AI and advice. The next big financial trend

Looking across income bands, optimism didn't follow a growing curve. It formed a U shape. Sentiment peaked at either extreme and dipped in the middle, and this pattern kept showing up: in AI comfort, usage rates, feature preferences, trust signals. Consistently enough that we had to examine it.

The instinct when reading data like this is to reach for age as the explanation. Generational narratives are the default frame in fintech (digital natives, millennials, boomers, etc.). Our data does show some age variation: the 30–45 group was the most enthusiastic about AI, and the 46+ group the most cautious, but the differences are very moderate and the direction is broadly flat. The income pattern is more dramatic and consistent across more questions. A 100% to 30% swing across income bands doesn't have an age equivalent anywhere in this data.

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Who we think we build for

The reason fintech defaults to generational framing makes sense historically. The access problem it was built to solve was distributed along age lines; younger users were digitally active, but largely excluded from traditional financial infrastructure. Revolut, Wise, Sharesies… products like these were designed to close that gap, get people into the financial system, remove friction, and make investing, payments, and currency exchange available to people they previously weren't. Age was a reasonable proxy for the audience because age correlated with the problem.

But access and advice are different problems, distributed differently. Fintech largely solved the first. AI is positioned to solve the second. Income predicts who wants that second thing far better than age does. So who makes up the groups present in the U-curve?

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Unpacking the three groups

We’ll start with the lowest income group. They were universally optimistic on AI, but only 17% had actually used AI for a financial decision. Their optimism is aspirational, not experience-driven. They're not enthusiastic because AI has worked for them, but more open to AI because they can see what it might do for them.

66% in this group were comfortable receiving AI financial advice, compared to 17–30% across the middle income bands. Fintech gave this group access to the tools. It didn't, however, give them direct, personalised guidance on what to actually do with those tools. Professional financial advice has historically been a premium, human-delivered service. AI is making advice feel accessible for the first time.

The middle income group is a different story. As mentioned above, the most pessimistic, though 31% have already used it for financial decisions. Their hesitancy is based more on risk. This is the group most exposed to the consequences of getting it wrong: impacting dependents, financial obligations, limited margin to absorb mistakes. They have something to protect and not much room to recover if it goes wrong.

The highest income group comes back to optimism, at 67%, but for different reasons again. Their financial lives are already complex: multiple accounts, investments, institutions that don't talk to each other. What AI represents for them is integration, a way to manage sophistication at scale. 

One thing was consistent across all three groups: high digital comfort and a preference for self-service. No one in this group struggled to use digital tools. The variation in AI sentiment is specifically about what advice means to each group, and what it actually costs to be wrong.

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The product gap

Good Fintech covers the access problem. The AI opportunity is an advice problem, and it requires looking from a different angle. The audience most open to AI-driven financial guidance isn't the digitally active younger user fintech already serves well; it's the lower-income group that has the tools but not the guidance, and sees AI as the first time that guidance has felt within reach.

Lower income users who are currently optimistic will encounter products built with someone else in mind. When the experience doesn't match the expectation, that aspirational openness closes. Right now, there exists an opportunity to not only get in first, but to also empower people who probably haven’t had tailored financial guidance before, with reliable, safe, and trustworthy service offerings.

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The business case

Tailored advice has historically been a premium human-delivered service, a service that priced out a lot of people on lower incomes. AI has collapsed the marginal cost of delivering advice to near zero. The model we are proposing from this research is not ‘how to extract more from low-income users,’ it’s ‘a segment never historically serviceable for advice that has just become serviceable.’ The opportunity here is for Fintechs and financial institutions to lay tracks for AI and agentic experiences so users can safely engage with their own data for advice and direction, before they jerry-rig their own solutions or engage with riskier alternatives promising to fill the gap. 

Consider this. Does your target audience have an unaddressed advice gap, and what are they using to fill it?

The lowest income group already sees the value. The industry hasn't looked in their direction yet, but it will eventually, because the demand exists. The question is whether the design thinking arrives at the same time, or after the opportunity has already closed.