Building Client-Centric Portfolios With AI With Cameron Howe

Cameron Howe is the Co-founder and CEO of Investipal, a Toronto-based fintech platform designed to empower individual investors and financial advisors through AI-driven portfolio management and onboarding tools. Under his leadership, Investipal remains a bootstrapped company focused on financial literacy and democratizing access to professional-grade investment tools. With a quantitative and ETF research analyst background at a global investment bank, Cameron recognized the need for more personalized and accessible investment solutions. This insight led to the creation of Investipal, which began as a DIY investing tool and has since evolved into a comprehensive platform serving advisors and investors.

Here’s a glimpse of what you’ll learn:

• [2:03] Cameron Howe discusses how his background as a quant led to founding Investipal

• [4:51] Bridging the gap between revenue models and true client value in advising

• [6:37] Using personalization to tailor investment strategies to client goals

• [8:44] How Investipal processes 225-page statements with OCR and LLMs

• [11:15] Challenges of managing multiple AI models in fintech applications

• [14:52] Applying deep learning to optimize portfolio construction and risk reduction

• [21:10] Cameron explains why incomplete client data poses a challenge for financial advisors

• [24:37] Where Investipal fits in the advisor tech stack and its key integrations

• [32:52] The value of having mentors 
  

In this episode…

Financial advisors are currently facing the challenge of providing deeply personalized investment strategies while managing a large volume of manual tasks that can be time-consuming. As client expectations for customization increase, the complexity of portfolio construction and compliance processes also rises. How can financial advisors balance personalized service with scalable, efficient operations?

Cameron Howe, a quantitative research analyst turned fintech leader, shares insights into transforming the advisor-client relationship through AI and automation. He emphasizes why shifting from one-size-fits-all investment models to more tailored solutions based on individual client values and risk profiles is necessary. Outlining how AI tools can automate tasks like account statement analysis and portfolio optimization, Cameron demonstrates how it frees time for advisors to focus on relationship-building. He also highlights the importance of explainable AI to ensure compliance and build trust, suggesting deep learning as a powerful tool for managing complexity in modern portfolios.

In this episode of The Customer Wins, Richard Walker interviews Cameron Howe, Co-founder and CEO of Investipal, about using AI to modernize financial advising. Cameron discusses advisor inefficiencies, how automation enhances client onboarding, and the evolving advisor tech stack. He also delves into data extraction from investment documents, managing AI model complexity, and how deep learning improves portfolio design.

Resources Mentioned in this episode

• Quik! 

• Richard Walker on LinkedIn  

• Cameron Howe on LinkedIn  

• Investipal 

• "[AI Series] Transforming Meeting Management for Financial Advisors With Arnulf Hsu" on The Customer Wins 

• "Inside Boutique Wealth Management: Navigating Wealth and Ethics With Joshua D. Rogers" on The Customer Wins 

• "Revolutionizing Tax Advisory With David A. Perez" on The Customer Wins

• Geoff Moore on LinkedIn

Quotable Moments:

• “Our goal is to help streamline the way the advisor runs their business.”

• “The advisor now has to be a quasi CTO and wrangle how all my systems talk.”

• “We will sit essentially as a co-pilot alongside you like your pocket CFA.”

• “People have such a strong affinity to their money that they want a human in the loop.”

• “We make sure all that gets scrubbed before it ever even hits an LLM.”

  
  

Action Steps:

1. Automate manual advisor tasks with AI tools: Reducing time spent on repetitive administrative work allows advisors to focus more on clients.

2. Personalize investment portfolios based on client goals and values: Tailored strategies help clients feel understood and create a competitive advantage for advisors.

3. Use explainable AI for investment recommendations: Transparent outputs help meet compliance requirements and increase client trust in AI-generated advice.

4. Integrate your tech stack for seamless data flow: Smooth system communication reduces errors, prevents duplication, and enhances operational efficiency.

5. Prepare for advisor workforce shortages through scalable automation: Automation allows solo advisors to serve more clients without increasing staffing needs.

Sponsor for this episode...

This is brought to you by Quik!

At Quik!, we provide forms automation and management solutions for companies seeking to maximize their potential productivity.

Using our FormXtract API, you can submit your completed forms and get clean, context-rich data that is 99.9% accurate.

Our vision is to become the leading forms automation company by making paperwork the easiest part of every transaction.

Meanwhile, our mission is to help the top firms in the financial industry raise their bottom line by streamlining the customer experience with automated, convenient solutions.

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Episode Transcript:

Intro: 00:02

Welcome to The Customer Wins podcast, where business leaders discuss their secrets and techniques for helping their customers succeed and in turn, grow their business.

Richard Walker: 00:16

Hi, I'm Rich Walker, the host of The Customer Wins, where I talk to business leaders about how they help their customers win and how their focus on customer experience leads to growth. Some of my past guests have included Arnulf Hsu of GReminders, Joshua Rogers of Arete Wealth, and David Perez of Tax Maverick. Today, I'm speaking with Cameron Howe, CEO of Investipal, and today's episode is brought to you by Quik!, the leader in enterprise forms processing. When your business relies upon processing forms, don't waste your team's valuable time manually reviewing the forms. Instead, get Quik! using our Form Xtract API.

Simply submit your completed forms and get back clean, context-rich data that reduces manual reviews to only one out of a thousand submissions. Visit quickforms.com to get started. All right, before I introduce today's guest, I want to give a big thank you to Jeff Moore of Valmark Financial Group, who is also a guest on my show for introducing me to the team at Investipal. Jeff is a thought leader in wealth management who you should be following on LinkedIn. So go find him.

All right. Today's guest, Cameron Howe, is the CEO of Investipal, a front office automation platform that helps advisors win business faster while building personalized portfolios at scale. Prior to launching Investipal, Cameron was a quantitative research analyst at a global bank in fintech operator. Cam, welcome to The Customer Wins.

Cameron Howe: 01:45

Thanks so much for having me, Rich.

Richard Walker: 01:46

Yeah, I'm excited to talk to you today. So for those who haven't heard my podcast, I love to talk to business leaders about what they're doing to help their customers win, how they built and deliver a great customer experience, and the challenges to growing their own company. So to understand your business a little bit better, how does your company help people?

Cameron Howe: 02:03

Yeah, it's a very good question. Maybe to take a step back on how we kind of ended up here. When I was a quant, I ended up working with a lot of different advisors. You know, the quant side of the business. We're trying to generate alpha.

It was interesting learning from the advisor world where that's certainly a goal. But the primary goal is to make sure my clients achieve their goals. I left a very interesting thought process where instead of, you know, building portfolios to beat the market or to match the market became more centered around making sure my clients were able to achieve the outcomes that they desire. And it led us down this pathway on the portfolio construction side to move away from this one-size-fits-all approach, where I can be more tailored towards my client goals without introducing that level of discretionary management that eats a lot of my time up. Just so happened, one of the main outcomes that happened with that was around what that advisor spends all their time doing, which is relationship management.

And we found a lot of the folks we spoke with, our current clients, they were spending a lot of time on manual tasks. Probably the same as the quick world as well. I'm having to do a lot of manual data entry, you know, fill out those constant compliance forms. So our goal besides the portfolio management side, is to help streamline the way the advisor runs their business so they can focus more of their time and attention on what they often love, which is working with their clients. And we'll take care of the monotonous work on the back end.

Richard Walker: 03:48

So let me be clear. Are you still producing portfolios?

Cameron Howe: 03:52

Yep. Yeah. So we will help the advisor optimize allocations, build portfolios from scratch. We have some AI tools that will do that for them. But on the other end a client onboarding flow to do document extraction risk profiling proposals compliance doc.

So we're I'd consider ourselves like a full stack solution on the investment management side within that advisory tech world.

Richard Walker: 04:21

That's fascinating. I mean, because if you're an investing, you're investing, you don't think about the operational side of things as much. And how have your clients reacted to this? Is this like, does it make perfect sense to them? Like you would do both of these things?

Cameron Howe: 04:35

Well, I'll pose a question back. How does an advisor usually make their money.

Richard Walker: 04:42

Through the relationship with their customer? Right. They earn the trust of the customer. They bring over their money, they manage it. They get a fee or, you know, AUM fee or something.

Cameron Howe: 04:51

Yeah. So yeah, they're making all their money on, on the AUM side. But what we found, you know, a lot of the value they provide is not on the investments. It's on the financial plan, the estate plan, the tax plan. And then the investment piece was left to something oftentimes a bit more generic, where I'm deploying models for folks.

So there's a we found there's a bit of a mismatch between the revenue model and the value model that advisors are offering their clients. So our goal is to stitch that together to make a more client-centric centric on the way we deliver investments. And you could think of that along the lines of, you know, a firm like a robo advisor would be offering based on your risk profile. Rich, you know, you filled this out. You're a balanced investor.

I'm going to put you in my balanced model. But, you know, this is all about the customer wins. How does that end? Customer win. when they might have unique considerations like ESG was topical before.

Maybe it's like values-based, belief-based. Maybe they have specific interests around like themes or sectors. How can we take some of that client-level of data to help personalize that investment strategy that advisor is offering?

Richard Walker: 06:08

Cam I love this perspective. I was just talking to somebody earlier today about identifying value and delivering value. And that's really what you're doing. You're saying the value to the to the advisor and the advisory firm is that relationship and that entire process, not just, oh, your investment did XYZ percent growth or something like that. I love that you took this perspective.

So what is the challenge then that you're solving that is so hard on that front-end process?

Cameron Howe: 06:37

Yeah. So if you want to go down the discretionary pathway, the discretionary investment management pathway, you're either growing your headcount on your team. Like I now need to employ portfolio managers or CFAs. Alternatively, I could deploy those model-based portfolios that but that discretionary management comes with increased costs or increased time. So now that advisor has to sit one by one, review Richard's portfolio daily or weekly or monthly make trades.

I have to make sure it's in line with my firm's approved fund list. Let's say if I'm deploying mutual funds or insurance products. So with all of that, there's this extra layer of complexity that I now have to spend all of my time and attention on rather than do what I love to do, which is interact with my clients, make sure they're comfortable with their financial goals, I'm helping ensure they meet that. So there's just a natural like we only have so many hours in the day, how do I want to spend those hours? And oftentimes I think I forget who it might be, like Josh Brown, but they talk about the different evolution of wealth management.

It started with like the stockbroker and providing access to the market for people. Then the second inning was focused on the portfolio manager, where my goal is to now manage your wealth. Now it's more focused on let me manage the relationship and that includes all those other aspects. So I now have to focus in on the financial planning. If you're high net worth like that, estate plan, tax mitigation strategies.

So the advisors forced to do a lot more while also having fees eroded over time. With all of that said, how can we use AI to help automate and architect a more efficient practice for them to ensure they can scale efficiently without incurring a ton of cost or a ton of time?

Richard Walker: 08:36

So give me an example of how you're deploying AI and what it's doing, and maybe even the type of AI that you're leveraging.

Cameron Howe: 08:44

Yeah, it's a good question. There's a couple different spots. I'll talk on like the client acquisition side. I would know to a lot of like what we've built out is just off of customer feedback. Like you I won't speak for you, but I'm no Steve Jobs.

I don't have, like, a grand vision here where I'm going to force my way of thinking on people. It's just off of like where we found a lot of pain, a lot of friction. That first part, our first part was focused around when I'm trying to win a client. Part of that step is I have to review everything that they're invested in. They'll send me over an account statement and like, you know, one example, we received a 225-page statement from UBS.

Richard Walker: 09:32

Wow.

Cameron Howe: 09:33

That advisor would have to sit down in the middle of their day and key in all of those securities into whatever sort of system they were using to visualize, you know, Rich has like 30% exposure to equity and a little bit of fixed income, etc. on our end. They'll just upload that document. We use a mix of like an OCR layer where we will classify that document and break it up. And then we'll use an LLM to make sense of the information within there. And we do some fact checks along the way.

So that's like one like low-hanging fruit. We've been able to build that depending on the type of statement. It could take that advisor or someone on their team several days just to work through that one document. Probably similar to quick, we get it down to a couple minutes. The other area that I find a lot more fascinating is deep learning.

So what we've done, you know, there's different types of AI out there. LLMs not a blanket for AI in general, really good at like contextual stuff, writing reports, etc. not good at math.

Richard Walker: 10:39

Have so terrible at math.

Cameron Howe: 10:42

Yeah. And I think, like, actually I'm curious, like, where do you guys apply? Do you have you applied AI yet into the quick world?

Richard Walker: 10:48

Yeah, yeah, we're applying it into well there's multiple right. We have machine learning. Yeah. With OCR to extract data out of the forms. That's the form extract product.

We are deploying technology internal to build forms with us, which is partly built with LLMs and OCR and a couple of other things that are going on that I'll leave out, because it's our secret sauce, so to speak. But yeah, there's a lot of different tech to apply into that world.

Cameron Howe: 11:15

Yeah, I find it. It's tough to keep on top of even on like our end. You know, OpenAI comes out with like an agents framework, but we were building it on a different platform before. Now do we have to pivot? Is it good enough or like the different LLM models better for certain tasks than others.

And then you have to manage like multiple different deploys I can't just go entirely with like my own bespoke one, or like with an off the shelf open AI model I have to think on, like what is the use case and what model is best suited today, but that ends up changing like two weeks from now.

Richard Walker: 11:49

Well, yeah, you have to build an architecture that you can swap out models within the same framework, like OpenAI models, because they keep changing or different LMS altogether, because maybe, maybe sonnets better this than OpenAI for that purpose. And we have that in our architecture as well, that we have to be able to upgrade and swap it out in case there's something better.

Cameron Howe: 12:11

Yeah. Yeah, that's I think it's like, is it? I forget who's doing it. There's like a middleware provider. Now when you talk about like that.

Of course. Yeah. It's like managing multiple different API keys. Like we use cursor a ton for like our software development. And you can pick what model you want to choose from.

But that's good for software development. It's not good for like client-facing apps. So you then have to your point on the architecture side. Now managed like an intermediary system to dictate multiple different models. One might go down or like OpenAI's hit like a limit.

Do I have to switch to a backup now to ensure the business doesn't go down?

Richard Walker: 12:49

Yeah, right. That's its own crazy thing. And speed latency. Yeah. Anytime you build these dependencies on third parties, you have those types of challenges to mitigate.

And believe me, I've been yelled at by the CIO of AIG because we chose Amazon once when Amazon went down. Yeah, that's.

Cameron Howe: 13:08

Funny. We've been through that battle. I've heard some firms. Yeah. To your point, I don't I don't I don't know AIG, but.

They will only go with you if you're on a sole approved cloud provider that they've already done due diligence. But, you know by and large, like the three major ones Microsoft, Google, and Amazon are all top notch. Like is it on the firm now to be more likely to bend the knee and be able to work with vendors like ourselves that are on multi-cloud frameworks, or one that's not on my approved list. It is still a major provider. It's not like a Ma and PA.

It's an interesting world. Now I feel like tech's always been obviously at the cutting edge for for software, rather for a long time. But now with like the AI piece, there's just this whole education world. I feel like we have to spend a lot of time on just. Is my data safe?

Are you training your models off of my stuff that we now have to navigate?

Richard Walker: 14:12

Yeah. That's true. I mean, you're taking in that 225-page statement. You're collecting a lot of information. Where is it going?

Right. Yeah. And I'm sure it's just going to produce whatever insight or report you have. But you're not taking all that and training a model with it.

Cameron Howe: 14:26

Well, what we actually do, we now have a client database where like, I know your socials, and I open up bank accounts in your name. That's how we make all of our money. Oh, I'm kidding, I'm kidding.

Richard Walker: 14:37

No.

Cameron Howe: 14:38

We make sure all that gets scrubbed before it ever even hits an LLM. To your point. Otherwise, it's a non-starter for any firm.

Richard Walker: 14:44

Yeah, obviously. So you were saying your favorite part about AI, and you diverted to ask me a question? Did we hit your favorite part?

Cameron Howe: 14:52

Yeah. So my favorite part is on the deep learning side, which is where like, we apply it in the portfolio construction world, where everything's like it's a mathematical model at the end of the day. I don't have to worry about like vectorization and whatnot. With like NLP libraries, we focus a lot more on the portfolio design, the unique consideration. Not to be too nerdy with you, but there's a world called modern portfolio theory where essentially, you know, I can throw in a bunch of different holdings and, you know, all those holdings might be low volatility, but they're highly correlated with one another, which objective wise, I don't want to have happen like in a oftentimes utility stocks let's say are low vol but they're also sensitive to interest rates.

So in a hiking interest rate environment, while I might perceive my portfolio as being low vol, it's actually quite correlated with the bond market, and I end up taking on hidden risk I might not be aware of. So, you know, modern portfolio theory tries to get around that where it doesn't matter. The securities I put in there. Well, it does, but it will design a portfolio that minimizes the volatility in aggregate, where I could have a barbell approach with some high-risk assets, some low-risk. But the way they get, the way that they're correlated with one another offsets that sort of correlation with like a low volatility area.

So this is like a layman I'm trying to be as layman's as possible here. But how? Like a typical portfolio manager might build a portfolio. If they are more robust, then you start wanting to add in constraints, which comes back to like the customer and the client end. My client has all these considerations as well.

Do I want to factor in alternative investments? Do I need to take into consideration the goals of the client? Do I need to take into consideration any client interests they may have to make it more relatable to them, to make sure I'm providing that level of value and that level of service. So that problem becomes very complicated. And you can't just use like generic programming for.

And, you know, that's an area we apply that deep learning model. And the cool thing with the deep learning piece, you know, back in my quant era, a lot of the work we would do was black box, which the SEC does not like. If you are providing investment Estimate recommendations with AI. The advisor still has to, or the investment professional still has to spend a lot of time figuring out why this is in the client's best interests, and they're ultimately the ones on the hook. So the unique angle that we've taken with this is, how do we explain the outputs from the model?

And, you know, back to the LM conversation. You can use the outputs from the deep learning side to inform how everything was calculated. And explain that back to the advisor. So there's the SEO side element. And then there's also the client interest to make sure that advisor can convey that message effectively back to the client.

So that's an area I like to nerd out a lot in.

Richard Walker: 18:12

Yeah, I can tell that's cool. Deep learning. What does that mean?

Cameron Howe: 18:18

So like machine learning would be, you know, very much to the tune of I'm going to give you a training set. A model like a regression model would be machine learning. I can then apply a testing set to make sure I can test it out-of-sample. The deep learning side will end up adapting over time like there's multiple different deep learning models. But like the most common one would be like a neural net where I don't really know.

If you think of like a brain structure, there's little electrons firing off to different nodes within the brain. It will calculate based on its own volition, essentially what each of those nodes I should prioritize. Each of those nodes might have a specific objective behind it. One node might have one around risk, one around returns one around like the client values. And then through that as well, it can end up reinforcing itself so it can end up getting better over time.

So it's not just like an optimization or a regression model, which is somewhat fixed. And if new information comes in, I have to fit it to that existing framework. It will end up adapting based on, in this case, market conditions, let's say, where I might kick out some stocks I would hold historically to enter in new ones, or the client goals might be vastly different. And I have to, rather than have 20 different machine learning models off the shelf, can use one deep learning model that's adaptable. So it's the adaptability.

Richard Walker: 19:47

Okay. That's cool. I appreciate this explanation. Hopefully the audience does as well. Does that mean that deep learning is theorizing or simulating or doing multiple projections to see patterns and figure out optimal answers?

Cameron Howe: 20:03

Yes. Yeah. It might. You could. I mean, to break it down, maybe more into like the financial services world.

You could think of it kind of like a Monte Carlo simulation where it might be running itself, you know, 20,000 times. The Monte Carlo assumptions are always fixed versus the deep learning, it will end up adjusting itself dynamically.

Richard Walker: 20:25

Cool. I built a Monte Carlo simulator back in 2001, and it was just fascinating to me what it did and how the outcomes worked. Because I don't know if you knew I was a financial advisor in late 2000 till 2004. Almost. So here's another question.

And I don't know if I'm just out of touch with how it works, but when I was an advisor, it was actually hard to get all of the client statements from all the different places they held investments because their view was, you're my advisor, I have my accounts with you. I'm not necessarily giving you these other ones that I've had for whatever years. But with your product able to read statements, is that a problem? I mean, really the genesis of my question is, is it a problem in looking at a portfolio, if you don't have all the pieces and all the information?

Cameron Howe: 21:10

That's fair. That's a very good question. I mean, the client might not want to. They might only be giving you a piece of their business to they don't want to give you the rest of it.

Richard Walker: 21:18

Bit. Yeah.

Cameron Howe: 21:19

That can also just come down to like the onboarding questionnaire. You ask that individual where, let's say they give you their statements, that statements are for like $1 million out of like 5 million. They, they own holistically. It's this is where that conversation with that end client becomes very important for the advisor. They still have to know what they're managing and what they're not managing, where, you know, like I might have my own Robinhood account that I'm day trading in.

It's still important for me to consider, okay, you're in 100% risk assets. So maybe now when I come over to my recommendations for you, I'm going to put you, you know,, your risk level, which might be balanced. I might have to put you into like a conservative bucket now to ensure that I'm offsetting that excess risk you're taking on your day trading account. So, you know, obviously the advisor wants to get everything from the client. We find, though, that they will get a piece of that.

With that, they'll have a conversation or they'll have their note taker join the call, let's say, to collect more of that information in real time, to inform the way that they're recommending a portfolio, let's say, for a client that still has to consider the overall asset picture as well. Like if you are a real estate, did you actually encounter this at all where like they'll have like a massive amount of assets that are in like held away accounts I'm not going to be overseeing?

Richard Walker: 22:49

Oh, sure. Yeah.

Cameron Howe: 22:50

How'd you get around it?

Richard Walker: 22:53

We said, look, we can't give you a financial plan without the full picture. We don't have to manage your assets. Right. But then we did something else on the back end. When we were done showing them everything, we had a summary page that said, okay, I want to show you how many accounts you have by type and how many you need.

So you have four individual accounts, each spouse and you have six joint accounts. You really need one individual account each and one joint account each. So this is spread out across four different companies. You either love opening statements and calling 800 numbers, or nobody's asked you to consolidate this before. And honestly, that year we did that.

We doubled assets under management because everybody's like, oh yeah, duh, I don't really want to. This was from that old relationship or whatever. And so to them they just didn't think about it. And we expressed that to them. In other cases, they were like, no, I have two advisors for a reason, or I have this account over here for this reason.

And that was fine. I mean, no pressure, but I mean, it was a long time ago for me. So I just don't know what people are doing today. And I is making it easier and easier to digest this information. So why not?

Cameron Howe: 23:56

Yeah. I mean, like to your point there, you know, we're just a tool. It's still on the advisor to and that's where the value is provided is understand at a deeper level what that customer is going through, where their assets are, what their goals are, and then structure something more holistically. You could give us one statement. You could give us 20 statements.

We don't care at the end of the day. Right. It's on like the value you're providing the client and making sure that discovery process, you're getting the full information set from them.

Richard Walker: 24:26

Yeah. So okay, where do you fit into the advisors' tech stack. Because there's so many things they have to encounter. Right. And you're probably hearing about this all the time from them.

Cameron Howe: 24:37

Yeah. I mean not to beat a dead horse, but like look at the Kiss advisory tech now it's unreadable.

Richard Walker: 24:45

It's too big.

Cameron Howe: 24:47

We are technically on that map sitting in the onboarding bucket, but I don't think that does us full justice the way we think about it. To your point, you know, the advisor now has to be a quasi CTO and wrangle. How do all my systems talk to one another? Our goal is to own that investment management vertical. I will not do trading and trading, rebalancing, but anything from that as it relates to the investment side.

Client risk profiling, proposal generation, account opening into portfolio construction. And I'll call it compliance monitoring. So like monitoring the ongoing performance of those accounts and flagging alerts is that area. Which means on our end, the main integration points we have are with the CRM providers at the top end and the custodial platforms on the back end. And then in conjunction with that, you still have your financial planning software, your fee billing engines, your PMS platform.

But we own that. I'll call it the investment research investment management protocol.

Richard Walker: 25:56

Okay. What do you think your future is, cam? I mean, in terms of like how AI is changing things, how you see your product unfolding over time, what is your vision of. I mean, you said you're not Steve Jobs, but you have a vision, I know.

Cameron Howe: 26:11

Yeah, it's a good question. We're seeing, I'd say, a major industry headwind, which is there's not enough people coming into the RA world to fill the gap there. It's funny. Like, we started off as a direct-to-consumer product. I don't know if I mentioned that before.

Richard Walker: 26:34

I haven't heard that.

Cameron Howe: 26:36

So we launched originally as a consumer product to help people move away from this day-trading mentality and think more long-term with their investment philosophy. The learning we had from that is people have such a strong affinity to their money that they want a human in the loop. So I don't think that part goes away at all. What I do think is there's not going to be as many humans getting into this industry. And as such, you know, in the next 20 years, that advisor is essentially focusing a lot more on the relationship aspects.

The goal we have is equipping them to manage that relationship effectively, to sell effectively without being bogged down in all that minutia. So our long-term vision here is we will sit essentially as a co-pilot alongside you as like your pocket CFA, your pocket portfolio manager, where you can ask us information to perform tasks. We will go and do all that work for you without any sort of complaint. And then you can focus your time and your effort in the real world, not on the computer system world.

Richard Walker: 27:46

Okay, you didn't say it this succinctly, but kind of what I'm intuiting from this is the future means we don't necessarily have to have more advisors enter the space. We still need them, obviously, but there's going to be consolidation in the sense that advisors can have more clients and more assets and more accounts and more portfolios, because the automation level and the capability of these tools like yours. Is that what I'm hearing?

Cameron Howe: 28:09

Correct. Yeah. Like, you know what I'll say. We're seeing the trend of the independent RA surface. I'm now trying to run my own practice.

And to do that effectively, I historically would need several other people on my team to support me. But we see it as a democratization where you could now end up launching your firm. It's quite cheap to get off the ground these days to launch your own RA, but to maintain it is another thing. So how can we equip you to operate as a sole individual without requiring that level of cost to service those clients on the back end?

Richard Walker: 28:51

Man, this is awesome. I think you've just touched on how we can solve this problem then. I mean, I think we ignore how much efficiency is being built out these days, and it's incredible what's happening. And, you know, there's another aspect to this too, because like in my world of forms, it's all paper or paperwork, I should say. It's largely not paper anymore.

Everybody said, hey, we're going to get rid of paper, right? But with printers coming out, we printed more paper. We got rid of the green ledger books for accounting with QuickBooks or whatever. But there's more and more paper. So I feel like AI is going to create more opportunity to create more jobs and more ways of serving clients in unique ways.

And your role in it?

Cameron Howe: 29:33

Yeah, I actually, you know, to throw that same question back at you, I'm curious, like where you see it evolving towards because I know, you know, like with like newer entrants, there is like a digital first mentality. But on the counter end of that, the advisor still has to get everything signed off by clients. Like I'm curious like what you see in the future on the quick form side.

Richard Walker: 29:56

You know, for probably the better half of 12 to 15 years now, I have known how to get rid of forms. I know how to do that. Yeah, but that's not the problem. I can't get rid of the need for sharing data, and the form is the vehicle for collecting that data. So the form plays multiple roles, the vehicle to ask the questions for the data, to then give the data to the person who's going to put it in the system, or give it to the system that's going to digest it and ingest it.

But it's also serving as a legal document in many cases. Most cases, frankly, that signature is there for a reason. I used to kind of play this out with signatures, like with DocuSign, been known them forever. Like forms are signed more often than contracts, like we will ten x the volume of signatures are getting because forms are used all the time. So, where is it going?

We already have plans in place to deliver more web-based experiences, more responsive, interactive experiences where the forms involved, without looking at the form, who wants to look at the form? You just ask me the questions. Next time you go to your doctor, do you want to sit in there with a clipboard and handwrite your information over and over again. Or would you rather type it on the screen at home before you go there? How about this?

What if they could call you and say, hey, I'm the AI assistant for Doctor Joe's office? Could I interview you on your way in on the drive? And now you answer the questions as as you go, and they fill out the form for you, and your voice authorizes it. Your unique voice signature becomes your signature. I think there's so many opportunities to advance how we collect data, where forms is the underlying design mechanism to say what data I need.

And the ultimate design mechanism for the signature for a legal artifact, if you will.

Cameron Howe: 31:37

Have you ever played around with whisper?

Richard Walker: 31:40

A little bit.

Cameron Howe: 31:42

That stuff's fascinating. The AI trend now of like, the natural voice agents and using AI. I couldn't agree more with what you said.

Richard Walker: 31:50

Yeah, I think all of that is happening. I've got a friend who started a company for being a voice agent and is looking for a way to apply it in the right way. As another friend said, voice agents, you could rate them as a B plus employee. They're not your A plus. They're not perfect, but they're 24 over seven.

They never take vacation, they don't complain, and they perform at a B+ level solidly every single time. And I think that'll become an A-minus and then an A player eventually. Now these are great questions. I have to wrap up and ask you my last question. But before I get there, what is the best way for people to find and connect with you Cam?

Cameron Howe: 32:29

Yeah, I'm quite active on LinkedIn. Cameron Howe if you want to connect with me there, you could also check out our website and connect with someone on the team at investipal.co as well.

Richard Walker: 32:42

Awesome. All right. So here's one of my favorite questions. I love asking my guests who has had the biggest impact on your career and how you got to this point?

Cameron Howe: 32:52

Yeah that's a good question. I'm going to say A when I first got into the capital markets world, my boss said, Mukhtari, shout out to you, great human being. He hired me from Deloitte, where I had zero financial acumen, but a new data science, and I think his first test for me before I got hired was to build a pivot table. And Rich. I never have experienced a pivot table in my life, but he still hired me, and the first task he gave me was to memorize because I'm in Canada.

I asked me to memorize the TSX, 60 tickers and the corresponding names and then fast forward very quickly thereafter, he had me running calls with clients, building out new strategies, and then ultimately spending a lot of time and managing the relationships with the investment advisors, which ultimately led me to launch investible. So I owe a lot to him for being able to take a chance on me and building it out. I wouldn't be in this seat without him giving me that chance.

Richard Walker: 34:01

That is awesome. We all need somebody to look at us and say, I see where you could go and give us that chance. Yeah, man. All right. I have to give a big thank you to Cameron Howe, CEO of Investipal, for being on this episode of The Customer Wins.

Go check out Cam's website at Investipal, and don't forget to check out Quik! at quickforms.com, where we make processing forms easier. I hope you enjoyed this discussion. We'll click the like button, share this with someone, and subscribe to our channel for future episodes of The Customer Wins. Cam, thank you so much for joining me today.

Cameron Howe: 34:32

Rich, thank you.

Outro: 34:35

Thanks for listening to The Customer Wins podcast. We'll see you again next time, and be sure to click subscribe to get future episodes.