Breaking the Swivel Chair Cycle: Smarter Tech for Advisors with Dan Bjerke

Can you give us an overview of what's going on there and what your role is there?

[00:00:25] Dan Bjerke: Sure. And, glad to be a part of the podcast, Doug. Great to see you. And, hello to your entire audience. I'm excited to share with you. I joined InvestCloud earlier this year, heading up our digital wealth business. And, we're excited to go on a new strategy and a new journey to deliver great technology solutions, primarily data, AI, and brilliant user experiences for the advisor and investor ecosystem.

And so I joined InvestCloud because I believe in the mission of serving the wealth management community with greater technology. There's a lot of problems to solve there from a disjointed system in fragmented data that I know we'll talk about today. And I really believe in the assets that InvestCloud has and our path forward is very bright. And we've got a great privilege client base that we work with every day.

And there's great, massive opportunity to do a lot of neat things with AI technologies to help advisors become more productive and more successful and to serve their clients in a much more personalized way. And to really help those end clients be successful and have their financial dreams come to fruition.

[00:01:39] Doug Heikkinen: Great. Let's start with some challenges. What are the biggest challenges you see today for financial advisors dealing with fragmented technology stacks? And how does the fragmentation impact both operational efficiency and client experience?

[00:01:54] Dan Bjerke: That's a great question and it's one of the primary reasons why I joined InvestCloud.

I've got 35 years of experience in financial services technology across banking, and payments, and wealth technology. And one of the key areas of the wealth management industry that's really been challenging for advisors and clients is that fragmented data in disjointed system problems that they face.

The cause of that is the way that the technology has evolved in this industry. There are at least six different core systems that InvestCloud integrates and works with today. These are custody platforms, portfolio accounting solutions, performance reporting, financial planning, CRM, and billing, just to name a few.

And in addition to that, advisors and their and clients have a multitude of front office applications that they're working with to support new client onboarding, proposal generation, campaign management, lead generation, overall account and relationship management. And then the basics, like client communication, client reporting, and then just practice management of advisor's book of business, just to name a few.

Some of those are manual, some of those are with Microsoft Excel worksheets. Some of those are PDF documents that are put together by the back office, et cetera. So that whole ecosystem and environment for advisors is very complicated. It creates that swivel chair effect that we often hear about where advisors are going from one system to another system, keying redundant data multiple times, which creates data quality issues.

And so it's a very challenging environment. At InvestCloud, one of the benefits that we have is we work with 65,000 advisors across the globe. So we're working with them on a day-to-day basis and hearing from them on what they need to become more productive, what they need to become more efficient.

And as a result of that, what we're hearing is that advisors on average are supporting upwards of 150 clients. So that's a lot of clients and they're spending over 40% of their time on administrative tasks alone. And so those administrative tasks are within all those different systems and manual workarounds, so they don't have a lot of time to really provide advice, build trusted relationships, working on approaching and onboarding new clients to build their book of business. And so what's happening is within advisory firms, what we're seeing is we're seeing a lack of organic growth. We're seeing some inorganic growth through acquisitions and roll ups, but what we're seeing is a lack of organic growth because of that productivity issue driven by these disjointed systems and fragmented data.

So the end results are advisors and their firms are looking for better solutions to solve that problem, to unlock that productivity gain, and which will drive organic growth and better client relationships and more personalized client relationships.

[00:05:00] Doug Heikkinen: Let's talk more about the data. What are some of the most common data silos you are encountering among these advisory firms that you work with?

And how do these silos limit the advisor's ability to deliver holistic advice?

[00:05:14] Dan Bjerke: Yeah, and again, it's probably those five to seven core systems of record across custody portfolio accounting. CRM, Financial planning. Those are really the core ones. And again, what happens there, especially in North America, is there's this multi custody environment where advisory firms have more than one custody. Global, there's one custody, and that is more of a workflow experiential issue that they're trying to solve for in global markets. So because that data is not consolidated, because there's not a unified single view, again, advisors have that swivel chair effect going from one system to another. Again, it creates that data quality issue. It creates an issue with the end client experience because they're oftentimes asked to provide multiple information multiple times. We've also heard the change of address issue. That basic thing that's been around for decades is continuing to be an issue today because all these different systems of record require an address and we gotta change it.

If you move, you have to change it multiple times. I personally just went through that, relocating from California to Florida and I had to go through, many different wealth management firms that I deal with, I had to go through and change multiple times just within one firm alone. As an example of that though, we've got portfolio management use cases. Those use cases can become inefficient and delayed due to incomplete and stale client data. So client onboarding and servicing can also become an issue because of that repetitive request for the same information. In addition to that, what that's doing is that's creating this friction in the client relationship and the client interaction model that's creating this level of frustration that advisors have to navigate.

[00:07:07] Doug Heikkinen: How does InvestCloud's unified wealth management platform address the issue of technological silos and what makes your approach distinct from other solutions in the market?

[00:07:19] Dan Bjerke: So I would say the biggest area of distinction for us is that years of experience and specialization in the wealth management space.

So not only do we have specialized experience in wealth management where we know the data, we know the workflows, we know the language, we know the problems. What we've got is we've built over many many years is technology that enables that data to be normalized and integrated into a digital data warehouse that allows for product engines to be exposed, to automate workflows and bring all of that, fragmented data to a unified state.

So it's not only just portal overlays for an a client experience or for an advisor experience, it's actually leveraging that normalized data model in a way that drives engines like financial planning, prospecting, new client onboarding, account servicing tasks, portfolio management, including digital investment proposals, are just many examples of those product engines and those workflows that sit on top of that unified data, and provide them these brilliant user experiences, whether that's a native mobile app, whether that's a browser desktop experience, and how that actual user experience integrates with the workflow, integrates with the data, and actually helps those journeys from both a client and advisor perspective. That's really our secret sauce.

And because that's unique to InvestCloud, because of that years of investment, because of those privileged client relationships we have in the industry, because of that specialization, we can now superpower that technology solution with the emergence of artificial intelligence.

And there's many examples of really cool things that we're doing from an artificial intelligence perspective to supercharge those interactions and those workflows in ways that we've never imagined before.

[00:09:15] Doug Heikkinen: With the increasing complexity of client portfolios, now spanning public and private markets, how important is it for advisors to have a single source of truth for client data, and what are the risks that they don't?

[00:09:27] Dan Bjerke: That's a really interesting question. So as we look at the emergence of alternative investments in this industry, one, it's a great opportunity for InvestCloud.

We have a very large managed account platform that supports separately managed accounts as well as unified managed accounts. And we've launched the private market account solution into the market. What we're seeing is that we're seeing the emergence of, really alternative investments have been really focused on ultra high net worth segment of the marketplace.

And so they're allocating maybe 50% of their overall portfolio to alternative investments that are semi-liquid versus the liquid types of traditional portfolio models. What's been happening though is the emergence of the opportunity to scale that down market to not only high net worth and mass affluent, but to the retail space.

However, to do that, it's gotta scale, it's gotta be efficient, and it's gotta be able to manage all the way through financial planning, needs analysis, portfolio construction, rebalancing subscriptions, client communications and reporting, et cetera, in educating both advisors and end clients on what alternative investments mean and how that's actually constructed and how that's delivered from a product standpoint in the industry.

So the importance of actually molding that into the advisor experience, having both the normal accounts that they manage along with these alternative investments, and providing a wrapper around it through the normal workflows that they manage on a day-to-day basis, is super important. One of the things that I think will start to continue to evolve is we'll see the adoption continue to increase. But one of the things that has to be solved in order for that adoption to increase is the application of technology in very smart ways to make sure that those manual processes can be automated.

Because if they can't be automated, we're not going to be able to scale it across the industry. So that ultra high net worth segment that's getting the benefit of above average returns versus normal investment profiles that the rest of the market segments are only exposed to, is we're not going to be able to get that two x return.

We're not going to see that alpha, because we're not able to scale it down. So what we're going to have to see is we've got the productization of it. We now need to apply data and technology to scale it in a very efficient way so that we can bring that benefit to the rest of the market.

[00:11:58] Doug Heikkinen: What's the role that AI is going to play and the data aggregation play in breaking down these silos?

And how is InvestCloud leveraging these technologies to empower advisors?

[00:12:10] Dan Bjerke: So we're very bullish on the application of artificial intelligence into our solution set and into the market, and we're already seeing some real world examples of that. One of the things that I'll say, Doug, is that with AI, there can be a focus on the use cases and the application of AI, but the first thing that needs to be focused on is the quality of the data.

So the data has to be constructed in the right way. Integration of multiple systems like we just talked about, has to happen first. So there's like a foundational layer. There's the integration that has to happen. Then on top of it, you can apply different AI tools for different use cases. We have a complete strategy and a complete product suite focused on artificial intelligence as part of our digital wealth operating system.

That smart solution suite is really focused on a couple use cases. One is leveraging AI tools to help with the know your customer portion of new client onboarding. So we've got a great capability there that really snaps in well to new client onboarding processes that helps with that account screening to make sure that they're compliant and that whole process is seamless in terms of doing the account scanning.

The second area that we're looking at is the ability for advisors to plan for a client meeting. Hold that meeting and then go through with the follow-up actions that are happening between the advisor and the client. So the whole meeting area is being, innovated and disrupted with the use of AI technology.

So what we're seeing there is the advisor's ability to use AI tools to go through and create automatically an agenda for a client meeting, pull past meeting interactions that the advisor had with a client, and summarizing the results of that. Pulling data from different sources and summarizing what some of the recommendations are that an advisor has for the client and putting that together. And then when the advisor holds the meeting with the actual client, they can focus on providing the advice, facilitating the meeting, listening, and not taking copious notes because the AI tools can transcribe the meeting. Take the relevant information and actually pre-populate CRM systems with any action items or follow ups that the advisor needs to take action on.

And all of that is handled through an AI capability for a meeting assistant, for an advisor. So when advisors are spending maybe three, four hours preparing for a meeting, conducting a meeting, and doing all the follow ups. All of that can be then consolidated down to a much meaningful, much smaller amount of time.

And then that frees up the capacity for the advisor to do other things. So that meeting capability is another area. Then we're expanding into the leverage of what we like to call next best action. So a next best action is leveraging AI to scan relevant data, pulling that data together and recommending the next best action for the advisor to take.

So when an advisor goes into a client record, it can scan the data and say the next best action to take for this end client are these three things for these reasons. And they could be life events. It could be some transactions, like a material withdrawal of funds or a deposit of funds into an account.

It could be based upon changes within the portfolio. It could be changes in the market. So it's really taking the smarts of the AI tool based upon integration of all that data, and really providing that next best action for the advisor so that they now, you're taking like best practices from high performing advisors and now automating that within your ecosystem and getting your network of advisors to perform at a higher level.

Then the next thing that we're actually seeing is the advent of agents, so taking manual steps and activities that are done by advisors, and building that into agents, that the agents are actually walking them through the steps to complete an action. Whether that's a new client onboarding, whether that's account servicing, it could be as simple as actually composing an email and letting the agent pre-write the email based upon the tone, based upon the objective that they wanna achieve.

And we would do that in a message composer, and then you could do that always with the advisor in control, but pre-building all of that. And so again, that frees up the time. And then the last thing that we're seeing is this area of practice analytics. So the ability to create a data ecosystem of advisors across the broad network, getting the rights to anonymize that data and leverage that data so that advisors can look at their book of business in comparison to other advisors across the peer group, and then determine where they might have wallet share opportunities, where they might have fees that are under compression, where there might be specific activities or specific actions that they should be taking to improve the performance of their book of business.

And all of that can be done because of all of that data that can be brought together, exposed, and then used in a meaningful way to drive different behaviors and different actions to get different performance outcomes. Collectively as a whole, we think that the combination of all those types of AI use cases can drive maybe 10, 15, maybe even 20 hours of freed up time for advisors that then they can do what they were meant to do.

Providing advice, building trusting relationships, building their book of business and then driving organic growth for their firms.

[00:17:58] Doug Heikkinen: World has become an amazing place, hasn't it?

[00:18:01] Dan Bjerke: Crazy.

[00:18:02] Doug Heikkinen: Yeah. How do you balance the need for robust data security and regulatory compliance with the goals of seamless data integration across these platforms that you've been talking about?

[00:18:13] Dan Bjerke: It is so important. I think it's, again, like I said, where data is probably one of the most important things that we have to focus on. I think the second thing that we have to focus on is the security and regulatory compliance. It's, I was just talking, I having a conversation with somebody earlier today and he's in the AI space for HR management.

And so there you're talking about associate data, associate information, and there's certain regulatory needs that have to cover that. When you're in the wealth management space, you have a fiduciary duty to protect clients' data. And so one of the first things we have to do is make sure that security and regulatory compliance is foundational.

There are no trade-offs, and that's the foundation of good data hygiene and how you're managing it. So those principles are non-negotiable. At InvestCloud, we don't compromise protecting client data, period. That said, innovation does not have to come at the expense of security or protecting client data.

So what we're seeing is there's a rise of, as AI continues to emerge and companies such as ours are leveraging new AI technologies to solve new use cases, we're also seeing the rise of secure, privacy preserving technologies. So there's concepts like data anonymization. There's concepts such as data tokenization.

So the technology industry has found different tools in different ways to protect client sensitive data. So a good example of that would be taking a social security number on a specific client and anonymizing it and modifying it so it's non-identifiable. But you can still use that information to drive rules and to drive workflows and to drive recommendations and best practices without using the personally identified information of a social security number.

So that allows us to integrate systems, unlock data insights without exposing that sensitive client information. Also adding tools like synthetic data generation, which is basically a complex way of explaining, creating new data from existing data. So it's like training models to represent data in new ways.

And again, it's overcoming that sensitive, confidential privacy information that clients hold dear, and making sure that we can still leverage our use cases and drive innovation. But protecting that anonymization is super important. So overall, it's about delivering the benefits of collective intelligence, like benchmarking, next best actions, more proactive service, and hyper-personalization.

But in ways that are compliant, protected through security, and making sure that you've got the internal governance and processes to make sure that as you're building and launching new solutions, that all of those are fundamentally first principles and that we don't overcome that.

[00:21:14] Doug Heikkinen: Amazing. This next question may seem unfair because it's the looking ahead question, but what innovations do you see as most critical for transforming the advisor experience and truly unlocking more of the power of data and wealth management?

[00:21:29] Dan Bjerke: So I think in the wealth management space, a lot of it comes down to real-time access or streaming data. So I think those are game changers. So instead of waiting for the normal batch updates to happen or logging into these silo data systems that are out there, I think the next generation is advisors getting a continuous flow of client data, from portfolios to planning tools to custodians feeding into one coherent view in a real time way, a more proactive way.

And it's like giving advisors almost like a living and breathing dashboard that they can work from. There's like a different interaction model. We oftentimes talk about chat with your data. So chat with your data in a real time proactive manner where it's like when people are using ChatGPT, they're asking a question, they get an answer, they're then refining it, and then they get another answer, then they're refining it. And that interaction is creating and training the models to get smarter and to be better. And so it's that interaction model in a real time interface with the data, in chatting with that, where it does seem almost like the system is real. It's like a human you're interacting with, it's getting smarter.

Information is coming back, it's recommending things based upon prior experiences and prior behavior that you have. And so I think like that explainable AI, those copilots that you hear about, they're not just like chatbots that are more static. It's more dynamic. They're smarter, they're transparent, and what it's doing is it's helping the advisors make sense out of the data.

Part of the challenge with advisors right now and actually their clients, is that there's so much data. It's, they're having difficulty discerning what's relevant. They're having difficulty determining, what do I take action on? What should I do? All of this data is fine. Some of it isn't designed very well, and they have to go hunt and peck for it.

So it's It's changing that whole interaction model and it's leveraging the system to be smarter and enabling the advisors to really discern what's important to them. And the advisor is still in control. Because they can ask further questions, they can interact differently and get different answers to try to get to the point that they're getting to actually take the action that they need to, to drive that hyper-personalization to the investors that they are managing, and then ultimately to drive better outcomes. And then again, back to that security and data, doing that in a trusting way that is foundationally in place so that you're not losing trust from the end client based upon how you're interacting with this new technology.

[00:24:19] Doug Heikkinen: Alright, last one for you. For advisors and firms that are hesitant to overhaul their existing technology stack, give us some steps that you'd recommend to begin breaking down these silos and moving toward a more integrated data-driven approach. And is it kind of like, get busy or get left behind?

[00:24:38] Dan Bjerke: That's such a good question, Doug. I think as clients go on this journey, oftentimes I see that they're somewhat paralyzed. Because they're looking at their existing infrastructure, and it can be a sense of legacy, it can be a sense of fragmentation, it can be overwhelming. And what happens is sometimes they fall in the trap that they're trying to drive towards perfection.

And a lot of times they could fall on a trap where they think that they need to do a complete overhaul of everything. And in my experience, 35 years doing this, a complete overhaul is fairly costly, time consuming, there's a huge opportunity cost, there's risk involved in that, and technology is evolving pretty quick and pretty fast. And so, that time to do a complete overhaul is pretty daunting. Instead, what I've seen that's really successful is great firms that understand specific use cases with a specific business problem, with a great outcome that they can drive and really getting laser focused and saying, okay, here's what success looks like, here's what an excellent experience would be.

Here's now a very meaningful, feasible way to go attack it. Going after that and applying it in that specific use case, and gaining then some confidence and gaining some momentum, and now having a proof point with a return on investment. High quality, great user feedback, and then building off of that to the next use case.

Now, even though you're not doing a complete rip and replace, you still have to have a technology North Star. You still have to have a place that you wanna make decisions, a set of principles, a framework that you're making the decisions. So you don't wanna make these decisions in a vacuum, even from a call it a point solution implementation.

You want to look at that within the context of an overall strategy set of principles and objectives that you wanna align to, but then go after it piece by piece and build to that North Star. And that will one, you'll learn from it. Two, you'll mitigate the risk. Three, you'll get really great user feedback. As a CTO, it can be a very overwhelming job because you've got all of these systems and challenges that we've just talked about. Here then you can gain confidence with your business stakeholders and your end user community that you're giving them solutions to solve very important problems for them. And then that will give you the goodwill to go after the next one and the next one, and ultimately staying consistent to that strategy and to that roadmap.

At the end of the day, you'll get to that North Star where all of that will come together, and you'll ultimately be able to drive the strategic objectives, the velocity of change, and take advantage of this new technology in the right way over the course of the right period of time. And by doing that, Doug, what you'll also have is you'll start unifying that data together as well.

Because the unification of the first use case will likely get you probably 70% of the way there from a unification of the data. And then your next use cases will get you to the 85% and then to the 95%. So you can go a long way with a simple use case in getting a lot of that unified and then building off of that foundation that you've put in place.

[00:27:58] Doug Heikkinen: Dan, I'm so glad we got time to spend together. This has been very, very interesting, enlightening, and you guys are doing some great stuff. Thanks so much for joining us.

[00:28:08] Dan Bjerke: Thank you, Doug. I really appreciate your time today. Take care.

[00:28:11] Doug Heikkinen: To learn more about InvestCloud, please visit investcloud.com. We are on all social media platforms @Advisorpedia.

Please give us a follow. For our producer Tory Miller, and everyone at Advisorpedia, thank you so much for listening.