For years, personal finance technology has treated visibility as the product. Connect your accounts. See your net worth. Track your spending. Watch your portfolio move in real time.
That was useful when most people had a fragmented financial life and very little access to their own data. But visibility alone is no longer enough. In open banking and open finance, the harder problem is not whether financial data can be aggregated. It is whether that data can be translated into decisions people can trust.
This distinction matters because the next generation of WealthTech will not be built around prettier dashboards. It will be built around decision infrastructure: systems that can collect fragmented financial data, normalize it, interpret it in context, and help users understand what action, if any, should follow.
In my work with Exirio, a multi-asset WealthTech platform, I have seen this shift first-hand. Users do not only want to know the value of their portfolio. They want to understand concentration risk, idle cash, tax exposure, currency exposure, underperforming assets, and whether their current allocation still matches their goals. The product challenge is to move from “Here is everything you own” to “Here is what this means.”
That is a much harder product to build.
Aggregation is becoming the baseline
Open banking has already shown that regulated access to financial data can change consumer financial services. In the UK, open banking reached more than 15 million users and more than 2 billion API calls in a single month in 2025, according to Open Banking Limited. The direction of travel is clear: financial data is becoming more portable, and consumers are becoming more comfortable granting access to trusted third parties.
Europe is moving in the same direction through the proposed Financial Data Access framework, usually discussed as part of the broader open finance agenda. The goal is to extend structured data access beyond payment accounts into a wider range of financial products. In the UK, the FCA has set out a roadmap for open finance as part of a wider smart data future.
For WealthTech builders, this is an important moment. More data access should make it easier to build better personal finance and investment products. But it also creates a trap. When every serious platform can access similar data sources, aggregation stops being a defensible advantage.
The differentiator becomes what the platform does after the data arrives.
A user with ten accounts, three currencies, public equities, private investments, pensions, real estate exposure, and cash balances does not need another static chart. They need a coherent financial picture. That requires data engineering, product judgement, and a careful understanding of user behaviour.
The real problem is financial context
Financial data is messy because financial lives are messy.
A bank balance does not tell you whether cash is idle, reserved for taxes, or waiting for a property purchase. A portfolio allocation does not tell you whether risk is intentional or accidental. A large position in one company may be a deliberate conviction, an inherited holding, an employee stock plan, or simply inertia.
This is where many finance apps fail. They classify transactions, calculate balances, and display performance, but they do not understand context well enough to guide the user. The result is an elegant interface that still leaves the user alone with the hardest question: what should I do next?
AI can help, but only if it is applied carefully. In asset and wealth management, AI is already being explored across distribution, operations, compliance, investment workflows, and software development, as McKinsey has noted. But in WealthTech, AI should not be treated as a magic advisory layer. Financial decisions are too sensitive for vague recommendations generated from incomplete context.
The more realistic opportunity is to use AI and rules-based intelligence to surface patterns, detect anomalies, and generate explainable nudges.
For example, a platform might identify that a user’s portfolio has become heavily concentrated in one asset class, that cash drag has increased over several months, or that exposure to a particular currency is higher than the user may realise. These insights should not pretend to replace a regulated advisor. They should help users ask better questions and make more informed decisions.
The best WealthTech products will know when to recommend action, when to recommend review, and when to stay silent.
Decision intelligence needs product restraint
There is a temptation in financial technology to turn every insight into a notification. That is usually a mistake.
A product that tells users too much becomes noise. A product that tells them too little becomes a dashboard. The difficult work is deciding which signals deserve attention and which should remain in the background.
This is partly a data science problem, but it is also a product design problem. A useful WealthTech platform needs thresholds, prioritisation logic, user segmentation, and feedback loops. It needs to understand the difference between a genuine risk signal and a normal market fluctuation. It needs to adapt to different user profiles, from self-directed investors to high-net-worth individuals managing complex portfolios across jurisdictions.
In practice, I think about this through three layers.
The first layer is data integrity. Before a platform can provide meaningful insights, it must know that account connections, asset prices, classifications, and historical records are reliable enough to support interpretation. Bad data does not produce weak insights. It produces dangerous confidence.
The second layer is contextual modelling. This is where the platform starts to understand the user’s financial structure: asset allocation, liquidity, time horizon, currency exposure, tax-relevant events, and behavioural patterns. Without context, personalisation is cosmetic.
The third layer is decision support. This is the layer where the product decides what to surface, how to explain it, and whether the user should be nudged, educated, warned, or simply shown a clearer view of the situation.
Many companies want to jump directly to the third layer because it is the most visible. But without the first two, decision support becomes theatre.
Embedded WealthTech will raise the bar
The next wave of WealthTech is unlikely to be purely direct-to-consumer. Banks, neobanks, insurers, brokers, and workplace platforms all want to deepen customer relationships through better financial intelligence. This creates a strong case for B2B2C WealthTech infrastructure.
Embedded wealth tools can solve a distribution problem that many standalone finance apps face. But they also raise product expectations. A bank or neobank cannot afford to embed a tool that produces confusing insights, weak data quality, or recommendations that create compliance risk.
This is why WealthTech companies need to think less like app developers and more like infrastructure providers. The product must be modular enough to integrate into existing financial ecosystems, but intelligent enough to create value beyond raw account aggregation. It must also respect the regulatory boundary between information, guidance, and advice.
That boundary is especially important. The industry should not blur it for the sake of engagement metrics. Consumers need products that help them understand their financial lives, not products that push them into unnecessary activity.
A good system should reduce anxiety, not increase trading frequency.
Trust will be the real moat
As open finance expands, consumers will be asked to share more sensitive data with more providers. That makes trust a core product feature, not a compliance footnote.
Trust is built through clear consent flows, transparent data use, reliable connections, explainable insights, and the ability for users to understand why a recommendation or nudge appeared. The OECD’s work on open finance is useful here because it frames open finance not only as an innovation opportunity, but also as a policy and implementation challenge involving privacy, safety, and responsible data access.
If a product cannot explain its logic in plain language, it should be careful about presenting that logic as financial intelligence.
This is also where AI adoption in WealthTech needs discipline. AI-generated insights should be auditable. They should be constrained by product rules, compliance requirements, and user context. The output should be understandable enough for a user, a product manager, and a compliance team to evaluate.
The companies that win will not be the ones that add the most AI features. They will be the ones that turn complex financial data into trustworthy, proportionate, and useful decisions.
From visibility to judgement
The first phase of personal finance technology gave people access to information that had previously been trapped inside institutions. That was a major step forward.
The next phase has to go further. It has to help people make sense of that information.
Open finance will create more data portability. AI will create more ways to interpret that data. Embedded distribution will create more channels for wealth tools to reach users. But none of this guarantees better outcomes.
Better outcomes will come from products that combine data access with judgement: knowing what to show, what to hide, what to explain, what to flag, and what not to automate.
The future of WealthTech is not the dashboard. The future is the decision layer that sits behind it.
