Today, we sit down with Kofi Ndaikate, a leading voice in the dynamic world of FinTech whose expertise ranges from blockchain to regulatory policy. We’ll explore the transformative impact of AI on financial prospecting, focusing on how new technologies are reshaping advisor workflows, the ethics of automated outreach, and the critical metrics for success in a large-scale deployment. The conversation will delve into how platforms are leveraging massive datasets to identify client intent and what this means for the future of building client relationships in wealth management.
Given that financial professionals often spend hours on manual prospecting, how does FINNY’s platform specifically change this daily workflow? Could you provide an example of how the F-Score engine and Intent Search turn a multi-hour task into a matter of minutes for an Osaic advisor?
It truly revolutionizes the advisor’s day. Traditionally, an advisor might spend their entire morning sifting through social media, local business journals, and other sources, trying to piece together clues about who might need their services. With a platform like this, that entire manual process is condensed. An Osaic advisor can log in and lean on the F-Score engine, which has already done the heavy lifting by analyzing countless data points to serve up a prioritized list of individuals who fit their ideal client profile. What used to be hours of tedious, often fruitless searching becomes a focused, five-minute review of highly qualified leads, freeing them up to spend more time actually advising clients.
Your platform’s Intent Search tool analyzes 1.8 billion daily signals from online activity. Can you walk me through a step-by-step example of how an advisor uses these real-time signals to identify a high-intent prospect, and what specific online behaviors might trigger a high-priority alert?
It’s about translating digital whispers into actionable intelligence. An advisor first defines their target client—say, a tech executive nearing retirement. The Intent Search tool then scans those 1.8 billion signals for relevant behaviors. A high-priority alert might be triggered if someone matching that profile suddenly starts researching terms like “early retirement strategies,” “capital gains on stock options,” or “estate planning for executives.” The platform aggregates these anonymous signals, flags the individual as a high-intent prospect, and presents them to the advisor. This isn’t just about demographics; it’s about capturing a moment of need, a crucial shift from cold outreach to a timely, relevant conversation.
With a rollout across Osaic’s network of 11,000 professionals, a significant scale-up is underway. What key metrics will you use to measure successful adoption and advisor growth, and how do you ensure the technology remains personalized and effective when deployed across such a large and diverse user base?
Success at this scale goes beyond simple adoption rates. We’ll be closely monitoring the average time advisors spend on prospecting before and after implementation, aiming for a dramatic reduction. Another key metric will be the conversion rate from prospect to client, as that directly measures the quality of the leads generated. To maintain effectiveness across such a large network, the key is deep personalization within the platform. The system is designed to learn from each advisor’s unique client base and success patterns, so the F-Score and intent signals become increasingly tailored to their specific niche, whether they serve physicians in one state or small business owners in another.
The platform automates outreach across channels like LinkedIn, email, and even direct mail. How do you balance this automation with the need for genuine, personalized communication in a trust-based industry, and can you share an anecdote of how this multi-channel approach successfully engaged a prospect?
That balance is the most critical element. Automation is a tool to initiate contact, not to replace genuine connection. The system is designed to handle the first touchpoint, which is often the hardest part, but it heavily relies on the advisor to bring the human element. For example, the platform might send an initial, personalized email based on an identified life event. If there’s no response, it could follow up with a LinkedIn connection request. I recall one instance where a prospect ignored an email but responded to a piece of direct mail that arrived a week later, which referenced a recent article they were quoted in—a detail the AI flagged. That physical touchpoint broke through the digital noise and led to a meeting, proving that a smart, multi-channel approach can feel both efficient and thoughtfully personal.
What is your forecast for the role of AI in financial advisor prospecting over the next five years?
Over the next five years, I foresee AI moving from a helpful tool to an indispensable partner in prospecting. The focus will shift from simply identifying potential leads to predicting future financial needs before the client is even aware of them. AI will be able to analyze economic trends, career trajectories, and life stages to proactively suggest when an advisor should reach out about college savings, business succession planning, or retirement. This will transform prospecting from a reactive search for clients with current problems to a proactive, advisory relationship that anticipates and solves future challenges, making financial guidance more timely, relevant, and deeply integrated into clients’ lives.
