In this episode of the Market Call Show, I dive into the transformative role of artificial intelligence in wealth management. Together, we’ll explore how AI is reshaping portfolio management, moving beyond a mere tool to become a revolutionary force in investment strategies. Drawing on groundbreaking insights from Brooklyn Investment Group’s latest white paper, we’ll uncover how AI enables wealth managers to scale their operations, cut time commitments, and reduce costs, all while enhancing the quality of client service.
AI's ability to automate complex tasks like portfolio rebalancing allows managers to oversee hundreds of accounts more accurately and efficiently. AI can cut a portfolio manager’s time spent on routine tasks by up to 82% and reduce computational costs by as much as 85%.
Join me as we discuss why adopting AI-driven strategies isn’t just beneficial—it’s essential for staying competitive in today’s fast-paced investment world. Whether you're a wealth manager or someone interested in the future of investing, this episode offers practical insights into how AI is setting new standards in wealth management, making it possible to serve clients with precision and speed.
SHOW HIGHLIGHTS
- I explore how artificial intelligence is revolutionizing wealth management, offering significant improvements in efficiency and personalization.
- The episode discusses insights from the Brooklyn Investment Group's research, highlighting AI's potential to scale operations and reduce time and computational costs.
- AI technology allows wealth managers to oversee numerous unique accounts with precision and speed, enhancing client service without increasing workload.
- According to research, integrating AI into portfolio monitoring can reduce a portfolio manager's time commitment by up to 82% and computational costs by up to 85%.
- AI-driven strategies are becoming essential in delivering exceptional client service, making personalized investment management more accessible to a wider range of clients.
- AI models predict when accounts need attention, optimizing tasks such as cash management, risk assessment, and tax loss harvesting.
- Advanced AI techniques, like zero-shot and multi-shot learning, enhance the adaptability and accuracy of investment strategies.
- The importance of human judgment in AI-supported systems is emphasized, ensuring decisions are reviewed and validated for consistency and accuracy.
- Challenges in AI implementation, such as handling complex conditions, are addressed by simplifying calculations and ensuring human oversight.
- Continuous improvement and evaluation of AI models are crucial, as AI is set to become an integral part of the finance world, enhancing efficiency and decision-making.
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TRANSCRIPT
(AI transcript provided as supporting material and may contain errors)
Louis: Hi, I'm Louis Llanes, and this is the Market Call Show. Today, I'm going to be diving into a topic that's really reshaping the way wealth managers work. I'm going to be talking about how artificial intelligence can not only help wealth managers manage large numbers of investment portfolios more effectively, but also improve the results for investors, which is very, very important. Our discussion really was inspired by a white paper that I read. It was put out by Brooklyn Investment Group and it's titled AI and Portfolio Management Portfolio Monitoring. It was put out the third quarter of 2024.
This research provides what I consider eye-opening data on how generative artificial intelligence is really able to help wealth managers scale operations, save time and reduce costs and also produce better results for clients. So I wanted to kind of break this down, because this is not an area that is optional anymore. This is something that is actually mandatory now in order to do a very good job for clients. So the first part I want to talk about is just the concept of offering personalized investments, very personalized investment management, more so than could have been done in the past, and being able to do it at scale, doing a lot of it. So one of the biggest takeaways that I've been reading in a lot of different research is that making personalized investments more accessible is really important. Making personalized investments more accessible is really important. So, people you know, historically, separate account management or direct indexing with tax loss harvesting, it was really only reserved for high net worth clients because it was so resource intensive. It took a lot of resources to get the job done, both with technology and with people. But now with AI, wealth managers can really scale personalization to a wider range of clients without there being like a proportional increase in the workload. This is really good news for a lot of investors. So imagine if you're a wealth manager that you can handle hundreds of separate accounts, each with a unique profile. The artificial intelligence can step into that automation and make many of those operational tasks a lot easier to do and much more accurate, freeing up a lot of time for portfolio managers. And this means that, instead of being restricted to a smaller group of clients, wealth managers can actually have more time and they can broaden their significant reach to more people and give you more individual attention. That's a big important takeaway here.
The other thing I took away from my research recently is that the time and the cost efficiency is really going to be improved. So I want to talk a little bit about numbers. According to that research report that Brooklyn Investment Group put out, integrating artificial intelligence into portfolio monitoring and specifically can cut down on the portfolio manager's time by up to 82%. That's not just a little gain in efficiency, it's literally a game changer. And it's not just the time saving, it's also that there's potentially 63 to 85% reduction in the computational costs.
You know, I've been in this business for a long time close to 30 years and actually over 30 years now and you know when we first started rebalancing portfolios, it was very intense and it's just gotten better and better. But now we've really had some breakthroughs on reducing the computational costs, so we're able to get much more precision and speed. So this is achieved by using that artificial intelligence and looking at accounts that need to be rebalancing. So a big part of our job is to make sure that all of our clients have their portfolios rebalanced and we need to know if something needs to be changed. So we're spending our time more on what we should be investing in and why we should be investing in a certain way, but the actual execution of making sure that we're aligned with that strategy is really a portfolio monitoring task, so we can allocate more resources truly on what's more important, which is understanding what we want to be investing and why we want to be investing in certain investments, and more time discussing with clients issues and customizing portfolios, and less time computating. So the other thing that I have taken away is that we've got a smarter portfolio monitoring, really algorithm. So the human brain can do a lot of things and we can really capture exceptions, but we can only do a certain number of things at a time, whereas in the technology world, we can give it guidelines and guardrails and rules to help us make sure that we are being consistent, which is really important in delivering consistent results.
So how does artificial intelligent monitoring work? Well, basically, we have models that can predict when an account needs attention, whether it's deploying access to cash, if it needs more cash or less cash, or if the management of risk is an important element, what's happening with risk and is there some change in the risk relative to how we want it to be to be, whether or not there's an ability and an opportunity to harvest tax losses, to lower the tax bills. The system achieves nearly a perfect recall, meaning that there's almost no important rebalancing opportunity that is missed because these screens are looking at everything. So this predictive accuracy it really ensures that we, as wealth managers, investment managers we can trust our systems and identify the right moments for action without having to sift through every portfolio one by one. That's crucial when you're managing a large number of accounts. So I want to talk about another takeaway that's really important.
Advanced AI techniques now are allowing us to do even more, so the technology is really fascinating. If you use large language models, these abilities really give you a performance that is much more extensive when we train the data, and so we can train the data based on how we trade and what certain things that we really want to be prioritized, and this can help identify even more effectively things that need to be done. And there's different, I guess, methodologies. One is zero-shot or multi-shot learning approaches and, in simple terms, zero shot learning allows artificial intelligence to make decisions with little or no context, which is not always what we want, whereas multi-shot learning allows us to use past examples to further enhance performance, and these techniques ensure that your predictions are more accurate and adaptable to what's happening in the portfolio and in the markets. So, and always, we have decision support. It's always ultimately human-based, but it's just a tool to help us to identify things and then make the ultimate decision as to what needs to happen.
As we all know, even AI, you know it's wise, but you have to trust, but verify every approach. So you want to have the human in the loop, which we do, and we want to ensure that these guardrails are in place to oversee every aspect and to make sure that things are flagged for trading only and when there's certain breaches or certain limits that we are looking at and it's automatically marked for human review. So each one must be reviewed, and that's a really important part of this. So the combination of AI's computational power and human judgment makes for a robust system that's efficient and reliable. That, by the way, is also a big part of how we manage money itself. So there's a lot of human judgment about, for example, the valuation of a stock or what may be happening with interest rates, and that can be overlaid on top of quantitative analysis. That helps to make sure that you're on track and you can use it as guardrails. It gives you much more consistency in your decision making Another, I guess, takeaway that I've gotten from research is that there's challenges and solutions, so let me explain a little bit.
Like any innovation that you have when you use AI and portfolio management, it isn't without challenges. When you use AI and portfolio management, it isn't without challenges. One issue that this paper mentioned, that kind of brings us to light, is that sometimes you can struggle with complex conditions, such as comparing small percentage values, but there are solutions to this. To simplify, you can convert these values into basis points, you can make calculations more straightforward, and simplifying is always a good idea. So I forget who actually said it. It might have been Einstein, but you always want to have the simplest solution that is the most effective. You don't want to have something overly complex, because the more something that is complex, the harder it is, the less reliable it is, the less robust it is complex. The harder it is, the less reliable it is, the less robust it is.
So the research that I've been looking at really is a balance between precision and recall, so you want to note that it's better to have false positives than to risk missing a necessary trade, something that you need to do. So that's why you need human review. So you want to be more stringent and it's better to have something tagged that is a false positive, meaning that it looks like it might be something you need to do, but you don't need to do it, so you can say, no, we don't want to have that, because that makes sure you don't want to miss something that's really important. You don't want to miss training opportunities that could impact the portfolio's performance. So, as I've been looking at this, you know this continuing improvement is really kind of the future and the future steps.
So what's next? I mean, I think there's evaluation of new models, there's new AI type algorithms that are coming out and they're always going to be part of the finance world from here on out. We just have to get used to that because truly, it makes you smarter. Actually, it just makes you more efficient, and the human ingenuity and having that overlay with human touch is so important. But we want to have these algorithms and we want to make sure these algorithms are better and better. That's really all I have. I want to just to wrap it up AI really isn't just a buzzword. It's a really a practical, powerful tool for wealth managers. It allows us to scale our operations, save a lot of time, cut costs, provide better service for you, the client. And, like I said, the future of portfolio management is going to have AI in it, whether you like it or not, but it's always best to be smarter and faster and more efficient with human judgment, because truly nothing will replace human beings in the end, and our clients are not a number, and we want to have that ability to be as customizable and to offer the best solutions that we can at the lowest price and have the best experience we possibly can, and technology and AI really is helping us in that realm.
Okay, so that's it for now. That's the Market Call Show for this round. If you've enjoyed today's episode, don't forget to subscribe. Leave us a review. I'm Louis Llanes. I'll catch up with you next time, where I'll dive into more insights to stay ahead of the investment management world. I hope you have a great day. Talk to you later.