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Happy Wednesday, Fintech Listeners!
Money Experience Summit wraps up today (MX, as always, did a fabulous job on the event) and the Salt Flats Summit, put on by my friends at LoanPro, begins!
It’s been a fun and tiring week, and I’ll be sharing the highlights from it in Friday’s newsletter.
Before that, though, I have a new podcast I want to share with you!
Back-to-school season isn’t just for the kids. Simon and I showed up with sharpened pencils and somehow ended up sketching the outlines of fintech’s next decade.
In our grab bag of fintech startups (not investment advice, as always) we started with an “operating system for immigrants” that says more about population shifts and politics than product design, detoured into a BNPL story that suggests Millennials and Gen Z may never touch credit the same way again, and then tripped over the parts of finance we forgot to automate (spoiler: there are a lot).
Welcome Tech is best understood as an operating system for recent immigrants, and it mostly delivers on that promise. Think: education, healthcare (telemed, dental, vision, prescriptions), job placement, and a debit card/wallet so you can start participating in the U.S. economy on Day One.
The need is obvious. New immigrants face the same bundle (or rather, stack) of obstacles, and if you don’t have money for lawyers, paperwork becomes an existential threat. That’s where AI agents could step in as the lawyer you can’t afford.
I don’t have to tell you that immigration is a lightning rod in the US, UK, and elsewhere right now. And while immigration is politically out of favor, as demographers, economists (and anyone else who studies this stuff will tell you!), the economic consequences of losing population are unambiguously very, very bad.
Granted, we’re in a politicized environment where things swing back and forth very fast. The politics of today may lean anti-immigration, but the long-term incentives to reopen the gates (at least partially!) are enormous.
It’s fascinating to consider a fintech company, infrastructure company, or B2C operating system focused on something that feels brutally cyclical — like the mortgage industry — yet always necessary. In boom times, lenders thrive; in busts, the focus shifts to survival.
Again, the timing is fascinating. Welcome Tech raised close to $70M in 2020 and 2021 – a very different environment for VC, but also for immigration and politics – and tacked on a $7.5M “focus round” more recently.
As ever in fintech, timing is everything. Welcome Tech is building infrastructure that’s forever essential but easy to downplay.
#2: BNPL Grows Up
“Does every niche get its own BNPL provider?” Simon asked about Bumper, the UK startup offering installment payments for car repairs.
At first glance, if you squint, Bumper looks a bit like Klarna but with grease under its fingernails. But car repairs are the perfect wedge, and here’s why.
Urgent + expensive → no one wants to slap £1,200 of brake work on a credit card and revolve it.
Awful UX → dealerships run on paper and 20-year-old systems; point-of-sale financing feels like a trip to the dentist at best.
Distribution moat → Bumper has already integrated with 5K auto dealers in the UK. Affirm and Klarna may not ever bother to build that deeply into this niche.
The thing is, point-of-sale lending isn’t new — elective healthcare financing, retail POS installment lending, this stuff has been around forever. What’s new is the way BNPL has rewired younger consumers. For Millennials and Gen Z, BNPL has already become a default.
That’s why Bumper doesn’t need to become a Klarna-scale juggernaut to capture market share. The future of BNPL may not be defined by a few giants, but by a patchwork of vertical providers (auto, healthcare, home improvement), each embedded in industries where credit cards and legacy lenders have never been the best fit.
That generational shift leaves plenty of room for smaller players who crack gnarly distribution problems (like car dealerships) while the big platforms chase scale.
#3: The Parts of Finance We Forgot to Automate
For 40 years, financial services has been obsessed with straight-through processing — the dream of automating humans out of every decision.
And we got pretty good at it: once a credit or fraud model is in production, 99% of transactions are smooth sailing; untouched.
But here’s the blind spot: everything around those decisions is still painfully human. Prepping data. Backtesting models. Monitoring drift. Wrestling PDFs into SQL. The industry stopped noticing those gaps because they felt impossible to fix (so we outsourced them to offshore teams and moved on).
That’s why tools like Scalar Field (real-time backtesting) and Structify (AI agents that clean unstructured data) feel like a new frontier. They’re not about further optimizing well-optimized automated processes; they’re about reclaiming all the invisible work we pretended didn’t exist.
Most machine learning today is a two-day setup for a 15-second magic trick.
If large language models can compress that two-day setup, the economics of where and how that magic trick gets applied look very different.
🎬 DIRECTOR'S COMMENTARY
I was delighted on this podcast to chat with Simon, briefly, about his new job — working on go-to-market strategy for Tempo, the new payments-focused blockchain that Stripe and Paradigm are spinning up.
In addition to continuing with Fintech Brainfood, Tokenized, and Fintech Nerdcon (among his many projects), I’m excited for Simon to go even deeper into the crytpo/payments vortex (and report back to us what he’s learned!)
This conversation with Eric Woodward was fantastic. We talked about financial data infrastructure from a first principles perspective, which is a theme that we’ve been exploring throughout this entire podcast miniseries (sponsored by SOLO).
If you want to understand why a bunch of crypto companies are rushing to acquire national trust bank charters from the OCC, this will be a useful podcast for you.
Fintech risk & compliance leaders take the mic to tackle today’s biggest questions and assumptions: when does elbow grease outperform automation? What are the use cases where AI actually earns its keep? And more! Register here.
