Reading time: 5 minutes
The automation of financial services isn’t a future scenario. It’s a current operating reality – and the institutions moving fastest aren’t the ones with the biggest budgets. They’re the ones that solved a problem most banks are still pretending isn’t urgent: finding engineers who can actually build AI systems that work in production.
What’s actually happening
The financial sector has been talking about AI transformation for years. What’s changed in the last 18 months is the gap between institutions that are shipping and those that are still in pilot mode.
Around 70-80% of financial institutions are already using or actively exploring AI in some form – but the distribution of outcomes is deeply unequal. The banks and fintechs that are ahead share a common characteristic: they stopped treating AI as an IT project and started treating it as a core operational capability. Fraud detection models that run in real time. AML systems that reduce false positives without expanding analyst headcount. Credit decisioning that processes applications in seconds rather than days. These aren’t moonshots – they’re in production, at scale, at institutions across Western Europe and North America right now.
EU banking institutions are projected to invest tens of billions of euros in AI by 2030. The distance between those leading and those still in pilot mode is almost never technology. It’s talent.
The skills gap that nobody wants to name
Here’s the problem that boardroom AI strategies consistently underestimate: the engineers capable of building production-grade AI systems in regulated financial environments are among the scarcest professionals on the market.
This isn’t a generic “tech talent shortage.” It’s a specific convergence of requirements that almost no candidate pool fully satisfies: deep ML engineering experience, familiarity with financial data structures and regulatory constraints, production deployment experience at the scale financial institutions operate, and – increasingly – fluency with the LLM and agent architectures that are reshaping how AI systems are built.
Job postings for AI engineers in fintech and banking saw triple-digit growth year-over-year in 2025 – the fastest-growing technical role category in financial services. In the Netherlands, Belgium, and Luxembourg, the domestic supply of engineers with this profile is structurally insufficient for current demand. Local hiring timelines for senior AI roles in financial services regularly stretch beyond two months. Compensation benchmarks are being pulled upward by global competition – US firms, UK scale-ups, and remote-first employers are all drawing from the same small pool. And the engineers who do exist in these markets are not sitting idle, waiting for your job posting.
The institutions winning this race aren’t necessarily outbidding everyone else. They’re finding talent through different channels.
Why Poland and Portugal are the answer – and not in the way you think
The case for nearshore AI talent from Poland and Portugal is often framed as a cost argument. It shouldn’t be – or at least, cost shouldn’t be the lead.
The real argument is depth and regulatory fit.
Poland has produced one of Europe’s largest concentrations of AI and data engineering talent. Warsaw, Kraków, and Wrocław are home to engineering teams for some of the world’s most demanding financial institutions – not because they’re cheap, but because the talent that exists there has been shaped by working on the hardest problems in European finance. The density of senior ML engineers, MLOps specialists, and AI solutions architects with genuine production experience in regulated environments is higher than in most Western European markets.
Portugal’s engineering ecosystem has matured rapidly, driven by a combination of strong university output, an influx of international technology companies, and a growing concentration of fintech and financial services clients. Portuguese engineers working in AI bring a level of English proficiency and cross-cultural fluency that makes integration into Benelux or US-based teams structurally straightforward.
Both markets operate within EU regulatory frameworks. Engineers who have built AI systems in Poland or Portugal understand GDPR, the EU AI Act, and the compliance constraints that shape financial services architecture. This is not a minor point – it materially reduces the risk and rework cost of deploying AI in regulated environments compared to talent from outside the EU regulatory perimeter.
And both markets offer genuine time zone overlap with the Netherlands, Belgium, and Luxembourg. Real-time collaboration, not asynchronous coordination.
The risk that’s being mispriced
There’s a version of this conversation where a CTO hears “nearshore” and thinks: communication overhead, quality uncertainty, delivery risk. That framing is outdated – and in the AI talent context, it’s inverting the actual risk calculus.
Spending four months trying to hire locally, losing candidates you were close to, and watching a competitor deploy a system you’re still scoping – that’s the real risk. Time is the most mispriced variable in AI talent strategy. Every quarter of delay has compounding costs: competitive position, institutional knowledge that builds around deployed systems, and the organizational confidence that comes from shipping something that works.
What this means for your hiring strategy
If your organization is building AI capabilities in financial services – fraud, AML, credit, customer decisioning, process automation – the question isn’t whether nearshore European talent is viable. The question is whether your current hiring process is designed to access it.
Most aren’t. They’re built around local job boards, domestic salary benchmarks, and sequential interview processes that assume candidates will wait. In the current market, that assumption is wrong.
The companies moving fastest have a different model: pre-vetted talent pools, defined speed SLAs, and partners who have already done the work of identifying which engineers actually deliver in production – not just which ones look good in a first interview.
That’s what the gap between pilot and production usually comes down to. Not strategy. Not budget. Not technology.
The right engineers found through the right channels, before the window closes.
ITDS connects financial services organisations with senior AI engineers from Poland and Portugal – pre-vetted, production-ready, and deployable in weeks. If you’re building AI capabilities in banking or fintech and want to understand what’s available: