From fragmented data to a single source of truth

Most fraud teams envision a unified data environment, but few manage to achieve it.

Technology estates are complex, acquisitions add more tools, regulatory requirements create new systems, and operational realities mean nothing is ever fully “rationalised.” Yet the Fraud Data Hub, with a single operational view of risk, is no longer a luxury; it’s essential.

A modern Fraud Data Hub has several defining characteristics:

1. It ingests everything, without friction

Structured, semi-structured and unstructured data. Transactions, logs, chat transcripts, device fingerprints, behavioural analytics, identity checks, KYC data, cyber signals, geographic attributes, payment flows. Nothing sits outside the system because every data point could matter.
Elastic’s Search AI data lake is built for this principle — ingest first, index immediately, search without constraint.

2. It correlates signals automatically

Data silos create gaps. A unified hub closes them by presenting cross-channel, cross-system context in a single query. Examples include:

• Linking a suspicious transaction to a device that appeared in a prior fraudulent session
• Detecting shared attributes across synthetic identities
• Matching behavioural anomalies to historic patterns
• Identifying connections between mule accounts and new payees

3. It supports multiple fraud and financial-crime use cases

Fraud does not operate in isolation. AML, sanctions, KYC, cyber signals and identity controls must increasingly overlap. The document describes the importance of a “composable architecture” enabling teams to flex into adjacent use cases.

This is vital for multi-line institutions.

4. It accelerates investigations

With a unified data hub, analysts don’t waste time gathering context. Evidence is already assembled, relationships are visible, and investigation steps are streamlined. This directly reduces the “20× cost multiplier” seen in fraud cases.

5. It reduces false positives

Elastic’s AI-powered search capabilities help fraud teams enrich alerts and verify them across all data sources, significantly lowering the noise that overwhelms operational teams.

6. It scales as the organisation grows

Digital transaction volumes grow exponentially. A fraud data hub must scale without performance degradation. Elastic’s underlying architecture enables horizontal scaling, ensuring teams can ingest more data from more systems with greater velocity.

A unified Fraud Data Hub is not a theoretical construct; organisations are already using this model to reduce blind spots, improve risk scoring and simplify investigations. The institutions that embrace this architecture are building the foundation for the next decade of fraud defence.