The great wealth transfer is completely reshaping what affluent clients expect from their wealth managers and private bankers. As digitally native millennials and Gen Z investors step into their inheritance, they want personalised, real-time insights delivered at the speed of their smartphone apps. Yet the high cost to serve means investment professionals often struggle to provide the personalised communications these clients expect, especially those with lower asset levels.

Mention the term artificial intelligence (AI) and people immediately think of ChatGPT-style tools that can write poetry or answer random questions. But these are the same retail AI tools that provide inconsistent responses based on shaky informational foundations. When you are managing someone’s life savings, “close enough” is not good enough. Client trust, fiduciary responsibility and error-free data are at the foundation of everything private bankers do.

So, how do you deliver the hyper-personalised service that today’s wealthy clients demand without sacrificing the accuracy and compliance required in wealth management? The answer lies in smartly deploying different types of AI on top of a trustworthy foundation of data and knowledge. When done right, this strategy enables private banks to meet sky-high client expectations, while simultaneously enhancing these high-touch, human relationships.

Generative creativity

If AI is like a digital workforce, think of it as having two different types of employees. The first type, Probabilistic AI (aka Generative AI), is like your most creative writer. With a pile of market data, it can craft compelling stories that help clients understand what’s happening with their investments. It’s fantastic at taking complex information and making it digestible. However, sometimes the creativity can go too far, leading it to generate inaccurate information, often referred to as “hallucinations”. This can pose a significant risk when dealing with individuals’ financial futures.

The second type, deterministic AI, is like your most reliable actuary — one who can consistently solve incredibly complex problems, even in light of variable inputs. Ask it to compute risk scores or analyse portfolio allocations, and it will deliver dependable, verifiable and compliance-friendly outcomes based on predefined rules and curated data inputs.

The magic happens when you combine both types of AI strategically.

Let deterministic AI handle the number-crunching for tasks such as calculating risk scores and summarising financial market data, news and events. Then let probabilistic AI take those precise calculations and transform them into personalised insights that resonate with clients based on their service relationship and communication preferences. One client might prefer a detailed analytical report, while another wants a conversational summary they can understand over coffee.

The key difference between retail and financial AI tools is that in finance, everything runs on vetted data sources instead of the potentially unreliable information found on the internet. This allows banks to trust the results from their AI solutions, as they control what goes into it. It’s like having a dedicated research team that works exclusively with verified, institutional-grade information, ensuring that insights not only meet client needs but also adhere to strict compliance requirements.

Today’s younger, affluent clients have grown up with Netflix recommendations and Amazon’s personalised shopping experience — they expect their wealth manager to know them just as well as these services do. The difference is that Netflix merely might suggest a bad movie, while a wealth manager’s recommendations directly impact their financial goals.

This is where AI applications get really interesting. Imagine generating personalised investment podcasts that explain market movements in language each client can easily understand or delivering real-time risk scores that provide immediate context when markets become volatile. Some clients want detailed portfolio narratives that read like stories about their financial journey, while others prefer quick updates they can scan during their commute.

AI can also scan market conditions, regulatory changes and individual client situations to flag opportunities for proactive, rather than reactive, outreach.

The competitive imperative

Personalisation isn’t optional anymore. The clients who will drive wealth management growth over the next two decades simply won’t accept generic services. They’ll find someone who can deliver what they want, and once they leave, they’re probably not coming back.

The good news? Wealth managers and private banks don’t have to choose between staying compliant and staying competitive. The most successful firms are proving that sophisticated AI strategies can improve operational efficiency and client satisfaction while upholding the regulatory standards that define professional wealth management. They’re investing in a combination of AI approaches, building the experience, and refining the processes that will be incredibly difficult for competitors to replicate down the road. The key is to not get left behind.

by: Mark Trousdale, chief growth officer at Communify, a market data analysis firm for the investment industry