London-based Zalos has raised €3.1 million to push a focused idea forward. AI agents that actually do the work inside finance teams. Not dashboards, not copilots, not layers of “insight.” The focus is execution.

Their approach is straightforward on paper. Instead of replacing existing systems, Zalos builds agents that log into them and run workflows end to end. That detail is easy to overlook, but it is where most of the value sits.

Why finance teams are still stuck in manual work

Most enterprise finance teams already have a full stack of tools. ERPs, accounting platforms, reporting layers. On paper, everything should work together.

In reality, a large part of the work still happens manually. Teams download files, move data between systems, reconcile numbers, and fill in the gaps where systems do not connect properly. The issue is not the lack of tools. It is the lack of continuity between them.

Zalos is targeting this “in-between” layer. Their agents behave like human users. They log into systems, extract data, process it, and complete tasks such as reconciliations or submissions. The key constraint is that nothing needs to be replaced. For most finance teams, that alone makes the approach viable.

The real bet: non-invasive automation

A broader pattern is starting to form across fintech and enterprise software. Instead of asking companies to rebuild their stack, new players are working on top of what already exists.

Zalos fits directly into this pattern. Their agents operate through user interfaces rather than deep integrations. This allows them to function across fragmented environments without waiting for clean APIs or perfect system design.

It is a practical decision. Finance stacks are rarely clean. They are full of legacy tools, custom workflows, and edge cases that do not scale well with traditional integration models. An agent that behaves like a user can move across these systems with less friction.

There are trade-offs. Finance requires reliability, traceability, and control. Zalos is addressing this by adding audit trails and oversight into the workflow. It is still early, but the direction reflects a clear understanding of how finance teams operate.

A crowded but focused category

Zalos is part of a broader wave of companies working on finance workflow automation. The space is getting more attention, and the use case is relatively well defined.

Finance operations offer a good testing ground for this type of automation. The tasks are repetitive, the rules are structured, and the cost of errors is high. That combination creates pressure to improve efficiency while maintaining strict control.

This is where AI agents start to make sense as operators, not just assistants.

What this means in practice

If this model works, the day-to-day work inside finance teams will shift. Less time will be spent on manual reconciliation and moving data between systems. More time will go into reviewing outputs and focusing on higher-level decisions.

At the same time, adoption will not move quickly. Finance teams are cautious by design. Trust, compliance, and auditability will shape how and when these tools are used.

Key takeaways for fintech startups

A few patterns stand out from this move.

• Solving around existing systems is often more realistic than replacing them
  
  
• The real opportunity is in removing invisible manual work, not adding new features
  
  
• AI agents are moving from assistants to operators
  
  
• Finance operations is becoming a primary testing ground for agent-based automation
  
  
• Adoption depends as much on trust and auditability as on performance

If you are building in this space, positioning matters as much as product. If you want help shaping that story or sharpening your go-to-market, reach out to us at Your Fintech Story.

Spade’s $40 million Series B is not just another funding headline. It points to a very specific bottleneck in modern finance: messy, inconsistent transaction data. The company is building a data and AI platform focused on making sense of card transaction data at scale. That sounds niche, but it sits right in the middle of how banks, fintechs, and payment companies actually operate.

Behind every payment, there is a surprising amount of ambiguity. Merchant names are inconsistent, categories are unreliable, and locations are often wrong. That noise makes everything harder. Fraud detection, customer insights, underwriting, and even basic analytics all depend on clean data. Spade’s pitch is simple. Clean the data layer, and everything built on top starts to work better.

From merchant data to decision infrastructure

Spade started with a focused use case: improving merchant-level data for card issuers. The company links transactions to verified merchant identities and enriches them with details like category and location. This may look like a backend improvement, but it has a direct impact. Better merchant data improves fraud models and helps detect unusual behavior faster, while also giving issuers a clearer view of how customers spend.

Now the company is expanding beyond that initial layer. The Series B is aimed at building a broader data and AI platform for financial services. That shift matters. It moves Spade from a data provider to something closer to infrastructure. Instead of just supplying cleaned data, the goal is to support decision-making systems across the stack.

Growth signals are already there

The funding comes alongside strong growth, with rapid year-over-year expansion and very high daily transaction volumes. These numbers suggest the problem is not theoretical. Financial institutions are already relying on this layer at scale, which says a lot about how critical this type of infrastructure has become.

It also highlights something broader. Data infrastructure in fintech tends to compound. Once integrated, it becomes deeply embedded in workflows, making it harder to replace. That creates a different kind of defensibility compared to front-end fintech products, which are often easier to swap out.

Why this matters for modern finance

A lot of fintech innovation focuses on the user interface. New apps, better onboarding, cleaner design. Underneath, many systems still rely on fragmented and low-quality data. Spade is working on that underlying layer. It is not visible to end users, but it directly affects how well financial products perform.

Better data leads to better risk models, better targeting, fewer false positives in fraud, and more accurate insights. It also enables more advanced AI use cases. Without structured and reliable input data, AI systems struggle to deliver consistent results.

Key takeaways for fintech startups

A few practical observations stand out from Spade’s approach:

• Clean data is still an unsolved problem in many parts of fintech, even in mature markets
  
  
• Infrastructure plays can scale quietly but become deeply embedded over time
  
  
• Narrow initial use cases can expand into broader platforms if the underlying problem is real
  
  
• AI in finance depends heavily on data quality, not just model sophistication
  
  
• Backend improvements often drive more long-term value than front-end features

If you want help shaping your strategy or positioning your product in a crowded market, feel free to contact us.

Revolut reported $2.3bn in profit on $6bn in revenue for 2025. That is not just growth. It signals that the model is reaching a level where scale starts to behave more like a bank than a startup. This is also their fifth consecutive year of profitability, which puts them in a very small group of fintechs that have managed to grow fast without losing control of the bottom line.

The headline numbers are strong, but the more interesting question is what sits behind them.

Revenue is no longer one-dimensional

Revolut’s revenue growth is coming from multiple directions. It is no longer tied mainly to interchange or foreign exchange fees, which tend to be the early drivers for many fintechs. Instead, the business now includes subscriptions, business accounts, and wealth-related products, all contributing in a meaningful way.

This kind of diversification changes the nature of the company. It reduces dependence on a single stream and creates a more stable base. It also starts to resemble the structure of traditional banking revenue, even if the user experience still feels very different.

Customer growth is still doing heavy lifting

Revolut’s customer base continues to expand at a rapid pace, reaching tens of millions globally. Growth at this scale is not just a vanity metric. It directly feeds into revenue through higher transaction volumes, subscription uptake, and cross-selling of additional services.

A key shift is the increasing number of users treating Revolut as their primary account. That changes the relationship. Users who rely on the app for everyday banking are more likely to stay, spend more, and adopt new products over time. This is where fintechs start to build real depth, not just reach.

Lending is quietly becoming the next engine

One of the more important developments is the expansion of Revolut’s lending business. Consumer lending, credit cards, and overdrafts are becoming a larger part of the mix. This is a natural step once a fintech has both scale and customer trust.

Lending introduces a different economic profile. Payments and subscriptions can drive growth, but lending is where margins become more substantial. At the same time, it brings exposure to credit risk, which requires a different level of discipline and infrastructure.

Scale brings new pressure, not just upside

Growth at this level does not simplify operations. It adds complexity. As lending increases, so does exposure to credit losses, even if the overall ratios remain under control. The company is also operating across multiple product areas, some of which carry additional operational and reputational risks.

This is the part where many fintechs start to feel the weight of behaving more like financial institutions. Regulation, risk management, and public scrutiny all increase alongside scale. The model can work well, but it requires constant adjustment.

Key takeaways for fintech startups

A few grounded observations from Revolut’s trajectory:

• Diversifying revenue early helps avoid dependency on a single product
  
  
• Becoming a primary account strengthens retention and long-term value
  
  
• Lending can significantly increase margins but requires strong risk management
  
  
• Growth at scale introduces complexity rather than reducing it
  
  
• Regulatory milestones can unlock new revenue opportunities when used strategically

If you are thinking about how your fintech evolves beyond early traction, these are practical points to consider. If you want to sense-check your strategy or growth plan, feel free to reach out.