Speed is key for fintech investors
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It is a sign of the times that a two-person founding team can now build what once required 30 engineers, and investors have begun pricing that shift into every term sheet. For AI‑native fintech startups, speed itself has become the pitch, and increasingly, the edge that determines who survives.
In H1 2026, AI‑native fintech deals jumped from 13 to 50 year‑on‑year, a rise of 284.6%, while funding more than tripled to €453 million, according to Sifted’s European fintech analysis. Forbes’ Fintech 50 for 2026 saw three AI‑native newcomers make the list for the first time. Meanwhile, Europe’s wider fintech deal count hit its lowest point in over a decade. The message is clear. Capital is concentrating around the startups that can ship fastest.
The Need For Speed
Speed matters in cross‑border payments because delays have real-world consequences. More than 800 million families worldwide depend on timely payments. Small business owners rely on fast settlement to pay staff and keep operations running. In corporate contexts, deals fall apart when payments get stuck in regulatory mazes or take too long to convert into foreign currency.
AI can accelerate those rails, but it can also create new risks, and as Ben Chisell, Group CEO of payments infrastructure company Paysend, points out, misapplied AI can backfire. “Fintech leaders adopting a scattergun approach to AI by deploying it across broad swathes of the business in pursuit of optimizing every workflow will find that it introduces more problems than solutions,” he says. “It’s an avoidable and unnecessary risk in a financial context, where a single wrong decision can have a material impact on customers, leaving them out of pocket.”
The Value Of Speed To Investors
Investors are now explicitly pricing speed and AI leverage into term sheets, reshaping how fintechs build teams and prioritize their roadmaps, yet Chisell says many founders still misunderstand how AI works. “It’s seductive to jump on the AI bandwagon, but it will lead you astray,” he says. “Just look at how quickly ‘tokenmaxxing’ went out of fashion, and how much money was needlessly spent trying to impress investors.”
It is Chisell’s engineering and data science background that has shaped Paysend’s hiring philosophy. “I look for problem‑solvers rather than managers who oversee nebulous processes,” he says. “Before making any hiring decisions, we work hard to understand exactly what it is we’re looking for, write incredibly precise and detailed job descriptions, and then scour the market for people who excel at those things.”
A Controlled AI Roll-out
And that discipline extends to how Paysend rolls out AI internally. The company restricts access to its most technically proficient employees, those who deeply understand payment workflows, and only expands access once results are proven.
He says: “The outcomes of this approach are agents building features end‑to‑end for our product team, growth agents finding the best opportunities across our thousands of corridors, account management agents identifying the biggest opportunities for our clients and country manager agents understanding their entire P&L and customer feedback to identify the right growth plans.”
The Ability To Grow Without Hiring
Speed over headcount is becoming another defining theme across fintech. Jacob Bennett, CEO and cofounder of relationship‑intelligence platform Crux Analytics, believes that AI is levelling the playing field between small lenders and national banks.
He says: “At an overwhelming number of industry conferences I attend, someone brings up JPMorgan’s technology budget, which now approaches $20 billion a year. The question that often follows is how anyone else is supposed to compete, but the more telling figure sits in the same announcements.
“Chase is also hiring more than 1,000 additional business bankers nationwide because relationships in small-business lending are still won by people. Headcount is how the largest bank in the country buys capacity, and AI now gives smaller institutions the ability to build that without hiring anyone.”
Developing The Infrastructure
If two startup founders can now build what once required ten times as many engineers, what distinguishes the AI‑native fintechs that will win? Bennett says the answer lies in proprietary data and infrastructure. “People ask me constantly why a bank cannot just take some public data and plug it into ChatGPT, and my answer is usually that they should go and try it. The results are underwhelming, because the model is only as useful as what you feed it. Building that layer- the proprietary data, the infrastructure, the systems that turn raw information into something a banker can act on- is what we have spent the last three years doing.”
Success metrics have shifted accordingly. Headcount used to be a proxy for progress, whereas the key numbers now are revenue per employee and how quickly customer feedback becomes a live product. Investors have caught up to this too.
“WhatsApp sold for $16 billion with 55 employees and Instagram for $1 billion with 13, and in both cases, they kept startup speed while building the discipline underneath it that made them worth acquiring,” says Bennett. “That balance is the thing I watch, and it is a lesson I learned long before Crux, making decisions for my own business in the morning that Fortune 500 teams spent all afternoon overthinking.”
Balancing Speed And Flexibility
Across all industries, financial inefficiencies remain a huge challenge. Business owners still spend an average of 86 hours a year chasing invoices. AI could automate much of that workflow, yet adoption has been slow, creating opportunities for companies like Aria, an embedded invoice‑financing platform helping businesses get paid on time. Since its launch in 2020, the company has processed over €1.5 billion in invoices by balancing speed for suppliers with flexibility for buyers.
“AI is effective when applied to the right scenarios, but can be destructive when not,” says CEO and cofounder Clément Carrier. “There’s a risk that business leaders will apply an uncritical lens and see AI tools as shiny and new and succumb to the temptation to plug them into anything and everything in search of speed and efficiency. But that’s a recipe for disaster in an industry like ours, where one mistake can cost a business thousands or millions. While speed is the ultimate goal in resolving late payments, it should never be sacrificed for accuracy and security.”
What that means in practice is being deliberate with the use of AI and understanding what it’s best at. In Aria’s case, fraud detection is an excellent use case given that AI excels at pattern recognition. “The speed and accuracy of its output are something that a human being could only ever aspire to,” says Carrier.
With wider fintech deal numbers falling but AI‑native funding soaring, founders have had to adjust their pitch to meet investor expectations that companies can scale faster with fewer people. Carrier says investors are increasingly skeptical of superficial AI claims.
“Investors are becoming wise to the AI hype and are especially savvy when it comes to figuring out whether a business is truly AI‑native or just bolting on AI tools to an existing workflow in a patchwork,” he says. “We’ve resisted the pressure to roll out AI tools across the business solely for the sake of it. That’s a decision many investors have come to respect because it shows our disciplined approach to technology rather than a simple checkbox exercise.”
