Don’t launch another AI initiative until you know where your data’s been.
There’s a strange irony in fintech right now: the sector that helped digitize banking is now being outpaced by its own ambition.
Everyone’s talking about AI. Everyone’s talking about the next analytics platform, the next-gen consumer insight engine, the personalized experience revolution. But behind the scenes? Decades of growth, platform shifts, and regulatory changes have left many institutions with deeply fragmented data environments. What once worked is now holding back innovation.
One executive at the Databricks Data + AI Summit 2025 described it bluntly: “bad data on steroids.” That about sums it up.
The Real Problem Isn’t a Lack of Innovation. It’s a Lack of Foundation
Legacy fintechs didn’t start with bad data practices. But over time, systems multiplied. Product launches moved faster than data models could keep up. M&A brought in new platforms. Compliance shifted. And somehow, five different teams ended up calculating “monthly active user” in five different ways.
It’s not just inefficient, it’s dangerous. When your insights depend on which version of the truth you pull from, even basic reporting becomes a risk.
Worse, this mess sets up AI for failure. Everyone wants the magic, but few want to tackle the behind-the-scenes data work that makes it possible. It’s not surprising that, according to Gartner, poor data quality costs organizations an average of $12.9 million annually. And in finance, where models drive real money movement, that price tag can be far higher.
Data Access ≠ Data Usability
At DAIS 2025, Zillow’s team shared their push to “break down silos and enable self-service.” It’s a worthwhile goal, and one we see many tech-forward companies striving to achieve. But self-service only works if the data behind it is trustworthy, consistent, and actually usable.
That means governance. It means cleaning up lineage. It means setting access policies that don’t just keep data safe, but also visible to the people who need it. Tools like Unity Catalog exist for a reason. Without foundational governance in place, “democratizing data” can quickly turn into yet another buzzword, promising more than it can realistically deliver.
The Compliance Clock Is Ticking
All of this would be easier to ignore if regulators weren’t getting sharper. GDPR, CCPA, GLBA – pick your acronym. The rules are tightening, and it’s only a matter of time before legacy systems fail to keep pace.
Barclays, for example, is investing heavily in building a “future-ready” enterprise data platform. Not because it’s trendy, but because it’s necessary. You can’t build consumer trust on a foundation of inconsistent records and opaque data pipelines.
And if your AI model makes a wrong prediction because your data pipeline dropped a few rows or fed it outdated info? That’s more than just a tech hiccup – it could be a compliance red flag.
Fintechs Are Not Lacking Ideas, They’re Lacking Execution Capacity
Here’s the real kicker. Most of the fintech teams we talk to know what needs to be done. They’ve got smart architects, sharp analysts, even solid roadmaps. But they’re stuck trying to modernize while keeping the plane in the air.
Internal teams are often swamped just keeping systems running. Hiring top-tier data engineers is harder than ever. And trying to bolt on another tool without untangling the backend just adds more complexity.
That’s where the right kind of help makes all the difference.
This Is Where Nearshore Engineering Makes Sense
Modernizing a data foundation isn’t a one-off project; it’s an iterative process. It requires close collaboration, constant testing, and real-time feedback. You don’t get that from an offshore team 12 hours ahead. You don’t always get it from a giant consultancy either.
Nearshore teams offer a sweet spot: high-skill engineering talent in time zones that align with your core team. You get velocity without sacrificing context. You can pair on Slack, debug live, or workshop a schema mid-sprint.
How Distillery Can Help
Distillery works with fintech companies to help strengthen what’s working and address what’s holding them back before scaling to the next phase.
We help:
- Unify and clean scattered data environments. For example, syncing real-time account data with regulatory systems like APEX to ensure accuracy and compliance.
- Build governed, secure access using tools like Unity Catalog, so teams trust the data they use and know who has access to what.
- Modernize legacy systems with scalable, modular architectures, like we did for Rakuten Super Logistics, enabling real-time reporting and streamlined data operations.
- Embed nearshore engineers who work as true extensions of your team, not siloed vendors, so projects move faster and stay aligned with business goals.
Our teams bring a practical, hands-on approach to turning theoretical roadmaps into real systems that work. We’ve done this before. And we don’t disappear after delivering the slide deck.
Final Thought
There’s nothing wrong with wanting to push the frontier. AI will reshape fintech. But only for the firms that have their house in order.
If your dashboards don’t align, your pipelines are stitched together with cron jobs, and your analysts don’t trust the numbers – they won’t trust your AI either.
Fix the data. Then scale the future. Let’s talk about how we can help you get there.