The Next Phase of Fintech: Where AI Meets the Infrastructure Layer
By Don Hoang
Fintech is entering its next act. After a decade defined by UX improvements and regulatory arbitrage, we’re now standing at the edge of something deeper: infrastructure transformation. AI is at the center of it—but not in the way most headlines suggest. The shift isn’t just about automating support or underwriting. It’s about re-architecting how financial systems work, how value moves, and how institutions interoperate.
This new era will be shaped by programmable money, real-time rails, and data-rich ecosystems—all stitched together by machine learning and embedded intelligence. The winners won’t just build apps on top of the system. They’ll rebuild the system itself.
Let’s unpack what’s changing, why it matters, and where I think the biggest opportunities are.
From Interface Innovation to Core System Change
If the 2010s were about making banking feel better—neobanks, sleek apps, faster onboarding—the 2020s are about making finance actually work better. The opportunity is no longer in consumer UX alone. It’s in rethinking the primitives:
Payments
Lending
Identity
Data orchestration
Risk infrastructure
We’re already seeing the signs. The growth of open banking, real-time payments, tokenized assets, and embedded compliance are early signals of a shift toward composability and programmability. But this is still early innings.
AI accelerates this transition. Why? Because traditional systems weren’t built to learn or adapt. They were built to process static rules. AI flips that logic. It enables systems that evolve—reacting to user behavior, regulatory nuance, fraud patterns, and credit signals in real time.
AI’s Real Advantage in Fintech: Systemic Intelligence
A lot of the noise around AI in fintech is focused on chatbots and customer support. That’s fine. But the real unlock is in the backend—at the infrastructure level.
Imagine:
Credit systems that learn and adapt across asset classes
Onboarding flows that optimize themselves by jurisdiction and risk profile
Payment engines that route value intelligently across tokenized rails
Treasury management that runs real-time scenario planning
These aren’t sci-fi use cases. The primitives are already here. The bottleneck is integration—and trust.
AI’s advantage isn’t just prediction. It’s the ability to create feedback loops across fragmented systems. That’s where fintech infra gets really interesting: when risk, capital, and operations become continuous, data-driven functions, not static rulebooks.
Tokenization Is a Rails Shift, Not Just a Wrapper
Much of the noise around tokenization misses the point. This isn’t just about wrapping real-world assets in a crypto skin. It’s about changing what’s possible when the unit of value becomes programmable.
If you think of money as a data object—rather than a balance on a ledger—everything shifts. You can:
Encode rights, rules, and compliance into the asset itself
Enable real-time settlements and conditional transfers
Move value across fragmented systems without reconciliation
From an investor lens, this changes the core assumptions of margin structure and velocity. You’re no longer just backing software that sits on top of a payment rail. You’re backing infrastructure that becomes the rail—and extracts value from every movement.
The parallel in the cloud world would be Stripe vs Twilio vs AWS. We’re now approaching the AWS stage: whoever owns the programmable core will win on throughput, reliability, and trust.
What Founders Should Be Solving Next
For founders, this is a wide open field. But it also demands a new mindset. You’re not just building a prettier front-end to a broken system. You’re rebuilding the plumbing.
Here are some themes I find most compelling:
1. Smart Risk Infrastructure
The old model: static rules-based decisioning with batch data.
The new model: continuous, adaptive underwriting based on streaming context, behavioral signals, and market shifts.
This isn’t just for lending. It’s relevant to payments, fraud, KYC, treasury, and even portfolio construction.
2. Composable Compliance
Most compliance software today is a UI layer on top of spreadsheets and rules engines. The opportunity is to make compliance composable and contextual—via APIs, embeddings, or on-chain attestations.
In a tokenized world, compliance shouldn’t follow the transaction. It should travel with it.
3. Programmable Liquidity
The marriage of tokenization and AI makes liquidity optimization a live function. Treasury and capital markets teams should be able to simulate scenarios, rebalance positions, and allocate capital with minimal human input.
This is especially ripe for private markets, structured credit, and cross-border flows.
4. Embedded Intelligence
Instead of SaaS tools with dashboards and alerts, imagine embedded systems that auto-act based on risk, policy, or preference. AI copilots are the tip of the spear, but the real win is full system automation—especially in back- and middle-office use cases.
What This Means for Investors
From a capital allocation standpoint, this shift requires rethinking how we evaluate fintech companies.
In the old world, you asked:
What’s the CAC to LTV?
Can they acquire regulated permissions?
How sticky is the app?
In this new world, the better questions might be:
Does this company build on or compete with the new rails?
Is it system-relevant?
Can it become infrastructure for others?
How defensible is its intelligence layer?
There’s a difference between riding the wave and being the wave. The best companies will create the conditions for others to build and grow.
Where the Opportunities Are
A few categories I think are early but interesting:
AI-native core banking platforms
Not just hosted cores, but learning systems that evolve with usage
Cross-border payment infrastructure with AI-optimized routing
Think programmable FX, dynamic corridor selection, and real-time liquidity optimization
Tokenized money market funds + short-term credit
Offering yield access and capital efficiency in real time, especially for treasuries and corporates
Composable identity and KYC as a service
Portable identity layers that move across wallets, banks, and platforms
AI-native accounting and reconciliation layers
Boring but massive—every tokenized ecosystem will need its own pipes
Final Thought: It’s Still Early, But It’s Not Too Early
Fintech is often accused of being slow to innovate. But that’s partly because the stakes are higher—trust, capital, and compliance don’t move fast. That said, we’re now entering a moment where the underlying conditions are aligning:
The tech is good enough (AI)
The rails are emerging (tokenization)
The pain is real (margin pressure + regulation)
The next generation of fintech leaders will be builders who understand infrastructure, not just interfaces. And they’ll win not because they can pitch AI—but because they can embed it into systems that learn, adapt, and scale.
