Victoria Toli, one of Finny's three co-founders, describes her startup as “Mint for financial advisors,” drawing a parallel to the popular dating app. The comparison makes sense in several ways. Yes, Finny helps with the age-old goal of matching advisors with potential prospects, but it increases the speed and success rate with a ruthless efficiency powered by algorithms, data and personalization.
It is at the forefront of a movement I call the Great AdvisorTech Acceleration, meaning the rapid introduction of new tools for advisors enabled by the widespread adoption of artificial intelligence, the increasing availability of big data sets, and a a new generation of young engineers, innovators who have mastered the art of synthesizing these elements into practice.
Launched in February, Finny has already had an impact, securing revenue-generating business from real financial advisors. A participant in the prestigious startup incubator Y Combinator, Finny recently won the top prize in Morningstar's annual fintech competition.
Morningstar CEO Kunal Kapoor said he was impressed with what Toli and her co-founder, Eden Ovadia, have brought to the wealth management ecosystem. “Eden and Victoria are working on an innovative solution for advisors and I'm excited to see what the future holds for this impressive team,” he said when I asked about the startup.
The team raised an undisclosed pre-funding round in February, backed by Y Combinator, Crossbeam Venture Partners and Merchant Investment Management, and launched what startup types refer to as a “minimum viable product” in May. Five firms participated in a paid pilot program. The founders say that within a month, the waiting list grew to 30 firms and now exceeds 70.
The idea for the prospect discovery tool grew out of Ovadia's experience at the Boston Consulting Group, where a research project confirmed what consultants already knew: Conversion rates from cold outreach were dismal. The group found a conversion rate of less than 1% after an average of 56 hours spent collecting data from platforms like LinkedIn and ZoomInfo and subsequent messaging campaigns.
The Finny team believes they can automate the process by identifying and prioritizing prospects—from a universe of 270 million individuals in available datasets—within a targeted niche using thousands of data points for each trend, prioritizing opportunity. of conversion to an individual advisor.
The prioritization score (what the team calls the “F-Score”) is unique to each leader-advisor pair. In other words, a potential prospect for one advisor may not be possible at all for another, based on the advisors own data profile and ideal client personality. The platform even automates contact and appointment scheduling, significantly reducing the workload for advisors.
Toli, a Stanford engineering graduate who joined Finny after four years as an associate at Kleiner Perkins and two as a growing product manager at Uber, said the key to Finny's rapid development lies in his ability to use open source code to customize large language models. . This allows the small five-person team to achieve in weeks what would take larger teams months or even years to build.
The startup's CTO and third co-founder, Theodore Janson, developed Finny F-Score's matching engine and algorithms from scratch. He says the tool is similar to Netflix's predictive content model, which appears to match viewers based on their profiles. Janson, who studied electrical engineering and mathematics at McGill University and has a master's degree in artificial intelligence from the Ecole Polytechnique in Paris, says F-Score accuracy improves continuously as it feeds data into the large language model that drives the alignment process. .
“Ours is an agent that does the work for the advisor and is always in the background,” Toli said.
Prospect/Customer details in the Finny interface.
Janson said the lead discovery and generation process is a bit of a black box for the advisor, who will never be able to fully understand the underlying formula behind the proprietary matching algorithm.
The two advisors who use the platform provided by Finny that I spoke to don't particularly care how it works, only that it works. They love the fact that it puts warm leads on their calendars without them having to do anything beyond initially providing a detailed description of their firm and the parameters of their “ideal client.”
Richard Will, a wealth manager and partner at Jackson, Wyoming-based Catalytic Wealth Management (the wealth management arm of venture capital firm General Catalyst, backer of firms such as Stripe, Airbnb, HubSpot and Datalogix), said that being in a VC firm gives him a front-row seat to many overblown AI startup ambitions. However, with Finny, while it can be a challenge to scale the model, he loves what the app is doing for his firm.
For example, when he goes to a certain city and looks for prospects between the ages of 30 and 50 who are either founders or in the C-suite of a biotech firm and from there, identifies, say, an interest in lacrosse, “my goal with him is always to get a date or two.”
“It helps me find people who are in the sweet spot and if not the customer, then the country clubs where they're going to be – 15 to 20% of the people I hit are calling me back,” he said.
“I just connected with a major real estate developer based on an email I drafted from a Finny recommendation,” he said. “When I'm looking for a biotech founder or a crypto founder … I'll change the email a little bit, but it gets the ball rolling,” he said, “and frankly, I'm using Finny because I want to understand the technology. “
Firms pay $500 per month per advisor, plus a one-time success fee equal to 25% of the annual fee from the client sourced by Finny.
Will said he's a fan of Finny's, but questions how “success payments” will work with the larger world of advisors. Other lead generation tools have tried charging success rates over the years with mixed results. He said the Finny team is open to feedback.
“I'm on a call with Eden or Victoria once a week,” he said, with questions or requests about what he'd like added in the future.
Alex Goldstein, an advisor in the corporate executive services team at Chesapeake Asset Management with previous positions at UBS and Merrill Lynch, praised Finny's ability to generate high-quality results at a fraction of the cost of traditional tools like ZoomInfo.
“When I started the business, I was in the training program at Merrill, and it was just cold calling, offering them tax-managed strategies,” he said. Later, he found success using LinkedIn for research. At Chesapeake, he said he could reach out to anyone and, in turn, has grown adept at using CoPilot AI and Salesflow. He heard about Finny from a friend after the startup had been accepted into Y-Combinator.
Goldstein said that while the startup is still in its early days, it reminded him a lot of ZoomInfo, but without the cost.
“They use AI to better tailor your search. For example, instead of just targeting executives at Nvidia or Oracle or Snowflake, I target firms that are seeing growth or add political party or religion to the search criteria,” he said. He also likes Finny's automation.
“Appointments just appear on your calendar. It connects through Calendly with the exact people I want to target,” he said, adding that he is paying for the service himself, but also questioned the long-term sustainability of the success fee.
Among the many prospecting and lead generation tools I've written about, Finny reminds me most of Aidentified in terms of its methodology, the massive data sets it has access to, and its use of artificial intelligence.
While I call Finny a prime example of “Great AdvisorTech Acceleration” because it's a small team that rapidly develops technology and scales up its power in a short time, I started seeing this trend a year ago without really understanding it.
Beginnings like SIFA, now called AdvisoryAIAND dance are examples in the advisory communication space, while the small team in Portrait Analytics has built a new AI-based hedge fund analyst.
There are a few others I've met with and received demos from over the past couple of months that I haven't yet written about – these fall into other categories that advisors are likely to find useful.
It's unclear how they will all fit into the fabric of the current advisor technology ecosystem, but the landscape will look very different in the months, and especially years, ahead.