ElasticON London: Inside Elastic and AWS' Partnership

Organisations in the financial services industry typically operate with very distinct silos of data, with many legacy banks operating platforms not designed for today’s industry and its need for flexibility, speed and intelligence.

A partnership between Elastic, the search AI company that enables FSI businesses to extract value from their unstructured data, and Amazon Web Services (AWS), helps tackle this challenge. 

Elastic’s open source search AI platform on AWS’ infrastructure enables companies in the financial services industry to eliminate data silos, deliver hyper-personalised customer experiences, manage risk more effectively and drive innovation. 

At ElasticON London 2026 – Elastic’s annual gathering of developers, architects and business leaders focused on leveraging agentic AI – Sal Gauri, Director for EMEA at Elastic, and Lisa Lewison, Head of Partnerships for Global Financial Services at AWS, spoke with FinTech Magazine. 

They elaborated on the data and AI challenges facing companies in financial services, why there is urgency to mitigate the impact, and how the partnership between their two organisations is designed precisely for this transformative moment.

Taming the data challenge

"Many of our customers are banks that have been around for a couple of hundred years. To stay in business, they have to manage people's money very securely, which often leads to legacy systems,” Lisa explains. 

This brings several key challenges: data sits in silos and customers exist as multiple disconnected identities across the same organisation. 

"Financial institutions often have multiple versions of the same customer because data sets are housed in different locations," Lisa says.

"They might see you as an insurance customer in one place and a loan applicant in another. If we look at Net Promoter Scores, being able to serve customers faster means banks are going to get better satisfaction ratings." 

A consolidated data view, she argues, goes beyond technological innovation or transformation for banks to become a commercial necessity.

Sal frames the same problem from a different angle. AI-driven financial crime is forcing firms to ingest and monitor more data than ever, pushing storage costs upward while compressing the time available to act. 

But the deeper issue, he says, is that most organisations are seeing innovation stall as a result of the data challenges they face: "A lot of companies are doing proof of concepts (POCs). These result in some kind of output, but they are not able to implement them because there is still a data problem." 

AI adoption stalls, Sal argues, when it is treated as a separate initiative rather than embedded in the tools that teams already use: "When features are in the product, companies are able to embrace them and start seeing the benefits. Proving the ROI of AI is a challenge but if it is part of a product, that is what drives it."

Unifying data for security and innovation

The platform Elastic has built addresses these problems, says Sal, describing a shift away from fragmented data warehouses and lakes toward what he calls a "search lake" - a unified layer through which all data, regardless of origin or format, can be interrogated at speed using the Elastic Common Schema. 

"We help companies find a needle in a haystack with accuracy, immediacy and the right context at a lower cost,” he explains.

“In the search lake, there are different data points connected to bring it all together and we put a layer on top of it so we can search it much quicker.” 

The impact of this shows up particularly well in fraud and crime prevention. As an example, Know Your Customer checks that once meant expensive third-party contracts and slow turnaround are being brought in-house, with response times falling from days to seconds. 

"Banks used to pay a lot of money to vendors to do that," Sal says. "Challenger banks are already getting the response back in seconds, or even microseconds." 

He explains how Electron, a South African payment processor handling 150 transactions per second on Elastic and AWS, built real-time fraud alerting on the platform and has since sold that capability to retail banking partners across the market.

How Elastic and AWS power innovation

What makes the Elastic-AWS partnership effective, say Sal and Lisa, is that security and innovation are treated as compatible rather than competing priorities. 

As an AWS Financial Services Competency Partner, and named AWS Global Generative AI Infrastructure and Data Partner of the Year in 2024, Elastic brings a proven, integrated capability to the cloud environment AWS provides. 

Deep integration with Amazon Bedrock gives developers access to high-performing foundational AI models, while the governance frameworks ensure institutions remain compliant. 

"We recognised that for customers to adopt this technology at scale, they need a secure foundation," Lisa says, pointing to how the partnership delivers model choice and flexibility, but within structures regulators can accept. 

For European institutions in particular, digital sovereignty is becoming an increasingly important concern.

AWS' European Sovereign Cloud, Lisa notes, was a direct response to regulatory pressure around data residency, a signal of how seriously both companies take compliance.

AWS works with customers through well-architected frameworks and landing zones, so that institutions can scale rapidly while maintaining a consistent security baseline. 

"It can be a challenge if customers try to retain too much of their on-premise legacy history while they move to the cloud,” she adds.

“We help them grow rapidly while maintaining that core baseline across their operations."

In practice, Sal notes, customers who adopt Elastic for one purpose consistently find it valuable for another.

Firms that deploy the platform for operational observability – monitoring system health, resolving incidents faster – discover that the same data pipeline serves their security teams, or helps them to innovate faster, for example. 

"Once the logs are in the system, those same logs can be used to investigate security issues," he says.

"Can we use it for something else? That is what we call a data mesh approach." 

The platform's three core solutions – search and Gen AI, observability and AI-driven security – are built to deliver in this way, combining to provide a powerful and comprehensive solution to meet the needs of modern financial services firms.