Trading Tomorrow - Navigating Trends in Capital Markets
Welcome to the fascinating world of 'Trading Tomorrow - Navigating Trends in Capital Markets,' where finance, cutting-edge technology, and foresight intersect. In each episode, we embark on a journey to unravel the latest trends propelling the finance industry into the future. Join us as we dissect how technological advancements and market trends unite, shaping the strategies that businesses, investors, and financial experts rely on.
From the inner workings of AI and ML to the transformative power of blockchain technology, our host, James Jockle of Numerix, will guide you through captivating conversations with visionaries who are not only observing the future but actively shaping it.
Trading Tomorrow - Navigating Trends in Capital Markets
Are Generative AI Agents a Game-Changer for Wealth Management?
Join host Jim Jockle in an enlightening episode as he sits down with Kanishk Parashar, co-founder and CEO of Powder, a pioneer in AI-powered solutions for wealth management. Together they delve into how Powder’s technology is streamlining processes like document analysis, cutting task times by as much as 90%, and pushing the boundaries of operational efficiency. Kanishk also shares insights into the safeguards for AI-driven accuracy and what this could mean for the future of financial services. If you're curious about how AI is transforming the industry, this is an episode you won’t want to miss.
Welcome to Trading Tomorrow navigating trends in capital markets the podcast where we deep dive into technologies reshaping the world of capital markets. I'm your host, jim Jockle, a veteran of the finance industry with a passion for the complexities of financial technologies and market trends. In each episode, we'll explore the cutting-edge trends, tools and strategies driving today's financial landscapes and paving the way for the future. With the finance industry at a pivotal point, influenced by groundbreaking innovations, it's more crucial than ever to understand how these technological advancements interact with market dynamics. Welcome to Trading Tomorrow navigating trends in capital markets. For this episode, we're looking at generative AI agents and how they're changing the way companies operate. By automating time-consuming tasks, these advanced technologies are enabling businesses to place their focus elsewhere. Genai has seen a significant surge in adoption and investment over the past year. According to a recent McKinsey survey, 65% of organizations are now regularly using GenAI, nearly doubling the amount from a report that they ran 10 months earlier.
Speaker 1:For this episode, we're excited to be joined by Kanishk Parashar, the co-founder and CEO of Powder, a leading company in the AI space that is at the forefront of this technological transformation. The former co-founder and CEO of RCI Navigator, kanishk previously worked at CoinInc. As the founder and CEO Powder's innovative solutions use Gen AI to help wealth managers work faster and more accurately. Join us as we discuss their journey, the impact of their technology and how companies like Powder are leveraging cutting-edge advancements to transform business operations and stay ahead in a competitive landscape. Kanishk, thanks so much for joining the pod.
Speaker 2:Thank you for having me here, James.
Speaker 1:And why don't we just start with a brief overview of Powder and your services? But let me just say this is probably our 30th podcast and Powder is the best name of a company we've had on yet, so if you could wrote that into your overview, that'd be great to hear.
Speaker 2:Yeah, absolutely yeah. So I'll give you an idea of how we came up with Powder. We were at our last company. It was called Navigator, we're the same founding team and Navigator was acquired by Adapart in January 2021.
Speaker 2:Now what happened was that, as we were working with tens of different RIA firms, what we realized was that a lot of advisors they told us that their differentiator was not their investing expertise, but it was their ability to build trusted relationship with their clients. And that realization, you know, also led to the fact that the information needed to build these trusted relationships they're spread out in notes, conversations, documents you know, we call it unstructured data and there's a lot of manual effort required to gather and leverage that information. So now, with the launch of generative AI, which is actually great and making sense of all this unstructured data, you know, we felt like we can bring in a new technology that can help advisors make sense of this data with a lot more speed and accuracy, you know. So, after chatting with a bunch of firms, we narrowed it down to our focus. You know, powder, which is document analysis for a state, you know, and the name is just completely random. It's the easiest one to pick and it allows us to move forward with two syllables.
Speaker 1:Fair enough. I was wondering if there was a dry powder angle in there somewhere. I wasn't sure.
Speaker 2:Yeah, actually you're right. There's actually a dry powder angle in there, but most people think of it as like skiing, so I don't mention it too much.
Speaker 1:Fair enough, all right. Well, I guess I'm the curious one. So what are the tasks that your AI agents are automating, and is it just a tackle from a productivity standpoint? And if yes, you know how much time is being saved?
Speaker 2:Absolutely so. Our first agent is able to read a brokerage document and in detail, like it breaks out the tax lots. It fills in missing information, such as if it's missing the tickers. It enriches the data. Like it assigns its asset classes, and that extract is ready to use for a firm. Our customers are telling us that they roughly save about 90% of the time they spend on this activity and, even more importantly, they don't ever want to go back to doing it manually. Once they use our system, they're happy getting rid of this mind-numbing task from their lives.
Speaker 1:So I'm an avid AI user, right? I use ChatGPT, microsoft Copilot to protect work and sensitive data, you know, but tell me with, with the availability of all this other generative AI, um, you know what is what is making your company unique?
Speaker 2:Yeah, you know, um, we're applying generative AI towards operational efficiency rather than investing alpha. So manual work at the firms are mostly done on unstructured information that's coming in like notes and so on, right, and filling out forms. You know finding things on the internet, right. So we believe like being able to leverage all of your unstructured data if it's coming from a database right, that's going to be a superpower. So the operational efficiency is what our aim is.
Speaker 1:And hallucinations. Everybody talks about them. I think the best comment I've heard on hallucinations is it's really the AI learning or exploring its own thoughts. But how would you ensure the reliability and accuracy of the AI's outputs, especially with critical business processes?
Speaker 2:Yeah, since we have started to build this technology, we have noticed the same thing. There's hallucinations, you know. So we have built many hallucination safeguards we call it, you know such as we have the AI read the statements twice and then we compare the outputs, For example. We also do simple checks on top, such as, for example, for a tax lot, what is cost basis plus gain? Does that add up to the asset value? And so, just like this, we do tens of more checks to make sure the output is accurate and trustable.
Speaker 1:So how much time are you spent doing training at this point?
Speaker 2:Believe it or not, that we're using, you know, a base level LLM. You know everything we're doing is built on top, you know. Now we have explored fine tune LLMs and we have explored uh rag implementations as well, you know, but we haven't put that into like general production yet.
Speaker 1:Got it, got it All right, right? Well, it sounds like exciting things are to come, but we'll dive into that. So you know where you are today. What kind of feedback have you received from your clients that have uh integrate powder into their operations?
Speaker 2:so the firm's employees. You know the firms that are customers. Their employees? You know they're. They're high-end trained staff. You know they get paid six digits salaries and they say they don't ever want to go back to doing the eye-watering manual labor of extracting information from PDFs. And the deal is now they can focus on higher value tasks, such as for pinpointing. Their value add to a prospect, which is when you get to receive documents and they're moving faster with less effort.
Speaker 1:And so you know I'd be remiss if I didn't ask. You know, obviously there's a lot of emerging financial regulation around the use of AI. Obviously, data privacy has been at the forefront of regulatory authorities and with broad, sweeping regulations put in place, like GDPR, et cetera. So how are you addressing data privacy and security concerns when deploying AI solutions?
Speaker 2:Yeah. So my viewpoint on this is that generative AI is no different than using cloud solutions, which are already widely deployed at most firms, you know, in the US and worldwide. So, to that end, we're close to acquiring a SOC 2 compliance, which is the industry standard that everyone trusts and relies on.
Speaker 1:Oh, that's excellent. That's excellent. And so you know, what would you say? You know, in terms of the main benefits for generative AI for financial institutions, is it productivity at this point or, you know, is it going to move beyond that, or what do you see the benefits?
Speaker 2:Yeah, I think you hit it on the head. It's all about operational efficiency, right? Instead of going to search for information or figuring something out, get an answer Way faster, way more convenient, as long as it's accurate, so you know. So today you know, for example, it's saving a bit of time and effort. It's pretty good. Tomorrow it's going to be. It's going to finish more complex tasks, tasks that require multiple steps, you know, and so an AI that enables your employees to be more efficient is a game changer.
Speaker 1:Obviously, you know, there's the fear of AI taking over the world and everybody's going to lose their job. What are your thoughts and reactions to not necessarily naysayers, but those who are sharing these kinds of concerns?
Speaker 2:A new technology has downsides these kinds of concerns and new technology has downsides, you know and like. So, for example, one of the downsides of generative AI is that it's very, very good at being wrong. It'll confidently tell you to walk off a cliff. And so I think financial institutions, you know, need to build and partner with companies that have built a series of safeguards which make the AI transparent, you know, and that's the way to get comfortable with it.
Speaker 1:Okay, and so you know, as companies are making these investments, you know how important is it for these institutions to invest in education and training for these workforces, or is it? Or you know, or I've seen studies where people are just turning on new functionality with an AI without any really guideposts or education. I mean, where do you think that importance level should be?
Speaker 2:So there's a quote from the NVIDIA CEO, jensen, that rings true, which is AI is not going to replace you. Someone using AI is going to replace you. You know, and the employees you know of major institutions need to demand that they get educated on this new technology that's coming at them. You know, and it's urgent that the culture is built around using AI, like starting today. You know, and this way, they're ready. When there's a massive leap of intelligence, they're ready to move along with it and if unprepared, they might be left with horses when there's a road full of cars.
Speaker 1:I like that analogy. I like that analogy. You know, one of the fascinating I was speaking at a panel about a month ago and one of the fascinating quotes. We were talking marketing and AI and changes in evolution, and there was this study that was speaking that the best users of generative AI are women over 40 as compared to men, because they, you know, ask detailed questions and, from a communications standpoint, you know, are very exploratory in the way they speak as compared to men, who are very curt. But the converse was interesting in terms of it was women over 40 who were not using and getting education on AI tools, where it was more younger men and younger women who were more excited. So I just think it was just interesting how the best cohort is the ones who probably need the most upskilling at this point or, you know, willingness to change their ways of working, which is crazy. But let me come back to a different question. Right? So you know, in terms of challenges for financial institutions, right so, in terms of challenges, for financial institutions.
Speaker 2:what challenges are they facing when integrating Gen AI, either on their own or through other third-party type solutions? Yeah, I think the challenge is that there's a lot of stuff going on and it's hard to figure out what's really AI, what's really important and things like that. So my inkling here is that to start with a very specific use case, that the institution can build its comfort experience and a level of culture, and then understand the positive effects and also understand the downsides, and then leverage that to jump into more use cases.
Speaker 1:As many companies, especially financial institutions. A lot of them are behind the Gartner hype cycle, if you will, in terms of adoption of different technologies. A lot of them are behind the Gartner hype cycle, if you will, in terms of adoption of different technologies. What advice would you give to these types of financial institutions as they're beginning their AI journey?
Speaker 2:It's simply around the fact that AI seems mystical if you're not a technologist and you really have to hone down and find one specific thing where you want to test it out and understand how it makes a difference. And building that experience and understanding the positive and the negative effects of it because there are some issues you have to address along the way will help the institution and its employees understand how to leverage it. What are the real solutions out there? What solutions are nice to have versus, you know, important to have today and then leverage that to jump into more broader use cases?
Speaker 1:so, you know, let's come back to powder for a minute. Um, you, you teased a couple. Uh, you know, maybe, perhaps future developments. But you know, what future developments or advancements can we expect from powder in the gen ai space, going forward as you take over the fintech space?
Speaker 2:Yeah, you know our customers are guiding us towards our next big agent that we're making. We'll actually launch a meeting note taker that has vertical AI specifically built for wealth advisors. So what I'll do is it'll automatically create a rich client profile that's based on conversations that the note taker is being a part of, you know, and the idea is that it'll capture specific information that a human note taker can miss easily, you know, and they're kind of sprinkled into conversations such as, like you know, clients like interest, their relationships, their values, their goals and more things that the advisor wants to capture, and what this will do is that later on, an advisor can then recall any of this information simply by asking a question in the chatbot assistant, and what we hope is that the advisors can create a very complete and thorough profile and get to really know their clients so they can do a better job of servicing them.
Speaker 1:Well, so you dropped the term and I just want to dig into that a little bit. You said the term vertical AI. Can you explain that to our listeners?
Speaker 2:Absolutely so. A vertical AI goes in depth into one domain and provides a very specific use case that is tailored to that domain, which you won't find in a generic AI capability. So a generic note taker will not help you understand a client the way an advisor wants to understand a client.
Speaker 1:And so I just want to stay here for a second, because this is very different. And you know, I've had one conversation where an individual was talking about assigning predetermined personas, you know, to a particular AI. You know, are you doing any of that to make the note-taking even more specific to the end user, which is that financial advisor?
Speaker 2:We're not doing that yet, but that's a great idea. Oh, okay, that's a wonderful idea. Yeah, and I'll send you a bill.
Speaker 1:I hate to say it, but we've reached the final question of the podcast. We call it the trend drop. It's like a desert island question. So if you could only watch or track one trend in Gen AI, what would it be?
Speaker 2:Yeah, you know it's like the whole industry in general, like open source. Llms are becoming better and better. You know they're becoming cheaper and cheaper. So today, you know we see hundreds of companies launching super amazing use cases, but those companies still have highly skilled engineers as their employees. It's expensive. In the coming years, the lowered cost and technology leaps, I think we'll be able to turn everyone on the planet into an engineer. So if you can think of it, then the LLMs can make it for you. So I can't imagine what happens when the entire human race can be a skilled engineer. There's no limit.
Speaker 1:Oh, I'm going to develop the next Candy Crush. That's all I know. So I want to thank you so much for your time and you know, congratulations on Powder, and you know wishing you the most continued success and we'll definitely be keeping you on the radar.
Speaker 2:Thank you, James, Really really happy to be here and thank you very much.
Speaker 1:Thanks so much for listening to today's episode and if you're enjoying Trading Tomorrow, navigating trends and capital markets, be sure to like, subscribe and share, and we'll see you next time.