Jack Sharry: Today, we’re going to talk to two collaborators who have built and continue to expand on a state-of-the-art comprehensive device platform. They have been working on together for a few years. So across the industry, we have seen significant investments in time, talent and resources in the development of ecosystems or platforms, designed to maximize the financial outcomes for investors, advisors and firms by managing multiple accounts, products, models, and income streams. Today, you will hear from two leaders who are doing this very successfully. And they’ll give you some insight as to what they do and how they do it. This week, we’re speaking with Deep Srivastav, who’s the Senior Vice President, head of digital investment solutions at Franklin Templeton and Martin Cowley, my colleague who is the EVP and Head of Product Development at LifeYield. Deep and Martin had been working together for many years on one of the most advanced platforms in our industry. And that is what we’re going to explore on today’s podcasts. So Deep Welcome to WealthTech on Deck, Deep and Martin. Thanks for returning to the show. Good to have you guys here.

Deep Srivastav: Thanks. Nice. Good to be here.

Jack Sharry: So Deep, let’s start with you. Please start by describing your role at Franklin Templeton, and how you work with AdvisorEngine I know it’s important partner of yours and all this. And reporting, Franklin Templeton affiliate. So tell us a little bit about go goals. optimization engine is an award-winning planning and implementation tool that you’ve gained great renown, and it’s about to get better. So first, I want to just establish your role. I work with AdvisorEngine and if you want a high-level view of what goes about.

Deep Srivastav: Sure, thanks, Jack for having us on the call. So I oversee our digital investment solutions. And what it effectively means is that we try to bring our investment capabilities to market in the form of digital products. And that we believe that digital is the way to go in the future. And this allows us to create those interlocked set of capabilities that we believe our clients advisors would like to integrate into their ecosystem. And as a part of it, AdvisorEngine has become a critical part of that extension, because whatever investment think about some of these capabilities as our investment capabilities on a chip, and you’re trying to bring it to the market. And AdvisorEngine plays a key role in completing the technology stack that helps enable advisors to participate in it. You mentioned the goal optimization engine. So one of the key digital products that we are trying to bring to life is the goal optimization engine. And what that does is that it creates a more personalized investment capability for our clients and investors. And the way it does it is that it leverages data, understands client’s goals better, and then uses them uses a fair amount of AI machine learning capabilities to identify and create more personalized investment solutions for the end client. And that is something we saw that was a difference, you know, when you talk about goals based wealth management as something that’s happening across the industry, but we oftentimes had seen that gap that while people were talking about goals, but the portfolios were not necessarily talking towards those goals, the portfolios where oftentimes when you go under the hood, you’ll see that the underlying investment portfolios were more static, more standard. And we saw that as an opportunity. We’re leveraging some of these capabilities, we are able to create something, portfolios that work in lockstep with clients needs evolve as the client’s needs evolve, and goals evolve. And as well as when the markets evolve, and they try to work along with it. So that’s what our goals optimization engine is as a digital product.

Jack Sharry: Terrific. So Martin, if you would you work with lots of different types of firms, this is a bit unusual, maybe you could comment on this unusual to be working with an asset management firm. Of course, they’re affiliated with AdvisorEngine, which is pardon the expression but a b2b robo for lack of a better description. But when you talk about the role that you play with LifeYield, because you’re helping to build this, we’ll get into more, more in depth on the whole goal process and a little bit but it’d be at a high level, how you work with Deep and the team at Franklin Templeton, how you work with AdvisorEngine, maybe compared go to some of the other tools that people are familiar with the marketplaces they have a sense of what we’re talking about here.

Martin Cowley: Sure. So my role as head of product, I’m having to figure out what does our product roadmap look like in the future? What feature candidates should we consider and which order do we tackle them in? So that comes from so many different places? It comes from keeping an eye on what’s going on in the industry, what some of our competitors would be doing and especially what our clients are doing? And the challenge is always trying to figure out, how do I build a product roadmap that we can take forward? That’s a single track and is usable in lots of different situations by lots of clients. Integration As with a lot of different systems that are all, you know, all have their unique aspects. So it means you’re kind of in the center of things between the client experience, client systems integrations, what we want to do with LifeYield, as we grow our product, and then how do we help firms like Franklin Templeton expand to use some of the features that we already have, or that we’re going to add in the future. So I guess, going back to how we’ve worked together with Franklin, so far, there’s two aspects that we have to focus on, we’d like to get really deep into the weeds. And you know, I’m in deep in the weeds with, with Deep all the time, inner workings of go kind of fascinating, that distinct from what you might see in other planning systems, it’s very interesting that GOE is an engine. It’s not a monolithic planning system, where people have to do the swivel chair thing across lots of different systems in order to be able to use it designed to be able to plug in to other experiences, such as AdvisorEngine, I’d say that’s a very similar tactic that we’ve taken as, as a firm LifeYield. We’re API first, we do have some user interfaces, but we focus on integrations. And by far, the easiest way to get somebody to use something is to have them not realize that they’re using it, because it’s plugged in behind the scenes. And I think that’s very much true of LifeYield. And it’s true of go when it surfaced through a tool like AdvisorEngine,

Jack Sharry: And not nearly as and not in the same universe as these two in terms of the technical acumen. But if I’m gonna give it a shot, for the layperson, to understand, what you guys are working on, basically GOE is a very sophisticated planning tool. It’s an engine. So it’s also built to implement what a lot of people don’t realize about planning is a plan that is that isn’t implemented in a scientific way, shall we say? It’s just a plan. And so really, what, between Martin and Deep what they’re doing is Go is an award-winning plan. And why? Because a couple of years ago, or a year or so ago, you guys won, like the best new digital innovation, I think, is what you want something like that. But basically, what you’ve done is you’ve taken a planning tool made it more sophisticated, applied implementation capabilities. And we’ll get into it now in a moment Deep if you’d comment. And what we’re doing together with between LifeYield and Franklin Templeton and AdvisorEngine, is you’re really taking it further in terms of adding capabilities route, taxes, route income stream. So when you fill in the blanks there, you’re taking a very evolved sophisticated planning tool, with a lot more than just that. And now you’re going to be taking it further and looking at coming out with a new and improved version fairly soon. So why do you fill us in on all that Deep?

Deep Srivastav: Yeah. And Jack, I think, would ever talk further. Since you mentioned the word you know, that was? Yeah, we did get a couple of awards. And I think that there is an interesting story, when our first paper, we’ve gotten off six papers published in different academic journals, and our first paper was published, I remember that an email coming to me that, hey, we have won this Harry Markowitz award. So you know, I just dug into it and say, Okay, what does this mean? And then you realize that the jury is all the four Nobel laureates of finance. And I literally fell off my chair, Hey, that’s pretty amazing, you know, we are on to something here. And then but you know, a lot of these academic work needs to be a really converted into a proper digital product. So we will be glad when we got the MMI, industry disruptor Award, as well for a finished product. But at the heart of it was this whole quest towards personalization, right, you know, so end of the day, are we doing some things? Are we as an industry pushing standard products? Or are we creating something which is really personalized for those end clients. And that’s what we were on to as a journey with QBO. And then over time, you realize that, to do that, you should be able to model it in a more stochastic way, you know, which is all the sophistication in the backend, but to be able to do it about what happens when market scenarios change, and goals change, people’s lives change. So you’re modeling it out for all of those different years and saying, How would my portfolio perform through all of these different times? So that was at the heart of this personalization story, and what helped to bring the product together? I think what was also fun and interesting was we when we met with the LifeYield team, I think, right before the pandemic hit, and we said that, hey, you know, there is the same spirit of personalization that seems to be you know, there on the other side, as well. And I think the two teams philosophy really just really connected with each other. Right? And you could see that we have the same thought process. And we said, Yep, we can really create a product a unified capability that cuts across investments and cuts across taxes, you know, social security and so on and really bring something which touches all these elements of financial life in the right way. So that brought this other side of the journey together, which Martin and you know, our teams have been on and marked a few.

Jack Sharry: Maybe go a little bit deeper on the goals optimization engine or go as it’s called, how that gets implemented. You’re working with a variety of firms, many wealth management firms, a lot of insurance companies we work with A host have a partner with other fintechs, a whole host of firms, how is go different? What’s kind of where I want to get to is one of these a word I’ve been using the flight two or two different variations on the same thing. One is that the capabilities that deepen his team are playing together and playing out currently are about to play out at adviser engine is around coordination of lots of different elements that are sort of weaving through all the different capabilities of planning and proposal and implementation. And next best thing to do as you’re implementing on the path toward asset location and tax optimal income, and so on and on. So very much a whirlwind process and a work in progress, both but maybe at least a current state where you are evolving toward and maybe compare that some of the other firms not by name, certainly, but just sort of where the industry is going. Because there’s a lot of this happening, this whole notion of weaving through the capabilities to back to what Deep was saying earlier about that highly personalized kind of guidance on how to improve outcome?

Martin Cowley: Yeah, well, so I think if I look at the way we’ve integrated in other situations, we pretty much span everything from planning, initial proposal generation, even kind of hanging off the back of risk tolerance questionnaires, where there’s no formal planning process, we’ve had that one end of the spectrum, we integrate in that way, where we’re very much at the beginning of the conversation. And then at the other extreme, we’re working with multi account portfolios, that may have managed accounts, they may have UMAs SMAs, they could contain brokerage accounts, all kinds of different products in those portfolios. And in that use case, we’re actually driving the execution, we’re running smart withdrawals, we’re figuring out how to adjust asset location. So we have a big span of use cases, some of which are higher level, some which are really, really detailed. Now, when we look in the planning, I’d say the kind of common thing that we see on the planning side is plans are often not tremendously dynamic in the risk tolerance questionnaire, or that exercise might be used to arrive at a target asset allocation and level of risk. But that level of risk may be assumed to hold true for a long period of time, perhaps a client considers themselves to be more tolerant of risk in accumulation, and then they may see a wholesale switch to a less aggressive asset allocation, once they enter retirement. So often, you’re just seeing those two changes in level of risk. The thing that’s unique about go is the fact that it’s much more, it’s like flying a plane on the instrument landing system, it’s making adjustments all the way through that glide path is pretty unique, when we compare it to other situations that we’ve integrated, other systems with which we’ve integrated. So I think that’s something that’s been interesting, we’re actually already set up to be able to work in that way, we just haven’t seen that, as commonly as we have with the more rigid asset allocations that we’ll see in other planning scenarios. So that’s something that’s really good with integration between LifeYield and GOE, the fact that we’ve we designed it to be dynamic, and goes really using us in a in a much more dynamic way than we’ve seen in other situations.

Jack Sharry: So Deep, I’m gonna assume that you’ve won all these awards for a lot of what Martin’s describing in terms of it’s a more advanced version of it’s not just a plan, that static one and done and maybe you return to it, or maybe you don’t, more likely you don’t. But you have a dynamic quality, what to what you’re doing where you’re you were making adjustments to use Martin’s analogy or landing the plays, playing music, sophisticated algorithms and instruments. Talk about that a little bit further. Where are you now? Where does that go? And also, how does that apply to say advisor, enter, which is, I think where you’re starting least initially, but the play and then if you would talk about where you hope to go from there, so maybe talk about how this all comes together.

Deep Srivastav: You know, as Martin described, you know, the whole thought process was something which really connects with clients in different life stages and really changes and adapts to what’s going on, right, you know, in a dynamic fashion. Now, where do we go from here, and the initial build was to get it to market really complete the value chain and get it to market which we have done. And we continue to expand on that. But then where do you go from here? I think what’s interesting is that when you change the nature of the portfolio advice, then it has implications on the whole value chain of advice, you know, because up till now, traditional systems, I’ve always thought of the investments as a static entity within this whole value chain and kind of done all the planning elements around it. What this does is it starts to reverse things, you understand that for real good investment outcomes to align with those goals, the portfolios and the portfolio risk becomes more dynamic, right? But when that happens, it has repercussions across the board. Right? So on one side, our investment solutions team is really working on making these portfolios more sophisticated and bringing in some of those multiperiod optimizers. same constructs in a deeper way, you can bring in better AI capabilities. So we are already working on that on how do you take it from the current stage to the next stage in terms of much significantly enhanced user experience and computational capabilities that will bring in. But then the third and the exciting part of it is that what it means is that, based on that how people do the profiling of their goals, how do people understand and assess their goals and plan for them? The whole shift from a risk tolerance questionnaire to thinking more about a goals-based questions and you know, how do you extract that information? How do you nudge clients towards that? How do they bake in the Social Security and the tax thinking right up front, as they are doing all of it through all of these life stages, how the portfolio risk analytics changes, you know, when you think about our static portfolio, then the way you think about risk is very different when the portfolio itself is shifting, right. So this entire value chain of what we call is financial planning, right up to investment advice and tax locations and stuff, there’s a whole chain, and we believe all the elements of this will start to change. And many of these places we see, you know, LifeYield is a critical partner, as we do that as well.

Jack Sharry: So quite a few to expand on that. Because what’s often not understood with all this talk about comprehensive advice platforms, comprehensive wealth management platforms, and your favorite word ecosystems that are being built around improving outcome for investors and advisors. We talked about this a moment ago, I want to go a little deeper on this. And that is that you’ve got to weave it through with risk and tax are kind of bookends or flip side of the same coin, is any risk adjustment you make is going to have a tax consequence, you got to be smart about that adjustment, as people knows, and so what’s often forgotten is if I’m going to do that, and also risk tolerance changes, markets changed, we’ve just lived through as much change as you might imagine, in a lifetime. We’ve just done that in the past three years. But the point being is that you need to have a dynamic engine to help make decisions or at least take preferences, what have you and translate, not in a simple static asset allocation all good, that’s good thing to do. But then be able to manage that through various cycles of various situations, looking at risk looking at tax down the road, including Social Security, because that becomes an important ingredient and the overall retirement income, something we call retirement income sourcing it LifeYield. So maybe you could explain that dynamic. There’s a lot there. A lot of people are hearing those words, but they’re not fully appreciating, I think just yet just what what’s at play here, the stuff as you well know, and Deep well knows, this is complicated stuff. This is important stuff, too, in terms of improving outcome and maybe expand on what he was talking about a moment ago about this dynamic nature.

Martin Cowley: I’ll talk about a couple of different things. So on the one hand, one of the challenges that we have, as we integrate more closely with go, and with AdvisorEngine, is we have a pretty big menu of functionality that is used in a lot of different ways. And as I mentioned, sometimes it’s used in quite a dynamic way where our API’s are being called repeatedly, and people are always monitoring progress and tax efficiency, that kind of thing. Other times, it’s much more of a fragmented conversation where it may just be part of an annual review. And there aren’t as many changes and adjustments being made. So the good thing about the way that we’re set up, and really, I guess any API is set up this way, and that it’s very dynamic is designed to be dynamic. So the challenge, I think, that we have, and we’ve been spending quite a lot of time together working on this is out of the menu of functions that we have, which are the best ones to integrate into go in the first kind of version of our integration. And then where do we go from there? Which things do we add next? So some of those just Foreman pretty natural product roadmap between go, AdvisorEngine and LifeYield. It’s the prioritization, the functionality that we have that in goes case is going to be called in a more frequent, more dynamic way, than is the case with a lot of our other clients. But that doesn’t really change the nature of the functionality itself. So it’s kind of there waiting to be integrated, or they’re ready to be used in a very dynamic way. So that’s one aspect, just in terms of plugging the two systems together, were both well designed in order to be able to do that. The other thing I would say, and this has more to do with the weight go works in terms of its glide path on making allocation adjustments over time, we talked about lots of different aspects of tax efficiency LifeYield. Being able to manage a bunch of accounts together, gives you a lot of options, a lot of flexibility on the tax front, but two common ones are tax harvesting, tax loss harvesting, or gain minimization and asset location. And I think the way the way I always look at it is tax loss harvesting is it’s somewhat of a backwards looking function in that you have a set of assets that already exist in there and a gain or a loss. Some of them were bought recently, others weren’t. So there are functions that we provide where you can realize losses you can harvest those losses build up a loss carry forward, use a pre-existing loss carried forward to offset gains. But ultimately, those things have already happened, the gains or losses have already happened up to the current day. The other aspect of tax efficiency is asset location. And I think that has a lot of room for play within go. Because when you think about the dynamic nature of the go, asset allocation, asset location is all about mitigating future taxes, and trying to put the most tax efficient assets in the accounts where you’ll get the most benefit and reduce tax drag. One of the things that we also do with asset location is we mitigate the need for tax loss harvesting, you don’t have to rely on that tax loss harvesting quite so much. Because we’re not putting things in places where they’re going to incur as many gains or losses that are taxable events. So that works really nicely with the glide path that go has. Because with those more frequent adjustments on the risk side and the asset allocation, you might expect to have a really big tax event or a substantial tax event when those changes are made. But with some of the features that we’re adding into the mix, it really helps minimize those tax repercussions.

Jack Sharry: So and a reminder for our audience, we’re studying how to get the best outcomes out of the platforms that are under construction seemingly across the industry that’s even across the industry, is that taxes are the single biggest expense investors incur more than all the rest combined from some of the research we’ve seen. Bottom line is, it’s a big, big thing to consider. And really what I’m what I’m hearing here, and coming to understand, because this is a more sophisticated version of some of the other work we’ve done, that the Go planning tool is much more than a planning tool. And then the tax optimization that LifeYield provides, is much more evolved here than in some other instances. And so real advance for the industry. So for those that are paying close attention, if you want to build a platform, you got to consider taxes. Since fundamentally, if you want to prove outcome, you got to manage multiple accounts and all the ways that they’re managed. There’s a whole ‘nother factor on models, which we’ll get to another day. But the point is complicated, sophisticated stuff, but it’s all about improving outcome and quantifying the benefit, all of which will be available go. So Deep talk a little bit about as we look to move toward a conclusion of our conversation today. Where does this go from here? Where do you see this going? I know this is a beginning of a journey. That’s not the end. So tell us a little bit more about what you see, coming up down the road?

Deep Srivastav: Yeah, I think. And since you touched on the multiple accounts, I would want to start from there that you know, multiple accounts is oftentimes as investment managers be worried about the trap where we come up with the solution, which is not practical, right, you know, you can come up with a very good portfolio advice, without realizing that you know, what, its clients have got all these multiple accounts. And you don’t know how exactly to execute that advice, in the right way, with the right tax implications, and so on. So some of these get fairly complex. And oftentimes, what we try to avoid, rather is to take a very simplistic view and say, Yeah, we will solve it for just this part and not the other. So that’s where creating the triad ecosystem with some of these LifeYield capabilities being a critical part of it is a critical first step in that otherwise, that we may not be able to really get to a from a theoretical very practical standpoint. And that’s one thing that is really getting us excited with as a start point. From there, where we go in the future, there is a lot if you think about when we combine these investment and tax thought processes, just imagine the amount of user engagement changes and the kind of prompts and thought processes that you can really enable for users to be thinking about, you know, investors to be thinking about. So a lot of that happens, again, advisors tend to do it with whatever limited, you know, capabilities they have at hand today. But with this massive computing capabilities, you can really reach out the scenarios and armed advisors and financial advisors were a really good conversation, what people’s potential goals could be, how could be thinking about them? Where could they add a little bit more money? Where should they be taking it off, take some of the chips off the table, it becomes a much more engaging conversation. So how do we leverage AI and data to drive that next level of engagement, got conversation, I think is a very important piece. And then of course, rounding it out with things like annuities, you know, other elements of taxes, and, you know, bringing more kinds of products into the mix so that it’s a holistic advice, considering all kinds of products, financial products that an investor may use in a lifetime. There’s a lot that we will be kind of working on over the next coming years.

Jack Sharry: So Deep, thanks for that morning. So I’d like to, if you would, as we do, as we look to wind down the show, what are three key takeaways that we ought to be aware of in terms of go capabilities, the LifeYield capabilities, the sophisticated and advanced version that we’re hearing about? So tell us about the three key takeaways I think our audience might enjoy hearing.

Deep Srivastav: I’m saying the first one is the importance of personalization, Deep talks about that early on, and we’ve done been hearing that term come up so much in recent years. So there’s a few things more personal than your financial plan and your tax situation. So marrying those two is important. And that’s exactly what we’ve been spending a lot of time working on. So that personalization is the first one. The second is that tax strategies, easy to overlook, but they make a big difference, whether it’s tax loss, harvesting, asset location, withdrawal sequencing, some things that aren’t always part of a plan, or very high level in a plan can really kick that plan into the next level of sophistication. And then lastly, talking about the eventual execution of the plan, there’s a lot of value that we see in bridging the gap between planning and execution. And that’s where having some of those underlying algorithms that drive the execution side of things, whether it’s for withdrawals, or rebalancing, or whatever it might be, there’s a lot of value and consistency all the way through from planning until eventual execution. So a lot of the work that we’ve done, as LifeYield, in conjunction with go and AdvisorEngine has been focused on bringing that consistency to the process. It’s great.

Jack Sharry: It’s always fun to hear about the next iteration, the next best version of what’s going on. But one of the things to underscore for our audience is that we’re seeing this with across the industry planning is great and fundamental to what we’re describing, arguably, our friends at Franklin Templeton, an advisor, to say they’ve gone further than other planning tools, they’ll let them argue that for themselves, but that’s the argument and has validity. And the other thing that we’re seeing in the marketplace is that if you have a plan without implementation, all you have is a plan. So you’ve got to tie that to the implementation and taxes are the single biggest cost you have to consider. So exciting stuff that you guys are working on presently, this will be out really next years, but I’m hearing terms of the capability and much more to come following that. And I know you’re many versions down the road that are in the works as well. So thank you both for this. And at this point, I’m gonna ask you both if you would be kind enough to share by or respond to my favorite question, which is, after enjoying this wonderful conversation, I always like to hear what do you do outside of work that you’re particularly excited or passionate about, that people might find interesting or surprising, so Deep, you want to kick it off?

Deep Srivastav: Surely I don’t know how much they may find it more surprising than interesting. I guess. You know, I read a lot. But what’s weird about that reading is I used to read a lot of fiction and stuff till I started realizing that there’s a lot of facts and things that are can be very interesting. So I started like researching things like here is this something called quantum biology, you know, life is quantum biology, in your genetics, there’s a whole world of very interesting stories that are out there, which, when you get into these fields, you start knowing, including the people we need. So a lot of that kind of reading is what I do. I would say that I was also known as a big prankster made between my friends and colleagues. But I started to dial that part of my life down a little bit.

Jack Sharry: Yeah, don’t stop that. Please, please. Martin. How about you? Martin, by the way, has been on the show a few times. For our regular listeners. Martin has more interesting things. He’s like the most interesting man in the world. But Martin, tell us what’s the latest version of whatever you’re doing that the rest of us can keep up with?

Martin Cowley: Yeah, I have kind of exhausted my list of hobbies. Pretty much. I was thinking about trying to set up a prank with Deep on you. But we didn’t actually get time to do that. Maybe next time. The only other thing that I thought of is largely because a colleague asked me about this game that I used to play back in England as a kid and still do whenever I go back there. But there’s a former billiards called snooker, often over here, it’s pronounced snooker, which is incorrect. But it’s definitely about to correct you. Yep, it’s snooker. And it’s played on an enormous table. 12 feet by six feet as the regulation size snooker table and you don’t get very many of them over here, because they’re so big, and nobody knows how to play the game. That’s something that I spent, I wasted a lot of time, as do a lot of British people growing up playing snooker Deep’s probably familiar with it. I know there’s been some really good Indian snooker players in the past a lot of Australian snooker players something that kids grow up watching and snooker World Championships on TV.

Jack Sharry: And I was one of those when they’re not watching cricket and all those other games we don’t understand.

Martin Cowley: That’s right. That’s right. I didn’t want to bring up the cricket because yeah, so that you put your foot in that, yeah.

Jack Sharry: Did they play cricket? They did yeah, I forgot. Oh. Sorry. By the way, just to fill out Martin’s dance card Martin is a marathon runner, classical singer, classical guitar player, black belt in karate, and I’m probably missing five other things that he’s done. So we’re down to snooker or stuck or whatever you call it. Okay. Well, Deep and Martin, thanks so much. It’s been a great conversation. I really appreciate the work that you’re doing also sharing that work with us. So for our audience, if you enjoyed our podcasts, please rate review, subscribe and share what we’re doing here at WealthTech on Deck. We’re available wherever you get your podcasts. Thank you both and look forward to our next conversation. Thanks.

Deep Srivastav: Thank you.

Martin Cowley: Thank you.