Helping banks like Nordea
power financial research with

Helping banks like Nordea power financial research with

trusted, governed intelligence

Bigdata by RavenPack gives banking and asset management teams a single, structured intelligence layer, combining premium content providers, automated research workflows, and seamless integration with the AI tools your teams already use.

Trusted data

Streamlined research

Connects directly via MCP

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Powering the world’s best teams

|THE CHALLENGE

The challenge for banks moving from centralised AI to team-level workflows.

Large financial institutions are increasingly moving away from centralised AI programmes toward a model where individual business units build their own workflows with support from internal technical teams. The opportunity is real, but so is the risk: fragmented data sourcing, inconsistent quality controls, and the challenge of ensuring that AI-generated insights meet the governance and trust requirements of a regulated institution. The question is not whether AI will transform financial research. It is how to give each business unit the right intelligence foundation to make it work reliably from day one.

|THE APPROACH

One intelligence layer built for financial institutions.

Premium financial content

Financial Times, The Economist, Washington Post, FactSet (including StreetAccount), Finwire, and Quartr — clearly structured by provider and dataset so your teams know exactly what they are working with.

Governance and trust built in

Every response is grounded in a curated, source-ranked content universe. Quality controls are built into the retrieval and ranking layer — the same infrastructure that would take years to replicate in-house.

Flexible architecture

Bigdata connects to your existing AI environment programmatically via API or via MCP, directly powering the Claude and Copilot workflows your teams already rely on, without requiring a centralised AI strategy to be in place first.

|AGENT SWARM

Automated research workflows, built on a structured intelligence foundation.

Agent Swarm demonstrates how multi-step, AI-powered research workflows operate on a governed, source-ranked intelligence layer. From equity briefs and earnings summaries to thematic screening and sector analysis, our clients run automated research pipelines across banking and asset management teams, without sacrificing accuracy or oversight.

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Agent Swarm

Live workflow demo

|USE CASES

Where Nordea’s teams see impact first.

Banking division — research and markets teams

Automated briefings, earnings summaries, and news-driven insights across your monitored universe. Bigdata surfaces the right information at the right time — structured, sourced, and ready for analyst workflows.

Asset management — portfolio managers and research analysts

Thematic screening across thousands of sources. Identify macro themes, sector trends, and company-level signals with a single query: governed, auditable, and grounded in premium content. Purpose-built for discretionary investment teams.

AI and technology teams — business unit level

API access and MCP integration allow each business unit to build, test, and iterate its own workflows independently. Where your teams want to build, Bigdata provides the data infrastructure. Where they want a ready-made application, the Bigdata platform is available from day one.

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|YOUR STACK

Designed to work with what you already have.

Bigdata by RavenPack is not a replacement for your existing terminals or content agreements — it is the intelligence layer that sits between your data sources and your AI models, making them more useful.

Where Bloomberg and Refinitiv provide market data and terminal-based workflows, Bigdata provides an AI-ready content and retrieval layer.

It aggregates, ranks, and structures information for the analytical and research workflows your teams are building today. For institutions already working with FactSet, existing content agreements can be facilitated directly through Bigdata, including content annotation and permissioning at the user and team level.

The result

Your teams keep what works, and gain a governed, structured intelligence foundation their AI workflows can actually rely on.

Keep your Bloomberg and Refinitiv terminals — Bigdata adds the AI-ready layer on top.

FactSet content agreements can be facilitated directly through Bigdata, with user and team-level permissioning.

|SECURITY

Built for the requirements of regulated financial institutions.

Bigdata by RavenPack is SOC 2 and ISO compliant. Client data — including watchlists, uploaded content, and query history — is not visible to RavenPack. Infrastructure runs on AWS, consistent with the direction many leading financial institutions are taking for their own internal research and data environments.

SOC 2 compliant

ISO compliant

SOC 2 & ISO compliant

Independently audited controls across security, availability, and confidentiality — the standards regulated institutions expect.

European data localisation

For institutions with data residency requirements, European data localisation is a point we are ready to address directly in conversation.

|NEXT STEP

The next step is a working conversation.

We have been engaged with Nordea’s AI and investment banking teams and understand the direction you are moving in — toward business unit-led AI adoption, governed data sourcing, and workflows that serve analysts and portfolio managers directly. We would like to show you what that looks like in practice: the use cases most relevant to your banking and asset management divisions, the content and integrations that map to your stack, and the governance controls that meet your requirements. We can go from there.

This document has been prepared exclusively for Nordea (“Recipient”) by RavenPack International S.L.U. and is intended solely for Recipient’s internal evaluation purposes. It is not authorised for distribution to, or use by, any third party. This document does not constitute legal, financial, investment, or regulatory advice; recipients should seek independent professional advice before relying on its contents. RavenPack International S.L.U. expressly disclaims any responsibility or liability to any third party who may obtain, read, rely upon, or act on the basis of its contents. Unauthorised reproduction, distribution, or disclosure, in whole or in part, is strictly prohibited. The analysis, projections, and scenarios contained herein are hypothetical and illustrative only and do not constitute a case study, testimonial, performance guarantee, or representation of actual results. All third-party company names, trademarks, and logos are the property of their respective owners and are referenced for identification purposes only; no endorsement, affiliation, or sponsorship is intended or implied.