How financial services companies are building contextual intelligence at scale

Most financial institutions already have plenty of data. What they lack is shared context.

Customer data lives in one system, and transaction data lives in another. Logs, alerts, communications, documents, and signals live somewhere else entirely. Traditional approaches — data lakes, warehouses, and even many AI initiatives — still assume that insight is something you retrieve after the fact.

McKinsey’s framing challenges that assumption. In a data-ubiquitous enterprise, intelligence is always on and always in motion and embedded into:

• Fraud detection engines

• Payment flows

• Compliance monitoring

• Customer interactions

• Site reliability and operational resilience

This is where Elastic’s role becomes distinct.

Retrieval augmented generation (RAG) has become shorthand for “enterprise AI.” But in financial services, RAG alone is insufficient — and sometimes dangerous — when context is incomplete, stale, or fragmented.

Elastic’s approach is better described as contextual or semantic search at scale.

Instead of treating retrieval as a one-time lookup, Elastic continuously indexes and correlates structured, unstructured, and streaming data like transactions, logs, alerts, documents, and communications into a unified, real-time context layer.

This matters because:

• Fraud decisions require temporal context, not static documents

• Compliance investigations depend on relationships, not isolated records

• Customer experiences hinge on what just happened, not what was true yesterday

Financial institutions using Elastic for fraud detection, for example, don’t just retrieve past transactions. They continuously correlate behavioral patterns, signals, and anomalies as they emerge, enabling automated responses that adapt in real time — exactly the kind of embedded intelligence McKinsey describes.

Leading financial institutions are already operating this way:

• Global banks use Elastic to unify payments data, logs, and customer interactions, enabling fraud teams to detect anomalies faster while reducing false positives not by adding models but by improving context.

• Insurance providers use Elastic to correlate claims data, policy documents, and communications, accelerating investigations while maintaining auditability.

• Digital payment platforms rely on Elastic’s real-time analytics to monitor system health and transaction integrity simultaneously, ensuring uptime, trust, and regulatory confidence.

What these organizations share is not a single use case but a shared data foundation that supports many.

Data ubiquity cannot be achieved through batch processing or delayed analytics. It requires streams.

Streams from Elastic enables institutions to treat data as a living system — continuously ingested, indexed, and analyzed as it flows through the enterprise. This allows decision points to respond instantly whether that decision is:

• Blocking a fraudulent transaction

• Triggering a compliance review

• Adjusting a customer experience in real time

• Alerting operations teams before an outage impacts customers

In McKinsey’s terms, this is the difference between data availability and data embeddedness. Streams ensure intelligence is present at the moment of action, not afterward.

McKinsey is explicit: Automation must come with sufficient human oversight. In financial services, that’s non-negotiable.

Elastic Agent Builder enables organizations to create domain-specific AI agents that operate within guardrails that are grounded in real data, constrained by policy, and observable by design.

Rather than generic copilots, financial institutions are building:

• Fraud investigation agents that surface evidence, not guesses

• Compliance agents that explain why an alert was triggered

• Operations agents that correlate signals across systems before recommending action

These agents don’t replace human judgment; they elevate it, ensuring automation remains transparent, explainable, and accountable.

McKinsey argues that winners in 2030 will be those who build reusable, enterprise-wide data capabilities instead of isolated AI projects. Elastic aligns directly with that vision.

By serving as a real-time, contextual intelligence layer across security, observability, search, and AI-driven applications, Elastic enables financial institutions to:

• Scale AI safely

• Reduce decision latency

• Improve customer trust

• Meet regulatory demands without slowing innovation

Data ubiquity isn’t about having more tools. It’s about having one source of truth that moves as fast as your business does.

For financial services leaders, the question is no longer whether this future is coming but whether your data architecture is ready to support it.

Get in touch to learn more about how Elastic can support your data ubiquity journey.