Speaker 1 : Hello and welcome to the Center for Webcast and Wealth Management. Where to begin? Sponsored by Broadridge. My name is Peter Carlos. We'll get started in just a moment. But first, I would explain the player on your screen. On the bottom, you will see the audio controls. The sliding bar allows to adjust the volume and the circle. Double arrows can be used for this action. Media player. Beside the audio controls of the text box you could use to submit questions to our panel. We encourage you to ask questions throughout the presentation and please know all.

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Speaker 1 : Will be addressed anonymously beneath the video. One thing is for a series of tests. The more information about our speakers.

Speaker 2 : Now let me show you some moderate levels. But 30 Liz is a financial journalist with many years experience.

Speaker 1 : Covering the industry and will be leading today's discussion. This.

Speaker 3 : Thanks very much, Pete, and good afternoon or good lunchtime, everybody. Welcome to our webcast and Wealth Manager Management. Where to begin? Sponsored by Broadridge. I'm sure many of you will know that artificial intelligence adoption is accelerating across wealth management and firms that delay technological investment risk falling behind their competitors. But it is tough. And the critical question now is should we adopt? I. It isn't one bit. How if, why, when? Who? And all the other questions. The challenge, however, lies in knowing where to start. Which process is a prime candidates for automation and how to ensure that teams are equipped to manage and lead this transformation. Also, how to keep them up to date. It's not a second forget idea. So thank you for joining us because today our expert panel will explore the key decisions both management firms have made to successfully implement, from identifying project ownership to fostering a culture of upskilling and commitment across internal teams. We're also going to discuss how employees it was mentioned firms can prepare for and thrive in the age of AI, ensuring they're ready for the destructive, disruptive potential of this groundbreaking technology. And we will need your questions along the way. I have got many, but please, as Pete said, please do put your questions in as soon as you can. We'll get to as many as we can. So my panelists, please welcome them. We have Alicia Rich, who is head of Clients and Advisor Digital Enablement at Broadridge. Jeff Gordon, who is Senior Managing Director and Chief Investment Officer at Lido Advisors, and Chuck Bennett's assistant Professor of Financial Planning at the American College of Financial Services. Panel. Welcome very much to the webinar on the jump straight in and tell our audience that if you want to find out about any of us, me included, you could hover over our faces and names in your player. Jack, I'm going to come to you first. And I, as we know, is transformation in many industries. But in wealth management specifically, we're talking about today, can you ease in gently and explain how it might affect key areas of wealth management as a business? And I'm looking at funds and investment options. I'm looking at financial planning and the changing face of the client. Let's start there, Jeff, and then shall come to you.

Speaker 2 : Perfect. You want me to ease you in gently? Yeah. Well, so, look, I think. I think the applications of of artificial intelligence, whether it's or traditional machine learning, slash the AI or, you know, this this newer gen AI technology that that everybody's kind of very, very excited about right now. Obviously, the applications are vast. I think I think there's a tendency to go to the obvious and, you know, start talking immediately about how this can help with managing portfolios and in, you know, investment management and, you know, even get to the point where it's assisting you with stock selection and research and things like that. I think that if we think about things a little bit. Out of the box. You know, this could help with a business development in a very, very significant way, whether it's helping with with the management of the relationship, with identifying certain traits, certain behaviors that that may indicate. Whether an advice, whether a client is more prone to prefer or. Or like certain types of investments, whether or not a client. May or may not have certain. Matters are around risk that that that need to be addressed that you know don't necessarily come up in an interview. A lot of that can be extracted from data. A lot of that could be discovered simply by patterns and behaviors and things like that. So, you know, beyond the obvious, I think there's there's a lot more. And then, of course, when we get into the operational issues. You know, something as simple as a chat bot that, you know, helps our people find what they need. You know, if you get to a wealth management firm, there's a lot going on. There's a there's a lot that could be needed at a given time. And these types of these types of platforms can help a lot with them.

Speaker 3 : And if we look at the client expectation, we've got well, on our focus, I'm just looking at my phone pings every 20s with stops. Tell me about this and can I help with that? And we will. We live in a very different world from when wealth management and a lot of wealth management businesses were established in what it was face to face. It was, let's go meet, have a drink and have a chat and sort out my my life financial plans. That's kind of gone in a way. So. So how can you how can and if you can kind of build on what Jeff said, how can all these everything that comes on with the massive I am brother, which seems to be expanding by the day, how can this help?

Speaker 1 : Yeah, absolutely. And you know that that brings up the point that really our clients, the modern client, really expects this real time insight and personalized advice, in part, like you said, is the the fact that we have information availability constantly almost to a fault, especially when it comes to consumers making investment decisions that, you know, requires a lot of synthesis, synthesizing all the data that's out there, but also taking into account their personal situation. So from a product standpoint and solution standpoint, I think, you know, we've always had as advisors, we have had a vast array of data regarding our clients, their behavior, their attributes, their goals and objectives. But with a with machine learning, there really is an opportunity to get to this next level of personalization, taking into account all of the other factors. And while for some firms that is adequately done maybe through the services that they currently provide, it does offer this opportunity to fill the gap of taking care of some of the clients and future clients and beneficiaries of clients that maybe don't fit perfectly into our client base now, but they will be good clients. That allows as advisors and as firms. I think some of these tools and resources are really going to be an incubator of sorts for the next a client for that next best client, and really offers the ability for us to provide services in a cost effective manner to those that maybe aren't receiving those types of services, but should.

Speaker 3 : Right now it does in wealth is a broadening a broadening church doesn't. It's that, you know, wealth is no longer just about being, as we would say in the UK, incredibly loaded. You're not alone, but you have different conversations over that and I've said that. But it's not about a massive amounts of cash. Your wealth is your financial wellbeing, is your it's your financial future. And this is this is a challenge. What you are hearing from your clients about what they want and the challenges that they find is, is as well and the platforms they've already created.

Speaker 4 : Absolutely. Jeff and Chet hit on a couple of key themes. You want to understand how A.I. is going to help you from an efficiency perspective, from a productivity perspective. And, you know, there is a ton of information that advisors are bombarded with day in and day out. And Jeff, one of the points you made about patterns and behavior, you might not have a lot of time to pay close attention to everyone and everything they're looking at or paying attention to. But what is going to help us do with machine learning and with generative A.I. is to be able to identify those patterns. Look at that and then nudge the advisor with what is the next best idea for an engagement with a client. And the big thing that we've been hearing and discussing with our clients is, you know, the communication frequency has a high correlation to satisfaction. And if I'm not hearing from my financial advisor, I think they might not be paying attention to me or caring about what's going on in my life with some of these tools. It's bringing that information to the forefront, pushing it to the financial advisor. So that way they can very quickly react to news that might be happening or a transaction that might have happened then they might not have anticipated for a client and be there too, to reach out and just acknowledge that we're here. We understand something's going on. How can we help? So it's making the advisors work a lot smarter, a lot easier, and a lot faster than they may have been in the past.

Speaker 3 : And, Jeff, I'm going to come back to you on this one, because when we talk about A.I., we talk about machine learning, we talk about tech, and we talk about chatbots. It does seem that these into personality or a lack of personal approach, whereas it seems from what you guys are saying is this can actually use this technology, could actually enhance the relationship and enhance the communications between the client, which I think a lot of is that in the wealth space, what kind of space people might be going to.

Speaker 2 : Yeah. So, you know, I think the heart of this is actually, believe it or not, a truth to power issue of what I can actually do. It's, you know, I don't know what's you know, what's going to be 50 years from now. But right now, it really cannot place you replace your advisor. It is a tool. It is another arrow in the quiver, if you will, for your advisor to use, if implemented correctly, for analysts to use if implemented correctly for operations people to use if implemented correctly. These are these are simply tools that that we can use to make sure that our business is running the way we want. And that could be anything from, you know, like we talked about ensuring communication, but it could also be making sure that that communication is professional and non-confrontational and making sure that departments interact with each other in a more efficient way. And that helps with communication instead of getting into situations where we're, you know, we're playing a game of broken telephone and, you know, the guy on that team said this and I need to relay that to the client. These are all things that can be helped significantly through proper use of these tools. So I again, I just don't I don't see this being in a place where where it could really replace the advisor right now. You know, I know the obviously we all know the stories and know the the claims, but I just don't see that yet.

Speaker 3 : Now it's a little bit like journalism, but I'm not going to go down that road. At least I'm going to get back to you. I said at the outset and we can see there's almost a polarization happening in the industry. You have some ability grabbing the idea of what this technology can do and running with it and or maybe just taking the first steps. And we have others who are still very much in need, not for many things or not for me yet. Can you explain and then I'll come back to you, Jeff, as well. What, what what are the leaders, what are those who are really pioneering this or at least grabbing it and having a go? Can you talk me through kind of the benefits in terms of cost efficiency and looking more long term, not just the quick wins and you if you can kind of just outline.

Speaker 4 : Sure. So as you said, there are varying levels of experimentation and adoption with A.I.. In a recent survey, we found that over 72% of firms are going to prioritize AI initiatives in the next two years, which is a great, great percentage of of wealth and financial services firms. But when you look at what the leaders are focused on versus what some of the new joiners or laggards might be focused on, you can see a vast difference. So to your point right now, the leaders who are investing early, who have a strong foundation, who have a strong culture, who have a skill, their talent, they are seeing ROI benefits. They are able to say, I'm going to reduce costs because we're going to stream line. Tasco Reduce reliance for the financial advisor, for their teams, for the operations teams, and replace that with automated processing. Also efficiency. And I think, Jeff, you had to hit on it as well. There's things that if you could do straight through and you don't have to have a person in the middle and you don't have to have somebody babysitting step by step. You're just providing much more overall efficiency, not only for the advisor, but for that end client. And I think that when you take a look right now at firms, your Schwab's your vanguards of the World. They've done a lot to enhance that operational efficiency, eliminating repetitive, repetitive tasks. And now they're starting to double down their efforts on how they improve that overall client experience. So going back to how do you deepen that relationship with the end client? How do you focus on their experience and make that a more premier experience and bring in different tools for your end investors?

Speaker 3 : Because this also gives you the opportunity. Jeff When we spoke about this, when we had a chat last week about coming up with innovative solutions, that might have been a lovely idea to do 2 or 3 years ago, but imagine how you would have me to do it. Just you would have cost $1 billion, but you can do new stuff now. I love what you tell us. I hope you can tell us the specifics, but if you can give us an idea.

Speaker 2 : Yeah, I mean, so look, you know, a big part of, you know, something that's that's been very, very big over the past few years, which, believe it or not, is, you know, an offshoot of the most basic versions of machine learning is custom indexing. Now, with a little bit of elbow grease, with a little bit of coding knowledge, with a little bit of understanding about how this type of thing works, it's it's very easy to sit down and basically say, all right, these are the tasks that need to be performed. This is what has to happen and use AI and use machine learning to codify those tasks and develop your own platform. And we use it for things like tax loss harvesting and finding stocks to a to use in order to replace stocks that we'd like to harvest without sacrificing much performance. We use it for our options pricing approach and in constructing our defined outcome trades. We use, you know, we we develop these models internally. But, but but our approach has been not, hey, select the stocks for me or hey, buy this for me or do that for me. It's. These are the steps that an analyst would have to take. These are those two and three minute tasks that everybody has in their job. We all have those jobs. We all have those things that we have to do. And they take 2 or 3 minutes and they're repetitive and it's every morning. And, you know, by replacing that with the machine, I can let the portfolio managers focus on other things. Or by taking something that is, you know, a little more systemic in nature, like a custom indexing portfolio. There's not much in the way of of of securities analysis and valuation that needs to be done when it comes to something like custom indexing. I can basically codify all of that using a series of machine learning algorithms using a series of. Platforms that we develop and have my own have my own platform, and that saves money for my clients. That saves time for my analysts. That means I don't have to hire people. And that means that. Very, very, very important once it works, right? It works, right? Yeah. The human the human element stays where it needs to stay. And that's in the discretionary part of the business. And, you know, the mistakes basically disappear. Which.

Speaker 3 : Which is which is very important at this part of the holiday season. And I'm going to continue. I can see if you have anything to add on that. And then we'll go on to who is not in, why we have so many like us in the industry. So yeah.

Speaker 1 : I think building on Jeff, the, you know, the customization of this and applying it as it needs to be applied to the practice, but then also looking at it from a professional standpoint. So one of the, you know, one of the applications that we look at constantly in research is what are some of the driving factors in investment choice in product? You know, some of these behavioral components that as a profession, when we are doing client onboarding, we're really good at gathering the information about assets and liabilities and even, you know, some degree of risk tolerance questionnaire. I think the more that we are able to integrate the research that we find into that practice element, the barrier has been traditionally the that gets so convoluted when you start layering on top of the optionality these behavioral attributes. Well, if I can help that and I think the initial way is really in proactive client outreach. So imagine if you woke up and your CRM all of a sudden because you had the in your fact finder, you have these behavioral attributes and you're able to kind of codify in the system what type of client this is. And so if the last five day trailing S&P or VIX or whatever the measure is, is such that you should be proactively reaching out to that client or a staff should be proactively reaching out to that client rather than the next day receiving that call and, you know, reactively having to talk someone off the ledge. That helps out a lot. I think that helps in advisors being able to market themselves differently. It helps in just client satisfaction and at the end of the day it'll help in portfolio performance as well, right? Because you're, you're keeping them from and at least you mentioned it, you're kind of nudging them into doing the right thing with this assisted device, with this. I add on you laggards, whether it's on the consumer space or in the industry. I think it's just inherent of any technology like this that there's going to be skepticism. Most polls that you read when they're asking consumers about their confidence or trust, there still is a lot of caution amongst the population when it comes to exactly what it is. And some of that is because that there's fears of its impartiality. There's fears of the bias that it inherently has inside of it, depending on the models making ethical decisions across the board. There's a lot of those things. You know, the interesting thing, though, is that when you look at like adoption and use, there still is this kind of effect that we see this this bias where there was one state where 42% thought that I would have a negative impact on society, only 27% thinks it has a negative impact on them personally. Right. There's still this like good for me, but not me type of mindset of like, yeah, it's good. I'm just not going to use, you know, if it's good for someone, but not for me. So I think the more that some of these disclosures and trust in these systems takes place, the better off. You talked about it in the application of journalism, which we don't want to go down. I think we can go down this path or not. But there needs to be regulation, right? There needs to be a regulatory component about it to where that trust and confidence can be built up in the consumers. That's not in my pay grade, but something that should be absolutely talked about.

Speaker 3 : At least I'm going to come to you and ask about what I saw in this lack of understanding or rather this. We think we kind of know half the story by so far because we've seen Chargeability and other j AI tools are available. There's lots and lots of news and but we don't really know unless we interact with it. And as I said last week, what it really means. And is this something that you hear from friends or people who are looking at it when they specifically in this larger area that I don't have time to train my staff, I don't think my staff are going to get it. What kind of what kind of stories are you hearing from that?

Speaker 4 : There's quite a few. I'd say the biggest one is I hear all this hype. What does it really mean? And so to your point, that lack of understanding and really looking at what is the issue, right. And to me, what I see is the biggest problem is they don't have a strategy. They don't have a problem that they're trying to solve. And so the idea that I want A.I. everywhere, or I just want to have something that I can say I'm offering to my advisors is not a strategy. You need to understand what's the problem you're trying to solve? Where can you start? Focus the teams. Do you have the right talent? Do you have the right resources? Do you have the right support across the organization? That's what firms have to actually answer for themselves before they then can say, okay, let's let's get started and let's look at something.

Speaker 3 : We segway beautifully. On to the next part of the conversation. How do you start on the journey to adopt A.I.? How do you, even in this big world of everything being zeros and ones, how do you start implementing that into something which may have just been on a ledger or sat on a black book and in someone's brain for decades? Jeff how do you first start? What how do you identify strategic priorities? What are you gonna tell me? Give me a blueprint.

Speaker 2 : So I think I think there's a couple things. And I think Alicia really raised a very, very important point here regarding strategy. You know. Don't go out and look for a problem. I mean, don't go out and create a problem to solve like that that I don't think that that should be the objective here. But, you know. One of the things that I think is important to understand and I think very, very few people actually understand this, is that the implementation of A.I., if we take the people that work in the AI business, if you will, you know, we'll call them data scientists, for lack of a better description. You know what they generally don't have. Is the subject matter expertise that that's just you know, what they have is the ability to understand statistics and complex models and quantitatively driven systems and build those systems in data warehouses. The list goes on and on. They don't know your business. They need you for that. And vice versa. So, you know, and I think that this is a big concern with people that simply don't adopt. Is I don't understand anything about this. I don't know how this works like this. You know, I want to pick stocks or picky eaters and, you know, buy a few bonds and take care of my clients. And, you know, if it ain't broke, don't fix it. And I think that. One of the ways the only thing that really works at our firm is, you know, you have to have a proof of concept and you have to show value. One of the my approach at Lido was I'll put in the work first. All right. I'll I'll I'll make the initial investment. I will take the time. I will do it. And then I will show the powers that be. How this helps the firm. And then that really needs to be the approach. It's. It's. You need to have proof of concept. And once you do have proof of concept and you're able to show this is how this helps you. It's not necessarily going to solve a problem. But it may uncover inefficiencies that you didn't even know were there.

Speaker 3 : Yeah.

Speaker 2 : Check that. That's the secret.

Speaker 3 : How do you. Is this something that you've seen as well? It seems very much that you've got well, somebody is in a position to say, look, I'm not a tech company. What do I need to do this? And it's assessing what we can do. And as Jeff said, you don't it's not a solution looking for a problem. You have to identify how it's going to benefit you and how do you how do you just let out perfectly how he did it? What are you saying? How do you. Is there a step by step guide that we can print out and hand out to the audience?

Speaker 1 : If only we could we could add a, you know, subscribe here now, link, and we will make, you know. Yeah. Just hitting on it. Like I think the main thing is there does have to be this kind of cross-functional leadership team when we're talking about first steps because there are a lot of brilliant people like like Jeff said, of, of data scientists and computer engineers and the companies that I've worked with and talked to in the earlier stages of this from an academic standpoint that are trying to seek out, hey, what is some of the training data that we can utilize to make these more efficient, more specific? A lot of them, just because of the way these tech startups are funded through capital or whatever, they're looking for that kind of quick subscription based. How can we get people, you know, to to adopt this in some of the low hanging fruit on That has definitely been some of the automation of tasks on client onboarding, whether it's feeding in PDFs and it's, you know, stripping everything out, which that's been around for a while, but that's A.I.. Whether people understand what A.I. is, is a whole nother thing and how it actually probably is already integrated into their, their business and once, you know, some shape or form. But it is some of these next gen things where we're talking about how do we make the client experience better, how do we spot through some of the machine learning things, patterns like we've talked about already that are things that we should be proactive about in a client situation or an asset movement situation. I think all of those are where it definitely can go. But the biggest thing is this cross-functional, because Jeff's to, you know, these there's a lot of brilliant people out there, but the domain specific knowledge in the application that lies with you, the advisor that lies with us as a profession, to be able to kind of guide it so that we're not okay, great. Here's a solution that I can pay for that my company is paying for. Where's all the problems? I can fit in here, but rather trying to make it so that it is it is a bit more bespoke and custom to what the advisor and their clientele and community.

Speaker 3 : I mean, can I bring you in on this? Is this. This is sound like lots of discussions that you hear. And, and also, can I ask you about the bespoke versus off the peg as someone who's obviously a follower of fashion? These are very familiar to me, but I think it is. Is there a right way or wrong way? Can you just go and take a couple of bits that already made that we're going to just about and then create something a bit more specialized? How does that work? Because it will you know, there's such a fast and so many people offering you solutions to a problem and you haven't thought of how does the same kind of meld together?

Speaker 4 : And so I'd say it really depends on the use case. One of the examples we've talked a lot about our chat box chat box could be something that you could take off the shelf. You could introduce certain guardrails, certain policies, you provide your content, and you either internally or externally, now have a way to answer questions quickly, get information direct to who needs it, the advisor, the end investor, when sometimes it comes to things about investment policy or the tone that you would use in crafting messages to your clients. That might often be a little bit more bespoke when it comes to generative AI because of how you want to train those models to make sure that it's representing the brand, representing the advisor, the firm, etc.. So I'd say from the experience of what can I get off the shelf from and what might need a little bit more curation that's going to really depend on, on the use case. And I think where I see the industry going is just like many technologies, there will likely be some standards that are introduced for financial services. We've got the same regulators, there's different things we need to address from a privacy perspective, from a control and risk perspective. So you'll start to see things that will be more out of the box, that have the guardrails that you need that make people feel more comfortable, that make the AI more explainable. And those are the things that I think the industry needs to come together to provide for financial advisors and for institutions just to help with that trust level that that the advisors need.

Speaker 3 : And it's there at the moment because it is so new for many. We mentioned some of the largest operations in the world earlier on, but we know that there's lots of smaller firms. Is there any way of kind of getting a use case where you you can offer this, you can do in-house this you need to outsource? Or is it is it really a case by case basis in Asia? And then I'm going to ask you some of that, too.

Speaker 4 : Maybe not case by case. I think, though, when you're thinking about what are things that we're putting when it comes to investment recommendations or how do we think about recommendations, firms are likely going to want much more control over that, and that's likely something they're going to want to insource when it comes to customer communications, customer support, those types of tools. The banks have been using them for a really long time. And so there's a lot of learnings, There's a lot of really good packages available. Those are probably use cases that are much more easier to say out of the box.

Speaker 1 : Yeah, yeah, yeah. And I think on that, I mean those the availability of solutions, this really is a solution depending on the time that the user wants to spend to know how to use the system, it really kind of democratizes, if you will, the access to it. Because if someone is trying to use it for client correspondence or whatever, it may be a generative API solution like a GPT or a clod or one of these, you can pretty quickly learn how to do prompting in such a way that it gives you your voice on behalf of the client. Right? Like it's it's going to give the voice that you want to project the message that you want to project the tone with sufficient prompt and with sufficient conversations with it. We early on, from a research standpoint, we're we're we're presenting a lot on this and we did kind of come up with some of those prompt sheets and everything else where really the more it's already trained and I'll give a disclosure right, of to the extent that you want to train it further on you or your information or how you operate, obviously putting any kind of client information into a generative AI, web based solution, app based solution, you don't you want to have it sanitized, but the more you can have your voice come out, if that's what you're using it for, a small firm can do that today. They don't need to have some big fancy off the shelf package that they may not be able to justify. But if it gains a little bit of efficiency in their practice, then you know that that's a win.

Speaker 3 : And a promise. We are going to get to your questions in a minute, but the Segway here is just to perfect data and to bring you in as well. Data and gestures talk. My information. Information in data is the goals of Aria and all of our era. I don't know which is the more expensive now. I mean, how do you approach data assessments and clean up and security? Just how do you do it? We've got about five minutes on this, so. We can discover a whole different webinar about how do you start and how do you get this right?

Speaker 2 : So. I think. If you have I mean, if you're a so-called laggard in this space, I'm willing to bet dollars to donuts that. You are not storing your data. You're doing what you need to do in order to meet regulatory requirements. But, you know, there's no there's no data warehouse. And in all likelihood, if there is one, it's not on a server somewhere or in the cloud or or anything like that. Wealth managers create enormous amounts of information. They create enormous amounts of data. A lot of this data could be used to train and feed models and, you know, in different parts of the business, whether it's the operations part, whether it's the business development part. All that information can be used in order to improve. How things work there. Data. Data. Data Scrubbing is. Is a challenge. Very, very few things come in nicely and cleanly, you know, like going on to a Bloomberg terminal and asking for returns from, you know, for the past 20 years on the S&P 500. It doesn't come that way. It really does. There's there's a lot of work and there's a lot of kind of. Figuring out and you're going to have missing data and you're going to have periods of time where it's just not there and you'll need to figure figure out a few things or go back in and fix it in there ways to deal with it. But the first and most important thing is start collecting your data. Start storing your data. Invest a little bit of money in a data in a in a firm database. We did it. That alone, by the way. So much for efficiency. Whether I, you know, many times it's much easier for me to query my my my firm's database as opposed to going to my reporting system and trying to get information from there. And it's the same data. In fact, a lot of the data is coming from our reporting system, you know, and we're just storing it in our database. But because it's raw, because it's, you know, going to get nerdy here for a second before it's because it's done in sequel, you know, a query that takes me 15 or 20 minutes in, you know, in my reporting system takes a few seconds when I'm when I'm querying my database. That alone saves a bunch of time. So querying, like just storing your data, making sure that that, that it's constantly being updated. It is a process. It is a worthwhile investment in that if you're going to start to develop in-house, it's a necessity.

Speaker 3 : Yeah. Alicia It's the incentive. There's no escaping it. Data is coming for us whether we like it or not. Everybody. I'm just little memory sticks. I'll go lying around in this office, but it's to do anything. Everyone is going to have to get a handle on the status. Is this something that you hear? And how did you come to advise clients? Because you can't. How do you get an elephant piece by piece, that kind of thing.

Speaker 4 : And it's literally the first conversation. And the best advice that we have is start small, but think big. You know what the critical domains are that you need to focus on. You need to look at them for accuracy, consistency, completeness for you to understand ongoing. How do I keep that as crisp and clean as possible? And then you can scale and start to expand beyond that and, and add in information from us, your CRM tool or from a financial planning tool. And sometimes, you know, directly from the email communications that your clients have. But to me, it's really about making sure that the core critical pieces are there are accurate, reliable to Jeff's point, that you just start collecting the data. Maybe you don't know how to clean it just yet, but start figuring out how you can collect it and retain it. And then as you start to test and continue to evolve your program, you can then get the better ideas on how to keep this moving forward. How do I make it more or more manageable?

Speaker 3 : Fantastic. Thank you. Right. I promise something from a single webinar, but now we are getting to the question. Thank you so much for sending so many of them in. Everybody, as Pete said at the top, that will answer anonymously. Some of them are very specific, so we won't be able to go into them, I'm afraid. But we have a great question. And how can I I'm Chuck, maybe I'll ask you is how can I assist financial advisors in acquiring new clients and generating leads?

Speaker 1 : Yeah, I think there's a couple of ways on that. One. I think from a I hinted at it. The more that the clients are feeling like they're getting this customized, almost like I have my private person on call all the time, whether or not it's you directly. But if it's facilitated through utilization and leveraging some of the AI solutions that through referral, through retention, through all of that, is a way we did a thought experiment using generative AI last spring where we had it, we trained it in the prompts to think as an advisor that just got parachuted into a market and like had it think through what would be the best marketing strategies given the demographics, given the locale, given the all of these things and had it brainstorm strategies as more of these generative solutions and custom generative solutions begin to continue to have reliable and consistent integration with search and with actually like having that in the background, some of the more complex and more advanced and precise large language models are still working at that. And the ones that already are on search, anytime you Google something now, right, like the first thing is not the sponsored search, but rather a generative search that doesn't have necessarily the same precision in the limb with reliability and accuracy within its comments. But and it doesn't allow it to be as creative when those two things begin to and continue to merge that strategy, that thought experiment of, Hey, you are my marketing expert, what are ways that I should go acquire a new client? Like what are the ways? And then you almost even train it to where, you know, the thought experiment we did is what are the largest? Employers that are local in the in this zip code and then, you know, setting an alert if there's a layoff, setting alert when there's an announcement of a retiree, you know, these different types of things are just those those additional touch points that allow advisors to focus on, you know, really honing and relate that relationship and some of these things that they impute their thought process into the eye that just allows them to be more efficient in some of those means.

Speaker 3 : Everything that you guys are talking about, it seems very much that this is these are tools and applications that enhance what you already do that are in place. Anything could replace the man hours and the or the woman hours and replace the hours that are put in by people. You know, they do it, but it's allowing you to do more interesting stuff. And we've had a couple of questions asking for specific applications and tools. I don't know whether anyone wants to dig into that. Certain areas that are really useful. Aleesha Jeff, Julie, chime in on anything specific, things like this and that is really useful. But was it a case of knowing what your problem is before you get to your solution?

Speaker 2 : Honestly. The thing I use most. I know how I come across in emails.

Speaker 1 : I really.

Speaker 2 : Do. I know how I come across in emails. I, I, I don't know what it is. I can't help it. This tool helps out with that. Like you would not believe. And again, that's not something that I need to develop in-house. That's something that's available to the masses. That's, you know, that's easy. And that is that has saved me. Thousands of hours per year, probably reviewing emails, looking for, well, maybe I should phrase this differently. Maybe I should say that differently. And I think that that's the most basic, simple, straightforward and frankly, elegant solution that that that you can think of right off the bat without starting to get into, you know. Really, really, really far down the rabbit hole.

Speaker 3 : We don't have time for the rabbit hole into our last couple of minutes. And Alisha, is it instead of kind of finding specific tools, uppercase that are going to be, you know, here's my top tips for Christmas. Is it a case of finding your problem and then working back to the solution?

Speaker 4 : Resounding Yes, I think it's you. Where where do we either have operational efficiency issues, where do we have productivity concerns? Where is it that we as a firm or as a team, we want to be able to better serve our clients, better serve ourselves with some of the, you know, reducing manual tasks, coming up with that, and then being able to identify what's the best way to do what do I need in house? Do I need a partner? Is it something that's that's already available to just points the copilots that are out there? They're wonderful. They're also helping me learn the technology more so I can feel more comfortable with it. So when I'm sitting with my clients and trying to guide them on what they should be looking into or what direction they should go, maybe to just talk the talk. And so sometimes just using those tools that are readily available are going to get you more prepared for what else could be next within my practice.

Speaker 3 : Okay, you've got 30s because in the spirit of the season and generosity, we're taking a couple of minutes of the audience this time. I'm Jeff. Where do you see evolving in the next decade in the spirit in the area of wealth management, Let's not go into anywhere else because we don't have the rabbit hole thinking about where and say I'm going to expand and develop an email function in the next ten years.

Speaker 2 Do I think that I will result in a lot of the back office functions being automated, a lot of the things that need to be done today, whether it's via the custodian or internally. I think a lot of that stuff is just going to be done in an automated way using A.I.. I think, you know, that that's going to create a lot of inefficiency in places like onboarding or in places like an account, new account opening subtasks, things like that that I imagine will be almost entirely automated by the end of the year or by the end of the decade. Now, there's also something to be said about. A certain subset of the population that doesn't want to talk to people.

Speaker 3 : I mean.

Speaker 2 : To me, it's weird to meet you, too. It's strange, but they would rather have a robo advisor. They would rather have somebody or something take care of this. They don't want to do quarterly meetings. They don't want to do a regular reviews. They would like to log into a website and see that they've made 15% this year or whatever. And I think that those are I mean, they're getting better. So, I mean, there are a lot more thoughtful, if you could say that there are a lot more higher there are much higher quality than they were originally. And I think that, you know, that's going to a allow for. Smaller clients to begin investing with professional help and be once again clear the plate for advisors to deal with other things. But that's that's where things go over the next ten years.

Speaker 3 : For sure. And you've got 25 seconds. You know what's going to happen in a decade.

Speaker 1 : I think ironically, even though we're talking about an advanced technology, I think it allows our profession to actually kind of get back to humanization. And what I mean by that is that, you know, we're we talked about it before when we were when we were preparing this is a helping profession, wealth management, financial planning. These are we're humans dealing with humans helping people set and achieve their financial goals through strategic, you know, whatever your mission statement is. Right. A lot of that does require this cognitive ability to do all of the analysis and everything else. But I think we can all agree some of the best people in that are not necessarily the best people when engaging in, you know, the emotional IQ, the IQ aspect of it. So I think to the extent that we have people that are in the profession or that want to be in the profession that are amazing with people, but they're intimidated by some of these analytical things. They're intimidated by some of these these tasks. It allows people, consumers to have a little bit more of that humanization, possibly with the caveat Then like Jeff mentioned, and I think we've talked about, it opens the door for us as advisors to be able to kind of create the A.I. version of us so that we can spend time with clients, we can spend time with clients and prospects as us in a more efficient manner.

Speaker 3 : Thank you, Chad and Alicia. You got four seconds now, 38 seconds. I think they said.

Speaker 4 : Chad said it best earlier, are going from reactive to proactive. These tools are going to continue to support advisors, make them work more effectively, make their clients happier, stickier, more loyal. I see there's a lot that this will bring to enable financial advisors.

Speaker 3 : Fantastic. That was superb. Thank you to my panel. Thank you to the audience to stay with us. I'm taking a little bit extra of your day and we'll be recording available after these people tell you all about it. Thank you also to Broadridge for sponsoring. Have a lovely rest of the day and a lovely rest of the year. Thanks for doing this.

Speaker 1 : Thanks, everyone. Everyone loves his. Thanks to everyone for attending the webcast to our panel, Alisha, Jeff and Chad, as well as to you for moderating our discussion. And again, to bring you to Broadridge for sponsoring our conversation today. Please check your email for your future webinars as well as for access to the recorder. Archive for this one. Thanks, everyone, Had a great day.