Finmate AI And PreciseFP Integration Expedites Transfer Of Client Meeting Data To Financial Planning Software (And More Of The Latest In Financial #AdvisorTech – June 2025)

FinMate AI And PreciseFP Announce Integration To Expedite The Transfer Of Client Meeting Data To Financial Planning Software

When a financial advisor onboards a new client, they collect a trove of client information from various sources (meeting notes, documents, questionnaires, etc.). Once it's collected, that client data then needs to be entered into the advisor's financial planning software – as well as other systems like CRM and portfolio management software – to be able to start putting together the client's plan.

Traditionally, this data entry has been one of the most labor-intensive areas of the financial planning process. A single financial plan can involve dozens of individual inputs, from demographic information on the client and their family to details on their balance sheet assets and liabilities to income, expenses, and savings. Manually keying in all this information from notes, questionnaires, and documents can take hours, and while data aggregation and integrations with other systems can help reduce this labor somewhat, there are certain data points that simply don't live in other software – e.g., the client's spending and saving numbers, or their planned retirement age – that still require some amount of manual data entry.

The need for a solution to expedite the data gathering and entry process led to the launch of PreciseFP back in 2007, which introduced online data gathering forms that could be emailed to the client. The forms are customized to map to specific financial planning software and CRM tools, which means that once the client finishes and submits the form, their responses can be reviewed by the advisor and exported to the advisor's software of choice. Although it doesn't completely automate the data gathering process – since the advisor does still need to review the client's responses to at least gut check their accuracy before exporting – it can save a lot of time by eliminating the manual data entry step between the client filling out the advisor's questionnaire and the data being populated in the advisor's financial planning software.

The caveat, however, is that not all financial planning data comes from client questionnaires. Some of it instead comes from client meetings, where clients bring in most of the information that the advisor needs to create the plan (which the most recent Kitces Research on Advisor Productivity showed is the primary approach to data gathering for 27% of advisory teams). In those cases, however, there has never been a way to automate getting the information that comes out during the client meeting into financial planning software: In the era when all advisors took meeting notes manually, the information from those notes would need to be keyed into the software. And even with the rise of advisor-specific AI meeting note tools like Jump, Zocks, and FinMate AI which can automatically capture key client data from what's spoken during the meeting, those tools generally only export data to the advisor's CRM, not to their financial planning software – meaning that even to the extent that the AI notetaker can capture the client's net worth or cash flow information, the advisor still needs to input that information manually into their planning software.

All of which makes it notable that last month PreciseFP announced a new integration with FinMate AI, which allows client information collected via FinMate AI during financial planning conversations to pass through PreciseFP, where it can then ultimately be pushed into the advisor's financial planning software.

It's a clever way to solve the challenge of getting data collected via client conversations into financial planning tools, which requires not only translating all of the unstructured information from the conversation into a structured format (e.g., recognizing that a client's statement about their mortgage balance translates into a liability that goes on their balance sheet), but then also mapping that data onto specific input fields for each tool that the notetaker integrates with. By integrating with PreciseFP – which has already built integrations not only to the "Big Three" financial planning tools of eMoney, RightCapital, and MoneyGuide, but also to some of the more popular niche specialized planning tools like Income Lab, Holistiplan, Asset-Map, and FP Alpha – FinMate AI can simply leverage the integrations that PreciseFP has already built, rather than needing to build and maintain new point-to-point integrations with each solution itself.

Similar to PreciseFP's core functionality of gathering data via online forms, however, the point of the integration with FinMate AI appears to be not so much to automate data collection as to expedite it. There would still be a human touchpoint in the process prior to the data being sent into the advisor's financial planning software: According to FinMate AI's preview of the integration, data from the client conversation would first be pulled together into a structured summary format within FinMate AI's app that could be reviewed by the advisor before hitting a button to send it to PreciseFP. Once the data is in PreciseFP, there could be another layer of human review of the data before it's ultimately pushed into the planning software. But even though these review touchpoints add some manual work back into the process, the nature of that work amounts to a check of the accuracy of the AI notetaker's data, which is still likely less time-consuming than the advisor entering (and double-checking) all of the data themselves.

The question going forward is whether other AI meeting note tools will follow FinMate AI's path of integrating with PreciseFP to push financial planning data into advisors' planning tools, or if instead – to the extent that they continue to build their integration capabilities to the other types of software that advisors use – they go the route of building and maintaining integrations directly with the financial planning tools themselves. On the one hand, integrating with PreciseFP can allow AI notetakers to quickly push data out to the many different planning tools integrated with PreciseFP, though it does limit the feature's reach to advisors who already use PreciseFP for data gathering. On the other hand, building point-to-point integrations with different software tools doesn't require users to have a subscription to another software platform to make use of them, but it takes far longer to build a full roster of integration partners (and potentially takes more resources to maintain those integrations) than effectively outsourcing the task to a third party like PreciseFP.

Either way, integrating with PreciseFP makes sense as an initial foray into expanding the integration reach of AI notetakers like FinMate AI into financial planning tools. If it proves popular with the subset of advisors who already use PreciseFP, we could see more AI meeting note tools following suit, either by setting up their own PreciseFP integrations or by building direct integrations with financial planning software themselves. And ultimately, just as financial advisors and their clients tend to talk about more than just the information that's stored in the advisor's CRM, it makes sense for AI meeting note tools to start integrating with more types of software as well.

AdvicePay Acquires AdvisorBOB To Encompass Both Client Billing And Advisor Payout Solutions

Although there are numerous ways for financial advisors to be compensated by the firms they work for, almost all of them revolve around paying the advisor directly or indirectly based on the revenue that they generate or are responsible for. In some models the connection is more direct, such as the wirehouse-style "eat-what-you-kill" model where the advisor's compensation is almost entirely comprised of a percentage of the top-line commission or fee revenue that they bring in for the firm. But even when the advisor is paid a steady base salary, there is usually some type of bonus structure based on revenue or business growth metrics that results in some correlation between the amount of business brought in and/or retained by the advisor, and what they earn in take-home income.

In practice, using a revenue-based compensation model in an advisory firm requires having a way to calculate the payout owed to the advisor. In the wirehouse and IBD world where "eat-what-you-kill" has been part of the business model for decades, firms have set up internal systems to do these calculations, which was necessary to systematize payments across thousands of advisors per firm who were each owed varying percentages of revenue based on their position on the firm's pay grid (and often sourced from commissions coming in from multiple product vendors). But at smaller independent RIAs and hybrid firms that employ revenue-based compensation models, the process has traditionally been much more manual, typically involving custom-built Excel spreadsheets maintained by back office staff which are at best tedious to update for each billing cycle and at worst prone to errors that result in the wrong compensation being remitted to the advisor.

AdvisorBOB was created as a solution for mid- to large-size RIAs and hybrids which didn't have the internal technology to automatically handle payouts to their advisors, but which needed something more reliable than the old spreadsheet-based system (especially as their advisor headcounts grew, necessitating a payouts system that could better scale with them). Which has become increasingly valuable in an environment where advisory fees are increasingly earned not just via commissions or a percentage of AUM, but also through subscription or retainer fees, upfront planning fees, hourly fees – or sometimes a combination of several of those options. Meaning that the payout calculation needs to incorporate all of those different revenue sources, and to pull in data from perhaps multiple different systems that handle client billing for commissions, AUM, or any number of flat fee models, making it all the more desirable to have a system that handles the whole process automatically.

Which is why it's notable that this month AdvisorBOB was acquired by AdvicePay, a platform that handles client billing and payments for advisors with flat-fee models.

What makes the acquisition significant is that it represents a first step for AdvicePay towards expanding beyond billing and processing client payments, and into covering more of the full "lifecycle" of advisory firm revenue from when it's billed to and paid by the client until it's split between the advisor and the RIA and paid out to each. With AdvisorBOB, AdvicePay now has solutions to cover both sides of the process, which gives them the opportunity to cross-sell AdvicePay to AdvisorBOB clients who use a fee-for-service model, and likewise to offer AdvisorBOB's solution to AdvicePay users that still use spreadsheets to calculate their advisors' payouts. In other words, owning both the front half (i.e., client fee billing) and back half (i.e., advisor payouts) of the revenue cycle, and their associated transactions, allows AdvicePay to earn revenue on both ends, facilitating a fully end-to-end revenue collection and remittance workflow, while using much of the same back-end data on advisor-level revenue that they already have in place.

But beyond the ability to cross-sell to existing customers, the real significance of the acquisition for AdvicePay is in the opportunity to reach a much larger slice of the RIA market than is possible today. Currently, AdvicePay only handles billing for flat-fee models like subscriptions and one-time engagements, while Kitces Research shows that the overwhelming majority of RIAs' client fees are still billed on an AUM basis. And while AdvicePay could have built or acquired an AUM billing solution, the reality is that most advisors already have AUM billing embedded either into their portfolio management software (e.g., Orion, Tamarac, or Black Diamond) or their broker-dealer or RIA platform, meaning there is likely not much demand for most RIAs to pay separately for a standalone AUM billing solution. By acquiring AdvisorBOB, which handles advisor payouts under both flat-fee and AUM structures, AdvicePay can potentially tap into a much larger market of RIAs beyond only those that offer flat-fee billing. That way, if and when AdvicePay does decide to build or acquire its own AUM billing solution, it would have additional tools (both in its existing ability to handle billing and compliance oversight for flat fees, and in its newly acquired ability to handle advisor payouts for all fee models) that help it stand apart from the solutions bundled into portfolio management platforms.

Ultimately, though, AdvicePay's acquisition of AdvisorBOB is a reflection of how flat-fee billing models, which were once most often regarded as an alternative to AUM fees for firms that served a non-traditional client base, are increasingly being incorporated by mainstream RIAs to use alongside and even in conjunction with AUM. If the pivot by AdvicePay towards serving a wider range of RIAs across their entire operational workflow of collecting client fees and paying them out to their advisors signifies that flat-fee alone might not be the future of the industry, it also shows how there's an opportunity in helping firms integrate a range of fee models – from AUM to subscriptions to one-time planning fees to all of the above – and to oversee how they're collected and ultimately distributed to the RIA and its advisors. If such a multi-fee model does start to gain traction, then AdvicePay will be well-positioned to serve the firms who use it on either the billing side, the payout side, or both.

Betterment Acquires Rowboat Advisors To Move Upmarket Building More Tax-Savvy Portfolios For Higher-Complexity Clients

When robo advisors like Betterment and Wealthfront originally launched in the early 2010s, they did so on the premise that most investors would be well served by a relatively simple portfolio comprised of a handful of low-fee index funds, aligned to their questionnaire-determined risk tolerance, and then regularly rebalanced to maintain the portfolio's risk profile against the investor's own needs and preferences. Later, the platforms made that same pitch to advisors, selling a generation of B2B robo advisor platforms to RIAs with visions of efficiently serving hundreds of mass-affluent clients with robo-managed portfolios.

In reality, however, most B2B robo advisor platforms failed, because they didn't recognize (until it was too late) that the challenge of working with mass-affluent clients was never really a question about how to serve them efficiently, but was instead all about how to acquire them at a low enough cost per client that they could be profitable (especially at the relatively low fees that the robo advisors charged).

But regardless of the flaws in the robo advisors' business model, their approach to portfolio management itself remains sound. Of the relatively few robo advisors that remain, most still use a version of the broad-based index fund model (which, it should be said, still works for the majority of client types), both on the retail and advisory side. Meaning that, for example, Betterment – which survived the mid-2010s crash of the B2B robo advisor market both by holding onto an early lead in market share and by adding an RIA custodian that could charge $150/month per advisor plus an up to 20bps wrap fee in exchange for its custody service and automated rebalancing technology – still uses a portfolio management approach that, at its core, is nearly the same as it was 15 years ago (albeit with the more recent addition of features like tax-loss harvesting and asset location with which it has sought to add value around the margins without altering the core strategy).

But while Betterment's relatively straightforward approach has traditionally worked well for most investors, it has also had a number of limitations that have kept it from being well suited for all of an RIA's clients, particularly as advisors have increased their emphasis on tax-aware investing in recent years (in part as a means to demonstrate their own unique value-add beyond "just" what a robo-advisor could offer their clients directly). For instance, Betterment in general doesn't support single-stock positions, which on the one hand aligns with the overall philosophy of investing in broadly diversified funds, but as a matter of practical reality can create issues when a client comes in with a large single-stock position that would result in a massive capital gains tax bill if it were sold all at once. And unlike other robo advisors like Wealthfront that have introduced direct indexing capabilities to amplify the tax-loss harvesting potential of their portfolios, Betterment has stuck with the traditional blocky ETF model approach. Which, again, was fine relative to Betterment's core offering to retail clients… but as Betterment has also tried to grow its advisor platform, all of these factors run the risk of putting Betterment behind other tech-forward custodians like Altruist, which offers not only automated portfolio model management capabilities like Betterment's, but also more customized strategies for concentrated holdings as well as direct indexing, all without charging a separate wrap fee for clients on its custody platform.

Against this backdrop, it's notable that Betterment announced this month it has acquired Rowboat Advisors, a maker of tax optimization and direct indexing tools for enterprise technology platforms, to absorb into its Betterment Advisor Solutions platform. Somewhat Ironically, Rowboat was founded by the lead engineer of the direct indexing technology originally offered by Wealthfront, Betterment's main retail competitor going back to the early years of robo advisors, although unlike Wealthfront (which has embraced direct indexing for its retail client base) it appears for now at least that Betterment will use Rowboat's technology only on the advisor side of its platform.

For Betterment, the acquisition of Rowboat is just one of several recent announcements that signal how the platform's approach is shifting after more than a decade of consistency. In April, Betterment announced that it would begin supporting "self-directed" (i.e., non-automated) investing, including the ability to hold single-stock positions in a portfolio, as well as offering Securities-Backed Lines of Credit (SBLOCs). Now, with Rowboat's technology and team, Betterment aims to add direct indexing starting in 2026 and eventually also Unified Managed Accounts (UMAs) for tax-aware management across all of a household's investment assets.

What all these announcements have in common is that they have almost no relation to the "one simple set of model portfolios, available across a range of risk levels, fits all" ethos that has guided most of Betterment's platform decisions up until recently. When it was primarily focused on mass-affluent or often younger and still-starting-out-investing retail clients, this made sense – direct indexing, SBLOCs, and UMAs generally have limited value for that clientele. Instead, Betterment's newer solutions appear to be more targeted towards advisory firms' higher-net-worth clients – that is, who have the most tax sensitivity to portfolio income, who often have the need to manage around concentrated and highly appreciated stock positions, who are most likely to be comfortable and adept with borrowing against their securities' value for liquidity, and who may have alternative assets like real estate, private equity, and limited partnerships with their own tax characteristics to incorporate into the holistic management of the portfolio.

On one level, it makes sense for Betterment to start adding more flexibility to its technology offerings. As a custodian that charges on basis points rather than generating revenue indirectly via spreads on cash sweeps, securities lending, or payment for order flow, Betterment relies on having a stable and growing level of assets on its platform – but its fairly strict adherence to its ETF model approach up until now made it a poor fit for clients who needed more customization, which in turn meant advisors "only" put their smaller clients on Betterment (limiting the platform's growth), and/or could never fully utilize Betterment because it didn't fit a significant swath of a typical advisor's clientele. The new tax-focused features being promised in the wake of the Rowboat acquisition would allow Betterment to better support working with advisors who have more clients in the HNW space, leading to more revenue for Betterment if that leads to a higher base of assets to bill from.

The question, however, is whether – after so many years of sticking with a mass-market-friendly, "good-enough-for-almost-everyone" investment philosophy – Betterment can effectively rebrand itself as a solution for advisors' HNW clients. For better or worse, the years of pitching advisors on the ability to efficiently serve smaller client accounts on Betterment left it with the reputation as a "down-market" custodian. But while that may not be the brand that Betterment wants to have today, it also has the advantage of being a really effective brand. Advisors know which types of clients Betterment can serve well, and they know that Betterment is a strong custodial offering for serving those clients. Is it really worth it to jettison that powerful association in order to move into the HNW client space – especially when there are other technology platforms like Addepar and Black Diamond that have already built their own effective brands around supporting advisors serving HNW clients across any custodial platform (and without a basis-points wrap fee)?

In a way, Betterment's pivot is reminiscent of Riskalyze's decision in 2023 to rebrand as Nitrogen and add a host of new "growth" features to go beyond the popular risk tolerance tool that had long been its core offering. At the time, Riskalyze viewed its strong association with risk tolerance as a negative, and its Nitrogen rebrand sought to open new doors by enabling it to move further upmarket, work with bigger firms, and accordingly charge higher fees by embedding itself in RIAs' business development processes in multiple new ways. But in hindsight, what it instead did was to wipe away Riskalyze's well-established brand and open itself up to competition on new fronts, leading to several years of challenged growth and declining user satisfaction (according to Kitces Research's latest AdvisorTech data). To the point where two years later, it's an open question as to whether ditching the established Riskalyze brand – even though it felt that the label was holding it back – had left the company worse off than if it had stuck with what it did well (and what advisors knew it did well). The episode sounds a note of caution for Betterment about the risks of diluting an established brand in the name of upmarket growth, especially when there are already multiple established competitors swimming in the waters that Betterment is seeking to wade into.

Ironically, the same lesson learned by the early robo advisors applies to Betterment again today, and may ultimately determine whether or not its venture upmarket will succeed: It isn't enough just to have the technology to serve clients efficiently, whether those are mass-market clients (as in the original robo advisors) or HNW clients (under the new Rowboat offerings). Rather, it's about efficiently marketing to those clients (or to their advisors) so they're actually convinced to bring their assets onto the platform. After years of robo advisor industry turmoil, Betterment earned a hard-won reputation as one of, if not the go-to platform for clients, large or small, for whom a fairly simple automated ETF-based investment strategy really was the answer. Now Betterment will need to learn whether it can repeat the act for clients, and the advisors who serve them, for whom "good-enough-for-most" isn't good enough.

Zoe Financial Raises $29.6M As It Grows Into A Platform To Both Generate Leads And Serve The (Smaller) Clients It Refers

One of the fundamental challenges of providing lead generation for financial advisors is not just finding new prospective clients to refer, but monetizing them in a way that's worthwhile for both the lead generation platform and the advisors who use it. For example, when a lead generation service charges advisors a flat for every prospect referred, it's naturally incentivized to refer as many prospects as possible – but if the advisors on the platform subsequently end up being inundated with leads that aren't a good fit but that they're still paying for, they won't want to use that lead generation platform anymore. Conversely, if the platform charges advisors only for leads who end up converting into clients, then it has an incentive to make sure it only sends leads to advisors who are likely to convert – but then the issue becomes what to do with all the leads who aren't a good fit for advisors on the platform, e.g., because they don't meet any of the advisors' asset minimums.

When Zoe Financial launched in 2018, it was unique among advisor lead generation services in that it charged for leads based on a percentage of the revenue generated for the advisor – which meant that Zoe was only paid for leads that actually converted to clients. This set it apart from other lead generation services like SmartAsset which charge per lead sent to the advisor, and the result was a solution that advisors were generally much happier with – in the 2023 Kitces Research Study on Advisor Technology, Zoe received an average satisfaction rating of 8.1 out of 10, while its closest competitor in SmartAsset received only a 5.0 rating, which likely reflected the large discrepancy in the quality of leads from the two services. As it turned out, advisors were fairly happy to pay a substantial amount for lead generation (between 15% and 35% of advisory fees for the entire length of the client relationship, in Zoe's case), assuming the service actually provided leads who were likely to become paying clients – because the lifetime value of each client is so high, it can still be profitable even after paying Zoe its share of the fee.

The challenge for Zoe, however, is that lead generation tends to move upstream: The firms that are most likely to use and benefit from lead generation in the long run are the biggest firms with the resources to hire dedicated business development staff to sift through leads and screen out those who would be a poor fit. And in general, the bigger the firm, the higher the net worth of the clients they tend to work with (and subsequently the higher the asset minimums to work with the firm). Meaning that as Zoe gained traction and started working with bigger RIAs, it subsequently became more difficult to provide a steady stream of high-quality leads for those firms, since the qualification standards for leads went up as the firms Zoe worked with got bigger.

In 2023, Zoe addressed this challenge by launching the Zoe Wealth Platform, a robo-TAMP platform for advisors in the Zoe network. The idea behind Zoe Wealth Platform was to allow RIAs to efficiently serve clients referred by Zoe who wouldn't have otherwise met the RIA's minimum asset or fee levels. Which was only possible because Zoe already had those incoming leads to begin with – whereas many similar B2B robo advisors have failed over the years because they struggled to effectively bring in clients for the technology to serve, Zoe already had the lead flow through its lead generation service, and needed to build the technology to serve them so it was actually possible to monetize the leads who didn't meet the asset minimums of the RIAs on its platform.

And now, two years after launching Zoe Wealth Platform, Zoe has announced that it has completed a $29.6 million Series B funding round, which at a minimum helps to validate Zoe's strategy of combining a lead generation platform to send leads to advisors and a TAMP to serve the overflow of leads who wouldn't otherwise be a good fit. Notably, a number of the investors in this round are some of Zoe's own clients, including Creative Planning, Mariner Wealth Advisors, and Captrust – which is a positive indicator for where Zoe's biggest users believe the platform is headed in that they're willing to invest in its further growth.

To that end, it's likely that the bulk of the capital raised in this round will be spent on marketing efforts to further expand Zoe's presence among the general public and bring new leads into its find-an-advisor platform. Now that Zoe has seemingly solved the problems of both delivering leads to advisors at a price they're willing to pay (by charging only for leads that become clients) and monetizing the leads whom advisors wouldn't have wanted to serve directly (by building an investment platform to for advisors serve them while splitting the revenue with Zoe), the next step is essentially just to pour more and more leads into the funnel for Zoe to deliver and monetize.

From an industry perspective, the growth of Zoe over the last few years highlights how advisors are really willing to pay a substantial percentage of their revenue for a solution that can help them solve their lead generation problems (which itself is reflective of the high cost of acquiring clients and its key role in the profitability of advisory firms). It also shows that, in spite of the struggles of B2B robo advisors to profitably help advisors serve small-dollar clients, advisors really are willing to use such platforms when they can serve as part of a reliable lead-generation machine – reinforcing the idea that it's not just about having the technology to serve smaller clients, but the means of marketing to them efficiently so that they actually come and sign up. A fact which is attested to by Zoe's Form ADV, which reports Zoe as a subadvisor to 79 advisory firms and a whopping 21,628 clients.

But at a higher level, Zoe's story simply shows that there are multiple ways to monetize the service of lead generation, as long as the leads themselves are there. The more leads Zoe can attract and deliver to advisors on its platform, the more ways that open up for it to participate in the RIAs' full revenue stack, from receiving a referral fee for clients that the RIA serves directly to a more even revenue split for clients whom Zoe serves on its TAMP. But at the end of the day, it's much easier to solve the problem of already having leads and needing to find or build a way to monetize them than it is the other way around.

CFP Board Introduces AI Practice Exam App (To Compete Against Third Party Exam Prep Providers?)

When a person gets the CFP certification, they earn the certification itself from CFP Board – the organization which sets and enforces the requirements for someone to call themselves a CFP certificants. But while CFP board sets the standards for the credential (e.g., defining the educational, experience, and ethics requirements), much of the fulfillment of those standards is done through third party vendors. For example, educational institutions ranging from the American College of Financial Services to public, private, and community colleges and universities across the country provide the educational coursework required for individuals to sit for the CFP exam. And while the CFP exam itself is administered by CFP Board, any exam preparation and review classes are done through outside providers like Kaplan, Dalton, and Brett Danko.

The practice of outsourcing the CFP education and exam preparation programs to third parties has the advantage of creating a wide variety of options for individuals to choose a program that suits their preferences. For example, someone who highly prefers synchronous (i.e., real-time) learning and lives near a local college or university might choose an in-person educational program, while someone who prefers the self-study route and has a busy work schedule might prefer a remote program where they can go at their own pace. Similarly, those who want to get through all of their exam prep in a short amount of time might pick a live program like Brett Danko's that compresses the entire exam review into a four-day period, while those who want a leaner program of study guides and a large bank of practice questions might pick something like Kaplan's Essential Review program.

At the same time, however, the reliance on third-party programs means that there is no one "official" option for studying and preparing for the CFP exam. Each program develops its own curriculum and study materials, and while CFP Board sets the standards for educational curriculum and provides guidance on what candidates will expect to be tested on in the exam, CFP Board doesn't provide the materials or exam questions itself (other than a pair of practice exams that CFP Board offers for each exam cycle).

…At least that was the case, until this month when CFP Board announced the release of its new CFP Exam Practice App. The app is effectively a bank of practice questions presented in short quiz format (as compared to CFP Board's practice exam, which is a full-length exam meant to simulate the experience of sitting for the CFP exam itself). The app is designed to provide instant feedback on practice question responses, and to pinpoint areas where the user doesn't perform as well in order to drill those areas with similar questions. Furthermore, the app's questions are generated from AI using training data from CFP Board's practice exam questions (which themselves are primarily drawn from previous CFP exams), which makes for a theoretically unlimited supply of questions for candidates to drill themselves on any exam topic – though from a practical perspective, it appears that the AI-generated questions are reviewed by human CFP certificants before they're rolled out to the app, so it would still be possible to run out of questions before CFP Board's reviewers can approve more.

But what really differentiates the CFP Exam Practice App from other CFP exam prep materials is that it has the imprimatur of CFP Board. For the first time, CFP Board is offering a bank of practice questions to compete directly with programs like Kaplan, Dalton, and Zahn – except unlike those programs, CFP Board can (and does) promote that its practice materials are developed by the same organization that actually writes and administers the CFP exam, which gives them a sheen of extra legitimacy compared to third party vendors that can only use their best guess in creating practice questions that come as close as possible to the real thing. Which doesn't make the third-party exam prep materials subpar by any means, but it's challenging to demonstrate the value of your practice materials when your competition is literally the organization that writes the exam itself. Furthermore, CFP Board has a leg up in marketing its Practice Exam App by virtue of the fact that it has the list of candidates signed up for the CFP exam, meaning CFP Board can target those candidates to advertise the Practice Exam App as soon as they sign up for the exam (and perhaps before they've even begun to research other third party exam prep solutions).

It's certainly worth noting, however, that CFP Board's new Exam Practice App doesn't compete with third party exam prep programs in every way – if a candidate still wants to go through a live review session, they'll still need to purchase a live program through an outside vendor. And CFP Board's intention may not even be to directly compete with third party review programs, but simply to fill a demand for more practice questions that has so far gone unmet. The ultimate goal is likely to increase CFP exam preparedness across the board, and in particular for those who rely on self-study because they don't want to (or can't afford) a more comprehensive review program.

Which makes sense from CFP Board's perspective, as its whole reason for existing is to mint new CFP certificants, and raising the level of preparedness for candidates who are likely to come into the exam the least prepared can arguably make the most difference in bringing new candidates into the profession. Still, however, it's eyebrow-raising that CFP Board would take the leap into producing its own exam prep materials – but to the extent that the new Exam Practice App can help candidates improve their preparation for less than the cost of a live review program, it could be a positive for helping create new CFP certificants who may have otherwise fallen by the wayside.

Vanguard Tests AI-Generated Client-Friendly Market Commentary Summaries

One of the best use cases so far of generative AI technology is its ability to take an existing piece of text and modify it in some way: E.g., to take a piece of writing and make it clearer and grammatically correct, to tweak the tone to make it more or less formal, or to smooth out technical language to make it more understandable to nonexperts. Which can be particularly helpful for financial advisors in communicating with their clients, as AI tools can help the advisor convert their messy meeting notes into a neatly bullet-pointed client email, or serve as a first editing pass to help the advisor overcome the "expert's curse" of overwhelming the client with technical financial terminology.

The challenge, however, is that most generative AI tools today are set up as a chatbot, where the user enters a question or prompt and the AI tool outputs text in response. Meaning that in order to get the "right" text output, the user needs to know what kind of prompt to enter that will generate that response. For example, if an advisor simply asks the chatbot to summarize a client meeting into an email for the client, the tool doesn't know whether to structure the summary as bullet points or paragraph text, to use a formal or informal tone, to limit it to 500 words or 3,000 words, etc. – it will simply fill in the blanks for any unspecified output based on whatever its predictive text algorithm generates in the absence of more specific instructions.

With the relative newness of generative AI tools, most financial advisors are not experts at crafting prompts to get the exact output they need from an open-ended chatbot interface. And while some advisors have made deep dives into understanding how AI tools work in order to make the most out of their capabilities, most are more likely than not to be turned off by the need to type in specific requests each time they need to generate a piece of text. However, few advisor-facing tools have fully grasped the fact yet that advisors tend to have little interest in being prompt engineers, with the exception of some AI meeting note tools like Jump and Zocks that can automate email follow-ups from meeting transcripts in a "push button, get text" way rather than requiring a fresh prompt for every interaction.

Which is why it's nice to see that Vanguard is starting to test out a feature on its website that can generate client-friendly summaries of Vanguard's quarterly market commentary. At first glance, it's a very simple tool, giving three dropdown menus for the advisor to select their clients' financial acumen (low or high), life stage (early career, pre-retirement, or already retired), and the desired tone of the summary (formal or conversational). But the simplicity is really the tool's key feature: Rather than needing to paste the article text into a ChatGPT-like interface and ask it a tailored prompt to get the right level of depth, focus, and formality, the summary is generated with three dropdown menus and the click of a button.

To be sure, not all advisors will love Vanguard's market commentary summaries in their current form. The summaries consist of four to five bullet points each, even for the "high-acumen" selections, which is barely longer than the disclaimers attached to the end of the summary, which might not be as in-depth as some advisors want to go (and suggests that Vanguard might not trust their AI yet to generate anything longer than that without worrying about inaccurate details or hallucinations of facts).

Still, at its core, Vanguard's new tool represents what could be a significant and desirable shift in the landscape of AI tools for financial advisors away from tools that require open-ended prompts which create friction and cognitive overhead for the advisor that offsets the time savings of the AI tool itself, and towards tools that give the advisor a few options to choose from and generate what they need at the click of a button. Which might sacrifice some of the flexibility afforded by an open-ended chatbot (for which there's always tools like ChatGPT to meet that demand), but can still be customizable enough to meet the needs of the vast majority of advisors and their clients. One can imagine generative AI summaries for not only things like emails and market commentary, but also other areas like financial planning data ("click here to generate insights on the client's balance sheet"), portfolio management ("click to summarize the client's portfolio performance over the last year"), and CRM ("Create a list of the client's next 5 action items"), all using a push-button interface.

Ultimately, we could see more of these changes as AI technology vendors further iterate on their products and listen to the advisors who use them. There's a good reason why AI meeting notes tools have been adopted so quickly over the last two years, and that's because they rarely require the advisor to enter a prompt to instruct them what to do – instead, the meeting summaries, client emails, and follow-up tasks are automatically created and ready for the advisor to review, edit, and send. And as much as a chatbot allows the user to ask anything that their imagination allows them to, the reality is that most advisors only need their tools to do a handful of jobs – so to the extent that those tools can make it as easy as possible to get advisors' jobs done, advisors will realize more of the actual time savings potential of their AI tools, and be more satisfied with them in the end. 

Disclosure: Michael Kitces is a co-founder and partner in AdvicePay

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In the meantime, we've rolled out a beta version of our new AdvisorTech Directory, along with making updates to the latest version of our Financial AdvisorTech Solutions Map (produced in collaboration with Craig Iskowitz of Ezra Group)!

So what do you think? Will FinMate AI's integration with PreciseFP lead to meaningful time savings in entering financial planning data collected from client meetings? Will Betterment's new tax optimization features entice more advisors to bring over their HNW clients, or is Betterment's reputation as a platform for less complex clients too strong? Are Vanguard's AI-generated market commentary summaries an improvement over an open-ended chatbot like ChatGPT? Let us know your thoughts by sharing in the comments below!