Money20/20: The Role Elastic Plays in Fintech Infrastructure

Elastic is a foundational layer for financial services firms navigating the dual pressures of scale and complexity. 

Speaking with FinTech Magazine at Money20/20 Europe in Amsterdam, Arno van de Velde, Principal Solution Architect at Elastic, outlined how the company’s search-led architecture is increasingly underpinning everything from fraud detection to AI-driven decision-making across fintech.

A background platform hiding in plain sight

Elastic’s technology is widely deployed across industries, but its role in financial services is often less visible. As Arno explains, the company’s strength lies in its flexibility.

“We’re a search company doing basically everything for search, observability and security across the globe, across all kinds of sectors and doing a lot for finance,” Arno says.

In fintech, that capability has evolved from core logging infrastructure into a broader platform supporting diverse use cases.

He adds: “So whether it’s detecting fraud, helping banks move transactions faster or searching for information or getting a similar result – those are all part of the same stack that we can deploy and have with customers to make their lives better.”

This toolbox approach has enabled Elastic to scale across use cases, often without customers realising the full extent of its capabilities.

Speaking from Money20/20, Arno says that two themes in particular stick out for him and Elastic in relation to its business: agentic AI and digital sovereignty. Elastic’s positioning sits between both.

“One thing is for sure,” Arno says, “combining technologies to reach a better outcome is a good place for us. 

“We see ourselves as a context engineering layer – finding those right answers so that LLMs can do cool things.”

The company has leaned into this role with recent product developments, including the launch of Agent Builder, designed to simplify how organisations connect data to large language models with appropriate guardrails.

But Arno is clear that AI performance hinges on data quality.

Elastic’s architecture enables organisations to unify structured and unstructured data, improving the reliability of AI outputs while maintaining control over deployment.

“We always said ‘garbage in, garbage out’ – and now it is ‘garbage in, disaster out’, because the magnitude of impact is bigger,” Arno stresses.

Sovereignty and deployment flexibility

A second major trend is the shift toward data sovereignty and hybrid infrastructure.

As regulatory scrutiny intensifies, financial institutions are reassessing cloud-only strategies.

The flexibility provided by cloud infrastructure is proving critical for firms balancing innovation with compliance, particularly in Europe where data residency requirements are tightening.

Elastic’s ability to operate across environments positions it as a neutral layer in increasingly fragmented infrastructure strategies.

This is particularly prevalent in financial services, where Elastic’s three core pillars – search, observability and security – come together.

On the security side, the company is focused on removing commercial and technical constraints.

Arno says: “Whether it's ingestion, number of endpoints, or number of users, we don't particularly care – it's just the compute power you need.”

In observability, the rise of AI is enabling faster incident resolution and even autonomous remediation.

And in fraud and anti-money laundering, Elastic is exploring new approaches to explainability using large language models.

“In a case in court, the lawyer will say: this is the story, show me the evidence. So the evidence involves those factual searches that we do,” Arno says.

Balancing AI with traditional systems

Despite the rapid adoption of AI, Arno cautions against overreliance.

He asks: “You can – but the question is, should you?”

He emphasises that traditional search infrastructure remains critical for speed, cost-efficiency and determinism.

Arno continues: “If you look at traditional information gathering – searches and that type of thing – that’s where the traditional components are way faster, way better and cheaper.”

In light of this, Elastic advocates a hybrid model, combining AI’s reasoning capabilities with the reliability of search.

“This is going to be the pattern we see going forward,” Arno predicts, “using search to augment AI and combining the two.”

Building awareness in financial services

A key challenge for Elastic, Arno says, is still visibility. 

Despite powering numerous fintech applications, the brand is often “under the hood”.

“It’s one of the biggest things we hear from many, even our biggest customers: ‘We didn't know you could also do that particular capability,” Arno explains.

Events like Money20/20 are therefore central to building mindshare and surfacing the breadth of its platform.

And, as fintech infrastructure becomes more complex – as announcements from Money20/20 highlight – Elastic is betting that its combination of search, AI integration and deployment flexibility will become increasingly indispensable, even if it remains largely unseen.