The main blockers to fraud prevention

Fraud teams rarely suffer from a lack of tools; in fact, most institutions have accumulated too many.

Transaction monitoring platforms, device fingerprinting solutions, behavioural biometrics, AML systems, KYC verification engines, card-fraud tools, cyber telemetry feeds and dispute-management systems all coexist in the same organisation, yet almost none of them share data naturally.

The real cost of fragmented data

Fraud practitioners know the day-to-day pain this creates. Alerts produced in one system must be contextually validated using information that often sits somewhere else entirely.

A suspicious device ID may correlate with a behavioural anomaly, but if those datasets never meet, the link is lost. A synthetic identity may look weak in one system but, when connected across channels, would show a clear fraud signature. These signals simply don’t surface when data exists in isolation.

Fraud thrives in disconnected data. Cyber risk spikes when controls fail, and fraud when visibility does.

Even the most advanced machine-learning models cannot compensate for siloed data. A model is only as good as the data it can reach. When that data is spread across different schemas, time-series formats and access permissions, fraud teams are left with partial visibility. And partial visibility always produces partial outcomes:

• More false positives
• Slower investigations
• Missed fraud clusters
• Higher operational cost

A ‘one search’ AI data lake

You don’t fix fraud by adding another tool; you fix it by connecting the ones you already have.

Elastic’s approach centres on a unified Search AI data lake that ingests every fraud-relevant source, regardless of structure, volume or origin. Structured, semi-structured and unstructured data is normalised and made instantly searchable.

By correlating signals across time, geography, attributes, identity and behaviour, fraud teams finally gain a single operational view of risk - the first prerequisite for meaningful detection.

When connected, everything speeds up

This unified view fundamentally changes the economics of fraud detection.

• Analysts no longer spend hours manually stitching evidence together.
• False positives drop because context is available at the point of decision.
• Invisible fraud rings suddenly become obvious because accounts, devices, payments and behavioural signatures now sit in one place.

Silo-breaking is not about technology modernisation; it’s about operational liberation.

Cross-functional intelligence becomes natural

Fraud rarely stays within fraud. Mule accounts signal AML exposure, compromised identities point toward cyber breach, anomalous access patterns might indicate an insider threat.

A composable data architecture allows all adjacent teams - fraud, AML, cyber, identity - to work from the same data foundation, each running its own logic without losing the collective picture.

This is what modern financial-crime defence looks like: connected, contextual, and collaborative.

Fraud isn’t difficult to understand; it’s hard to see

Fraud detection doesn’t fail because of a lack of intelligence; it fails because the intelligence exists in too many places. Break the silos, and everything accelerates: Detection. Investigation. Response. Prevention. Strategic foresight.

Or, put simply: Fraud thrives in disconnected data. Cyber risk spikes when controls fail, and fraud when visibility does.