LPL turns to AI to crack financial advisor pay questions

Of all the tangled financial skeins advisors have to unravel, some of the most complicated can involve their own pay.

Now LPL Financial is hoping to use artificial intelligence to provide brokers nearly instantly with the sort of insights in their own compensation that they might otherwise have to spend hours trying to secure on their own. Speaking this week before 6,000 advisors at their Focus 2025 conference in San Diego, LPL executives announced that the firm has spent roughly $50 million to provide “AI-powered forecasting, multicustody tracking and deep analytics” for advisor pay.

LPL Chief Product Officer Gary Carrai said in a statement that advisors often have “to navigate a maze of systems and spreadsheets just to understand how they’re paid.”

“Our goal is simple: bring clarity and intelligence to every payout,” Carrai added. “That means transforming raw data into actionable insights, using AI to forecast earnings and flag missed opportunities, and delivering a customizable dashboard that adapts to each advisor’s business. With deep analytics and comprehensive investment tracking, we’re not just showing advisors what they earned — we’re helping them understand why, and how to earn more.”

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The many uses of AI in wealth management

Like people in many industries, wealth managers have been scurrying to find ever-new ways to put artificial intelligence, machine learning and similar innovations to use ever since the tech firm Open AI astounded the world in late 2022 with its release of the Chat GPT AI-powered chatbot. So far, firms have mainly used large language models like Chat GPT — which can almost instantly produce reams of coherent text in response to simple prompts — for tasks like writing emails or providing accurate transcriptions and summaries of their discussions with clients and colleagues.

Many have also turned to conducting market and economic research, or to scour clients’ social media sites and other public postings to learn of life events that may require a bit of financial advice. One of AI’s chief strengths is in identifying data patterns that might take human beings hours and hours to find, if not elude them entirely.

LPL is a pioneer in using the technology to shed light on one of the most vexing things advisors have to do — figure out exactly why their compensation is what it is and what they can do to increase it. Industry experts and recruiters were quick to paint it as a possible way to meet a crying need in the industry.

‘The whole thing can certainly be 40 pages’

Compensation consultant Andrew Tasnady, the founder and managing partner of Tasnady & Associates, said it’s not uncommon for firms’ pay policies to run to 30 or 40 pages. Undergirding them all is usually a fairly simple compensation grid laying out how much money advisors get to keep from the revenue they produce for the firm; the grids tend to be tiered to allow higher producers to retain a larger percentage of the income they generate.

The complication comes in with the many incentives firms use to encourage behaviors like arranging loans to clients or selling insurance products while discouraging others. Tasnady said some firms’ plans have “gotten so big that they now have a summary of: here’s the basics.”

“And then they’ll say, but you have to see this other plan for the gory details on each of these individual more esoteric elements,” he said. “So the whole thing can certainly be 40 pages.”

Complexity a result of advisor incentives

Most advisors prefer straightforward payout plans in part because they make it easy to doublecheck and ensure they aren’t being shorted owed compensation. But firms’ almost uniform desire to nudge advisors into selling certain products or opening certain types of accounts often makes simplicity elusive.

As a result, Tasnaday said, some wealth managers have opened quasi call centers charged with answering advisors’ questions about pay. Many also have managers provide periodic explanations of why advisors are getting paid what they are and what they could perhaps do to make a little more.

“You know, ‘Let’s have a quarterly or once-a-year meeting on what we can do to help you be more effective — less administration, more sales time, more products,” Tasnady said.

LPL’s AI service seems designed so that advisors can now gain a similar understanding on their own.

“This sounds like they’re going to try to make it more efficient by just giving better insight reports to the advisor so that they can self-evaluate their performance,” Tasnady said.

Compensation at IBDs v. wirehouses

To be sure, the compensation policies of independent broker-dealers like LPL Financial are often far less complicated than those of large wirehouses and other firms where advisors are usually direct employees rather than independent contractors. That’s partly because the independence that comes with being at an IBD like LPL usually entails a degree of control over payout policies. In other words, advisors have greater leeway to make their compensation as simple or as complicated as they want.

Phil Waxelbaum, the founder of the recruiting firm Masada Consulting, said many of the back-office support services that independent broker-dealers provide to advisors are made available almost like items on an à la carte menu. And it can be daunting to try to sort out just how adding on or paring back certain offerings will affect the bottom line.

“But if you programmed an AI with these values — of all of the menu items and their pricing, you could go into AI and say: Here’s my practice. Here’s what I do. What do I need to operate?” Waxelbaum said. “What do I need to operate my practice? And what will it cost? And then the AI will come back and say, ‘OK, now you will achieve a net payout of 85%.” 

For all their financial expertise, Waxelbaum said, many advisors would struggle to understand how their own businesses make money. Hence the need for a system like the one LPL is offering its advisors.

“They’re just trying to create the ability to performance-test your business and say: If I do this, I’ll make that. If I do that, I’ll make this,” Waxelbaum said. “If I use this service, I’ll make this. If I don’t use this service, I’ll make that.”

Advisors with the same revenue numbers but different takehome pay

Another industry recruiter, Jeff Feldman, the founder of Financial Recruitment Partners, said wealth managers can sometimes struggle to understand why advisors generating equal amounts of money for their firms — having, say, $1 million in annual revenue production — might have differing amounts of take-home pay. The answer is that the advisors with higher compensation usually have their clients in products or accounts with higher profit margins.

“So when firms put together these comp plans, they’re not just looking at the top-line numbers,” Feldman said. “They’re looking at the profitability of those numbers, and that’s why they’ve got to play around with how much compensation they’re going to give to the advisor based on profitability. It’s no longer just top-line revenue numbers. It comes down to the profitability of the book and the practice.”

Tasnady said AI could prove useful to advisors who are trying to increase their compensation by giving advisors a peek into what their colleagues have sold to their clients and what share of their revenue they’re obtaining from certain products.

“It might say: ‘You’re only getting a half a percent of your revenue from life insurance sales and the average for LPL is 7%,'” Tasnady said. “So if you sold more insurance, you might be able to get an additional 6.5%. I could see where it might provide that type of feedback in terms of averages compared to the rest of the advisors, and might identify opportunities where you’re underrepresented in certain products.