A practical guide to building an anti-fraud data lake

An anti-fraud data lake is not just a storage solution; it is an operational asset.

Built correctly, it becomes the heart of detection, investigation, intelligence and regulatory reporting.

When it works as an operational layer rather than a passive repository, teams stop wasting cycles hunting for context and start acting on it. Patterns surface faster, links between identities, devices, transactions and behaviour become clearer, and analysts can move easily from real-time alerts to years of history in seconds.

Because when the same data foundation supports both detection and reporting, you reduce duplication, inconsistency and blind spots across the entire fraud lifecycle.

A strong anti-fraud data lake includes:

1. Comprehensive ingestion

Collect data across:

• Payments and transactions
• Login and authentication
• Device fingerprints
• Session telemetry
• Identity verification
• Behavioural biometrics
• AML and sanctions alerts
• Chargebacks and disputesCustomer interactions
• Cyber events

2. Normalisation and enrichment

Standardise formats, enrich entities and resolve identities so that a single customer, device or mule can be recognised across all systems.

3. Searchable schema flexibility

Elastic’s index structure allows teams to search across any data, even without predefined schema, significantly reducing engineering overhead.

4. Real-time and historical correlation

The lake must function as both a detection and an investigation engine, linking past and present behaviour.

5. Composable architecture

Fraud, AML, cyber and identity teams must access the data lake without stepping on each other’s workflows.

When all of these come together, the data lake becomes a living system — powering models, supporting investigations, and continuously improving organisational fraud intelligence.
When this foundation is in place, the value compounds quickly. Every new signal strengthens existing models, and every investigation enriches future detection. Teams stop operating in narrow lanes and start sharing a common view of risk that adapts as fraud tactics evolve.

The result is not just faster response, but a smarter organisation, one where data, context and action flow together, and where fraud controls become progressively sharper with every case handled.