Most people who rely on Elastic every day have no idea they're doing it. That's roughly the point van de Velde made when he sat down with press at Money20/20 Europe in Amsterdam: the company's technology sits so far under the hood of financial services that even its own customers are sometimes surprised by what it's already doing for them.

As a Principal Solution Architect at Elastic, van de Velde walked through how a platform built around search has expanded into one of the less visible but more essential layers of fintech infrastructure — and why that matters more, not less, as AI adoption accelerates.

A background platform hiding in plain sight

Elastic's technology shows up across nearly every industry, but in financial services its footprint tends to stay out of sight. Van de Velde puts that down to how adaptable the underlying platform is.

He describes Elastic as fundamentally a search company — one whose tools for search, observability and security get deployed across sectors worldwide, with a significant share of that work happening inside finance. In fintech specifically, that capability has grown well beyond its original use as logging infrastructure into a platform supporting a much wider range of jobs.

Detecting fraud, speeding up how banks move transactions, or simply helping a system find the right piece of information fast — van de Velde frames all of it as variations on the same underlying stack, one Elastic deploys repeatedly to make customers' operations run better. That flexible, toolbox-style approach is what has let the company expand into so many different use cases, often quietly, without customers fully realising the extent of what the platform can do.

Looking out from Money20/20, van de Velde flags two themes he sees defining the moment for Elastic: agentic AI and digital sovereignty — with the company positioned somewhere between the two. His take is that combining technologies to get a better result is exactly where Elastic wants to sit, describing the company's role as a kind of context layer that surfaces the right answers so large language models can act on them effectively.

Sovereignty and deployment flexibility

The second major shift van de Velde points to is the growing pull toward data sovereignty and hybrid infrastructure setups.

As regulators sharpen their scrutiny, financial institutions are increasingly re-examining cloud-only approaches. The ability to deploy flexibly — across cloud, on-premise, or a mix of both — has become critical for firms trying to innovate while staying compliant, especially in Europe as data residency rules tighten. Because Elastic can operate across all of these environments, van de Velde positions it as a neutral layer sitting underneath increasingly fragmented infrastructure strategies — something particularly relevant in financial services, where the company's three pillars of search, observability and security tend to converge.

On the security side, he says Elastic has deliberately tried to strip out commercial and technical friction: pricing and access aren't gated by ingestion volume, endpoint count or user numbers, but simply by the compute a customer needs. In observability, AI is starting to enable faster incident response and, in some cases, remediation that happens without human intervention. And in fraud and anti-money-laundering work, Elastic is testing new ways to make large-language-model-driven decisions explainable — building the kind of evidentiary trail that would hold up if a case ever reached a courtroom, where a lawyer would need the underlying factual search results to back up the story being told.

Traditional search still wins on speed and cost — the future is AI and search working the problem together, not one replacing the other. — On why search infrastructure still matters in an AI-first world

Balancing AI with traditional systems

Despite how quickly AI has been adopted across the industry, van de Velde warns against leaning on it by default.

His framing is simple: just because a task can be handed to an AI system doesn't mean it should be. For classic information-retrieval jobs — straightforward searches and similar tasks — he argues traditional infrastructure remains considerably faster, cheaper and more reliable than an AI-driven approach. That's why Elastic is pushing a hybrid model that pairs AI's reasoning strength with the consistency of search, rather than treating the two as competitors. In his view, that combination — using search to ground and augment AI rather than replace it — is the pattern the industry will increasingly settle into.

Building awareness in financial services

Even with all of this underlying scale, van de Velde says Elastic's biggest ongoing challenge is simply being seen.

Despite powering a wide range of fintech applications behind the scenes, the brand often goes unnoticed by the very institutions that depend on it. He notes that one of the most common reactions Elastic hears — even from its largest customers — is surprise that the platform could handle a particular capability they didn't know it offered.

That's part of why events like Money20/20 matter to the company: they're an opportunity to build recognition and make the breadth of the platform more visible. As fintech infrastructure grows more complex — a trend underscored by the wave of announcements coming out of the event — Elastic is betting that its mix of search, AI integration and deployment flexibility will keep becoming more essential, even if most of that work continues to happen where nobody's looking.