future of capital markets unfolds . Over the past year , the rise of AI technologies such as Chak , gpt and Copilot AI has sparked widespread curiosity and adoption across many industries , encouraging professionals to explore the practical applications of AI in their daily tasks In finance .

Discussions about the transformative potential of integrating AI has never been more popular . Until recently , it was just talk , but now this interesting frontier of innovation is rapidly becoming a reality , and today we're thrilled to welcome a guest whose groundbreaking startup is helping .

Joining us today is Lakshay Chauhan , the co-founder and CEO of FinPilot , which is being called chat GPT for financial questions Currently available in public data . Finpilot uses AI to pull information out of unstructured financial data , for example , data found in SEC documents .

Along with his co-founder , john Alberg , lakshay's company received $4 million in seed financing led by Madrona , with participation from Ascend VC and Angels from leading hedge funds . Lakshay is a longtime machine learning engineer in Seattle for the hedge fund industry . Lakshay , thank you so much for joining us today .

I mean , it's a fascinating product and I'm really excited to dig into this a bit further with you . So perhaps just to kick us off , where did you come up with the idea for FinPilot ?

And so , you know , over the three , four or five year period I kind of , like you , had mined all the quantitative data we could for the fund that I was working at . And so during that course of the time , like after , you know , mining all the quantitative data , we were looking at unstructured qualitative data .

So , you know , we know everything about the financials , unstructured qualitative data . So we know everything about the financials . We know everything about momentum data , whatever we could get our hands on . But then there's this thing about the quality aspect that matters a lot in investing and that we weren't really capturing at the time .

So that's where I started to dig into . It's like what can we do in understanding this unstructured data , this textual data ? And so we started digging into these sources like filings and transcripts and market research reports . And when I was looking into these , transformer models had come along .

It had been a few years and I was playing around with them and so this is way before ChatGPT or GPT-3 . I think GPT-2 was out at the time . But the fact , when I was playing around with these , that these models could understand language so well was very surprising to me , and that was like whoa .

Actually , the fact that these models are good at understanding long , you know , text and like logic and reasoning could be more interesting for the human side of things , or the analysts themselves , because they are the ones reading these long documents and computers are processing data , which you were already doing .

So I think that was sort of like okay , can we do something with like for the humans , because there's just so many analysts there ?

And that to me seemed a very compelling opportunity , given that , as an all knowledge worker , spend a lot of time right reading and synthesizing information and given that I could see , you know , ai is getting there to understand these sort of documents .

And that was really , like you know , a starting point and we started talking to people and kept getting more and more signal what it could look like . But that was really the genesis of why it made sense to do it , because just these models got so better at a point where they could understand this information more generally .

You didn't have to program them right , you didn't have to program . This is how you extract data from an earnings call or understand sentiments like this or whatever . Like it was very general purpose and to me the applications was like , okay , the human productivity side of things could be really , really fascinating . So that , yeah , that was it .

So typically in the financial world or the quantum world , quantitative data is just numerical data , like tick data , credit card data , like all the numbers right , and unstructured data is typically numerical data , like tick data , credit card data , like all the numbers Right , and unstructured data is typically they can be numbers , but it's basically something that has

not been processed and is more raw form . So it could be security cam footage . You know you've heard hedge funds looking at you know parking lot images of Walmart and trying to figure out , OK , what's the traffic ? Like parking lot images of Walmart and trying to figure out , okay , what's the traffic like . So that would be categorized as unstructured data .

What I'm specifically meaning as more in terms of like textual data , so PDF reports , right , SEC filings , transcripts and you know management calls on , you know conferences and stuff like that . So the data is raw , it's not structured , it has not been analyzed in any way , it's not been , you know , easy to search . That's what I mean .

But you could sort of like same thing applies to videos and audio and whatever .

Right when ChatGPT came along in November of 2022 , it was quite interesting Like you just type anything and you'll get something amusing back to you , and so the technology of large language models enabled you to think about a lot of different things and quickly build something that could show you oh , this is possible , like a very cool demo .

But as we started working with these models , we realized that actually , if you know , we want to take care of like analysts right , like buy side analysts , for example it would take like a lot more than sort of just putting together these APIs , and we realized that these models are not good at domain specific information .

So if you wanted to ask finance related questions , they would make a lot of mistakes , both in terms of understanding the question , but also like hallucinations , which is the technical term for making something up that doesn't exist in anywhere . Right , and language models are notorious for that .

So our approach was , in having the ML background , we built this retrieval system that has been fine-tuned and built for financial domain , and so what that means is we have four AI models that we've built in-house that understand financial documents very well . So when you ask a question . When you try to understand a table , it just knows much better about .

Okay , what's being asked ? What is the right piece of information , what's the nuance between EBITDA or adjusted EBITDA or all these kinds of nuances that general models don't capture ?

And to do that we had to do training our models and running our own GPUs and running our own inference stack , and that has been , you know , a lot of fun because , like , you kind of like uncover these little nuances that make you , oh , like , play around with these models and you kind of learn where they fail and where they don't fail .

But then building it our own helps us , you know , make it faster and cheaper . So that's a big part of it . Like , our sort of like core thing is building a retrieval system that can identify . When you ask a question or give a task , it can identify what piece of information do I need to find ? Where do I need to find ?

From thousands of documents , essentially . And so that's been the core part of what we've been doing so far . And then you kind of like layer on top of you can put a chat , you can put like AI agents on top of it .

You can do multiple things , but the core of it is like being able to find the right piece of information you're looking for , with confidence and accuracy .

So , yeah , so essentially what a language model really is like , the way they're trained is right , you take all the text that's possible in the world that they can get their hands on and they try to feed into this model , which does fill in the blanks .

So you would have an English sentence , like you know , the cat is eating its food or something , and you would like blank out two words and then you'll force the model to predict those words through this probability distribution of all the words that are possible .

So initially it's random , it's just filling out words , but as it's training , it tries to , you know , understand what is the most likely word after this sequence , right ? So this is called pre-training and this is what the most expensive part of it is .

And so when models are being pre-trained , where they're learning , like how to complete sentences essentially , but they don't have any specific domain knowledge about , you know how to do a financial analysis or why NVIDIA's certain metrics are different than AMD and things like that , like how one company is defining net retention revenue different than another .

It doesn't have that nuance . It's general so far . So to incorporate that specific knowledge for finance or some team or some company , you need to teach that model . So you kind of like extend that fine tuning , like training process and sort of like zone in into specific aspects of this model that you care about , and so what that looks like .

You create a data set of like hey , this is what I want to do and this is the output , or these are the things I'm looking for , and you give a lot of training examples . So if you're trying to teach it , it's almost like teaching like a young kid , but with lots and lots of examples .

Like , hey , I want you to understand the nuance between this term and this definition , or how this company calculates subscriptions , or you know , billings versus this company or whatever it is , and you just create like this data set by either , like you know , having humans or expert analysts sort of annotate it or doing yourself or some automated system .

But you know , essentially you're trying to give it more examples of what you want it to do or where it's failing , and so that part is what we typically mean by fine tuning is like training that last player of the network , to just understand you a little better on what you're trying to do .

Where is a bike ?

So , beyond just like , is it good or bad , you want to understand why , something like as you know , if you take the top analyst , any field or any company or any industry , you want to understand how they do , their reasoning and thinking and impart that to the models . So that's where the challenge and the opportunity comes in .

a question . So your team has two products . Let's start with the web-based AI chat tool . You know , can you give us a situation where in which a financial professional would want to use this ?

If you want to use the web , you can specify it . But once you do that , you can ask any question and it gives you , like it can scour all those documents and give you succinctly the answer that you want . And the very nice thing about it is it can take you um , all everything is cited , so like , if it cites a number , you can click on that number .

It'll take you exactly where that's coming from , even down to a cell in a table , and that's very powerful for two reasons . One , language models are known to hallucinate , as I mentioned before . So if you go to ChatGPD and ask a bunch of financial questions , more likely than not you will find something that's not true or not present anywhere .

But the other problem is the field where we operate in , like , accuracy is obviously the most important thing , right , Like I need to be able to trust the output . I need to be able to know where the numbers are coming from and to build that trust and confidence for the analysts .

We have spent some time in building this where you can cite everything , which is not that straightforward , but we feel like if you get one thing one time wrong and you can't verify it , you're not going to be able to use any of these tools right .

Like trust is going to be a big part of AI adoption across any industry , but especially for us , because you know , like if you have to manually just do all the work , you're not , like AI is not providing value .

So that's one thing where , like , well , somebody can ask you know , hey , what has been going on with the litigation of 3M in the last five years ?

They have something going on with the PUFA or something , and you can get a very quick answer , versus like reading all last five years of documents , or hey , why have the gross margins of a certain company been falling ? And you can quickly get those answers . And obviously , like simple things about , like segment revenues and things like that .

But the other thing we actually launched recently , which was and we didn't know how popular it was going to be , like it is , so basically , a lot of buy-side folks have investment thesis right and after a quarter happens they want to know hey , for this thesis , were there any question answers that were discussed in the Q&A section of the call ?

So in this tool , basically you just put in your thesis or whatever topic and within you know two seconds you'd get all the relevant Q&A questions , so you don't have to dig through all of them manually and it turns out it's pretty popular .

So , yeah , those are kind of things where you want like quick answers and you want to like dig into a company and even get some ideas from the AI to analyzing that or this . You know QA analysis tool which is just pop in your thesis and get back what's the most relevant questions from the call .

I think it can be used for both and it will start as more productivity , because for the alpha piece you need more reasoning and you need more systems embedded in and for the alpha piece of it , my intuition is it has to be very focused and strategic .

That means you need a lot of human input , humans kind of design , and come up with ideas and sort of help and get help from AI for executing those ideas . And that's a scale and speed that humans cannot do . But what obvious and probably the first step is in the productivity side .

So whatever you're going to do , ai can help you do it cheaper , faster , you know better and that's like the obvious thing , that's like the lowest hanging fruit . So I think it'll tag both markets . My intention is productivity is going to happen first and then it'll kind of like fall into the alpha market a little bit .

But alpha is such a you know it's very hard right Like it's very hard , very hard to generate alpha right and part of it is like it's an art in some sense , like if it was science , you know you would have figured it out , and so part of that is this human AI interaction .

So I think people who can leverage AI in terms of like either more coming up with more ideas or executing them better than others , I think there's value there . But obviously the first step is just to get the productivity layer fixed and you can get as much value from AI on that layer initially

.

So let's say I'm writing a report or I'm looking at a company and I want to get a head start and I have AI do like a first draft of you know a quarter or some market or some you know top 20 companies in this industry sector .

I want a consolidated information and , like an AI has done all the work and I have a report right Now , given at least now the way the technology works is like , I can never be a hundred percent sure that the outputs that have been generated by AI , like the numbers , the facts , will always be a hundred percent correct , right ?

So you cannot simply risk sending that report to a client where even one number or something is wrong . Where you know this , you know mentally that AI can hallucinate . You'll see like , oh , can I trust any of the other facts ? Right ? So to build that trust is super critical , because that's the only way to drive adoption of AI tools for the analyst .

So our sort of approach is well , we can't change how LLMs are trained or we don't have that technology as an industry yet where we have 100% confidence . So it's almost like what can we do ? That's the next best thing . And the next best thing is you try to link and source back pretty much everything , like down to every number .

So if you think something's fishy or something doesn't make sense or something is very critical , you can check in a click right and then okay , that's good with me . So I think it's very critical one to have the human analyst , trust the AI output and drive the adoption and then go on to , you know , leveraging all the productivity benefits .

Without it it's like , oh , it's a cool tool and I can , you know , it's a good starting point , but I have to do all the work again because I can't trust it . That's , it beats the purpose . So I think that's why it's very critical . It's unique .

I think we've spent a lot of time on that because early on we just figured out like , as analysts cause you know , coming from the hedge fund world , like I , if I can't trust it , I just won't use it . So , like , being able to source everything back is challenging in building the right system .

So you need sort of optimizing the whole stack so you can go back , flow through your system and go back to the primary source document , but I think to answer your question in one sentence , it's going to be very important for adoption , otherwise it will remain in a surface level versus actually being embedded in workflows that give you that 30 , 40 , 50%

productivity boost .

One is so SEC is looking and I think they're trying to understand , okay , what is the all sorts of capabilities that AI systems can do and where you know , like they're trying to get the map right now , right , and so I haven't seen anything concrete yet .

But there has been some talk about being very careful , especially in the advisory business , right , you can't have AI systems right now that you know within the advisory system that can make some recommendations , right , like that is a very , very tricky path right now , and for good reasons , right , like because these systems are generally intelligent enough , but then

you can't really rely on something that important and critical . So there will be regulation . My intuition is on that front and part of that is , I think firms and enterprises have taken sort of like , not a pause , but they're doing a wait and see approach , listening for SEC sort of like commentary , and so I think they will be more cautious .

Where it's directly interfacing with investors , right , and how the outputs of AI go to the investors directly in making decisions . The other aspect of it is like materially non-public information , right .

So if you're an investment bank and you're dealing with a company that's about to go public in three months , obviously it's super sensitive information , right and risking that information to LLM which can get leaked or can used we don't know how OpenAI uses it is also very critical .

So in that approach , I think you might see more private models come up right which takes care of like everything is in-house for that bank and they don't send anything out there . So it's kind of like you solve that problem by figuring out what piece of technology I want and how can I bring that in-house .

As for us , I think right now we're focused on sort of research analysts . We're not facing with , like you know , a retail investor or something like that . So we are focused on as a research platform and trying to being as transparent within the AI models . It's not a black box at all .

You can , you know , flow through all the steps that the AI took to give you certain output . So I think right at the moment , it's not something that's blocking us from anything . But I think , depending on where you end up and like what's the application of AI that you go after will dictate how much AI regulations . You have to , but it is super early .

I think SEC is also trying to wrap their head around , like like , where should we even begin ?

You know , how do you see , you know how do you change that , especially with newer technology . How do you get people using and deploying , and even recommending your product .

And so it's surprising to me when we talk to our customers and when you talk to people like potential , like new people who have not even heard about AI tools , when we communicate to them like this is what can do , and we show them like the excitement is like , oh man , like this , we could save like three hours every day for each , every analyst , or something

like that . You know , three hours every day for each , every analyst , or like something like that . And the other type of customers are like they're more excited . They've kind of , like they know , chat , gpd , they've been trying to like cajole it into doing what they're already trying to do .

They're more excited than us and they're giving us ideas Can you do this , please , can you do that ? And so , like we've seen somewhat of the opposite problem where , like people are coming to us like , hey , can we do this now , can we do that ?

So it's interesting Now , maybe because we're not , you know , in the layer of like super regulated , like we're not trading right , we're not , we don't have a fund that we're recommending right Like .

So I think maybe that isolates us a little bit at this stage , but I think as you move across , you know , within the field , within the domain , to different areas , you might have to wrestle with that . So so far , so you know , one of the things that we have a close beta on is called AI agents , like task agents , where you can just give like a task .

Hey , I'm looking at these top 20 companies in this sector and I want to do XYZ analysis and you can have like a pretty detailed analysis and it just does that for all the 20 companies .

Or it can look at , you know , incremental changes in quarter to quarter about certain qualitative aspects , and that has been getting a lot of momentum and it's like , basically , because it's so generic for the buy side , it's like , okay , I can just have it do things for me .

So that is interesting where I would be in the camp of like , okay , how are we ever going to break into it ? But it's been sort of like the excitement has been quite the opposite .

One thing that I am tracking closely is the latency and the cost of the powerful models and the reason that's and this may not be super important two or three years from now , but in the short term it's kind of like really important because that allows you to figure out what you can do at a sort of like a speed and cost that makes sense , right , like if

you take a week to do something that may not be as valuable versus a day or two hours , right .

And it goes back to this AI agents that need to do multiple things , like hundreds of steps , and like rechecking and verification and all that , and so being able to go down to this , like being able to do all those steps fast and cheaper , is going to be very critical .

So one thing I am like looking at these curves of like how , sort of like how fast inference takes on these models .

So it's going goes back to like algorithmic sort of advancements and chip like GPU , you know advancements and like how , through GPU throughput advancements , and chip like GPU , you know advancements , and like how , through GPU , throughput advancements , all these things like which was very low level things , but it translates in big way for the application layer that we're

operating in . So I think that would be one thing that I'm very keen on learning . And the other thing , if I may , is just the open source models that are . They are getting good . They're not quite there yet , but if they can match certain quality , that would be a huge win for the industry , especially on the private model side and the regulation side .

So yeah , you asked me for one , I give you two .