It’s a great time to be a fintech, particularly an AI-native fintech. Everywhere you look, the establishment banks appear saddled with efficiency drains and unnecessary complexity. Once considered the cost of doing business, complexity in the form of manual data extraction from multiple silos and fragmented reporting systems across departments now resembles an albatross. But if you’re a fintech, you’re probably flying high.
It’s no wonder banking leaders are eager to rapidly automate the extracting, reconciling, formatting, and interpreting of information they need just to make decisions. But they often view AI as a magic wand they can wave or a mysterious elixir that will cure what ails their institution. It will float down from the heavens, penetrate every last corner of their system, and fix it all.
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Much to their chagrin, AI requires a high level of architectural efficiency even just to function at all. We know this because we’ve seen the architecture that undergirds the most successful AI-native fintechs.
AI-native fintechs were initially criticised for what looked like a monolithic approach to building infrastructure. However, this wasn’t the rigid, unchangeable ‘monolith’ of old software; rather, it was a deliberate choice to build a deeply unified data architecture. Granted, this was a fairly new approach. Prior innovations, such as the neobank movement led by Revolut and N26, changed the presentation layer. Neobanks and innovations on cloud-native architecture that followed were real innovations, but somewhat limited. AI, by comparison, is the first major innovation in finance in decades that can be implemented at the very core of a bank. It’s not an improvement or an expansion on something that already exists.
A typical bank has a core orbited by a number of services. When they integrate an AI agent, the agent is precluded by this vast constellation of services from moving through everything and accessing all the data everywhere. A monolithic AI-native fintech, however, has highways through which this agent can move and collect data. The highways are the main event. Analysis and decision-making move fast because the infrastructure is there to speed them along.
How AI-native fintechs built for speed
AI-native fintechs are built on and for raw data and instant intelligence. Their approach to analytics, once synonymous with reporting, depends on serious query refinement. Rather than constantly export data or build dashboards manually, they direct the AI through these queries. A well-refined query enables AI to do the real work of banking analysis. Beyond transaction authorisation and settlement, it can analyse total turnover, online purchases and contactless payments, ATM withdrawals, credential-on-file transactions, and POS terminal payments.
This analysis begins with sorting. In-store purchases (contactless and POS), online commerce (3DS or stored credentials), and cash usage (ATM withdrawals) form the basis of compounded queries, which then produce 30-day trend analysis, deeper wallet-level insights, and continuous drilling into subcategories without rebuilding reports. AI-native fintechs excel at this level of data sorting, and as a result their decision-making is dynamic instead of scheduled. They receive clarity at speed.
Everything moves faster than once thought possible. Business onboarding is 95% faster, shrinking from between three to ten days to under an hour. Drafting manual risk narratives typically takes a couple hours; AI-native fintechs automate them in mere minutes. Risk monitoring goes from manual and periodic to real-time with 100% coverage. Entire days become mere minutes.
TradFi’s uphill AI battle
Institutional banks see this clarity-at-speed system, and naturally seek to adopt the AI that makes it possible. Prior to AI-native fintechs, this internal efficiency was simply a fact of life. Now it is a competitive disadvantage. Insights produced by compounded queries require analyst intervention just to generate. Data has to be reconciled across multiple systems, restricting reporting cycles to weekly or monthly cadences—only for follow-up queries to restart the process. Every new query is a new delay. Gamed out over years, this creates strategic drag.
Retrofitting is no small task in the face of interconnected legacy systems, organisational silos, and layered management structures. Beyond the strictly technological, internal transformation always comes with a high operational and political cost. An AI-native fintech’s model can support as many as 1 million customers per five staff members, whereas a traditional bank currently requires roughly 150 employees to manage a similar load. Even if the retrofitting is successful, what’s to be done about the 145 newly redundant employees?
Institutional banks are learning the hard way that rewiring legacy systems can be more complex and expensive than building new. Their options have narrowed somewhat to reinvent themselves or, quite frankly, perish. No AI-native fintech faces this same conundrum.
Who owns the financial future?
The AI-native fintech enjoys efficiency by design. In practice, this looks like unified infrastructure and minimal operation friction. Data is structured from the jump and analytics capabilities are embedded into the system. There aren’t several steps between query and decision. Their leadership can refine multiple queries and drill into subcategories around structured analytics, then immediately make a decision.
The emerging competitive gap between AI-native fintechs and legacy banks is virtually unprecedented. Customer features once reigned supreme, but now speed does. Internal, AI-supercharged speed-at-scale translates to external, user-facing benefits. Instant analytics and continuous refinement beat out delayed reporting and static dashboards, all undergirded by internal clarity and structural efficiency. If you’re a fintech, you’re in the ideal spot to use AI to pull ahead of the pack. Legacy banks may have trouble matching the pace, but it’s still anyone’s race. Let the fastest decision-maker win.
Raman Korneu is CEO and co-founder of neobank myTU
