Wealthtech (2025)

Wealthtech startups are using the newest technologies to put a sheen on the way wealth is managed and created. Funding for the sector is up this year in Europe, with two quarters of year-on-year growth (see chart below).

The sector is broadly divided in three: management (the most lucrative and hard-to-break-into segment); creation (cost-effective alternatives to financial planners, mainly in the form of robo-advisers); and education (call it the rise of the “finfluencer” — part therapist, accountability coach and teacher).

There are a lot of legacy players in this field, especially in the management game because startups can't gain the trust of high-net-worth clients overnight. They have an easier time targeting broader demographics, like B2B clients and retail investors.

An ever-popular play is the robo-adviser, underpinned by machine-learning algorithms. These systems analyse data and offer guidance with a speed and thoroughness human advisers simply can’t (they are also less prone to bias). Newer generative AI models, underpinned by language-learning models, are the newest upgrade, enabling robo-advisers to better simulate how financial planners speak.

Soon, powerful agentic AI systems will likely mimic the strategies of successful investors. Will flesh-and-blood financial planners start to feel the heat? Possibly. Last year, the Financial Times published the story of tech entrepreneur Edward Morris, who turned to a chatbot for help with due diligence on an investment in chipmaker company Arm.

In an attempt to be more relatable, some companies have created digital financial advisers that use slang relevant to their target demographic. UK-based Cleo, for example, hires a team of comedians to roast its (mainly GenZ) customer base for poor spending habits. But startups are clever enough to know that humans still crave some soft skills from time to time. Some banks including Standard Chartered offer a 24/7 customer service chatbot in addition to access to human relationship managers.

There are the usual AI health warnings to think about: chatbots sometimes spit out inaccurate or misleading information (“hallucinations”). A major concern is whether AI may provide bad investment advice that results in users losing large sums of money. Bloomberg reported in 2019, for example, that Hong Kong-based entrepreneur Samathur Li Kin-kan lost $20m when he used a robo investor service.

Real people come into their own during a market crisis. Because AI systems are typically trained on legacy data, it is unclear how they will make decisions and adapt quickly to sudden shifts.