Link Analysis / Visualisation
Our graph-based data store and visualisation interface, powered by Amazon Neptune, represents relationships between entities in a flexible and intuitive way. This tool enables your investigation team to uncover and exploit information that would otherwise be hidden, giving you an edge in the battle against fraud.
Key Stats.
10
M+
Distinct entities resolved
15
+
Distinct entity types
20
M+
Connections between nodes
Link Analysis / Visualisation: Navigate the complexities of fraud detection
Spot collusion and scams by using network visualisation to see how entities are connected in a broader context. Navigate through these networks interactively, visualising connections over time and on a map. Export network information to other applications along with link analysis metrics such as centrality measures and the shortest path to fraud. Enrich your data with external sources, from weather reports to location details to social media profiles.
Our link analysis and visualisation tools are designed to help you navigate the complexities of fraud detection, offering powerful insights and intuitive interfaces to support your investigation efforts.
Key Benefits
Intuitive Visualisation
Represent relationships between entities in a clear and flexible way, simplifying complex connections.
Interactive Navigation
Explore networks interactively, visualising connections over time and across different locations.
Enhanced Analysis
Export network data and link analysis metrics to other applications for deeper insights.
Data Enrichment
Use our simulation module to test and compare rule sets, ensuring your fraud detection system is always optimised.
Our Detect services
We employ an array of targeted techniques in our automated fraud prediction process. These include models trained on your data using the latest machine learning methodologies.
FAQs
How network-based insights support detection and investigation.
What is link analysis (network analysis) and why does it matter?
Link analysis maps relationships between entities; people, addresses, devices, vehicles, repairers and claims to reveal organised fraud that looks legitimate when viewed one claim at a time.
How do I interpret a link analysis chart?
The visualisation highlights clusters, central nodes and unexpected connections. Investigators typically look for dense clusters, repeated shared attributes, and high-connectivity entities that may indicate coordination.
What data sources can be used for link analysis?
Claims and policy records, contact details, payment information, addresses, vehicles, supplier/repair networks and device/location signals. The more consistent your identifiers, the stronger the network resolution.
Can link analysis be used alongside scoring?
Yes. Network signals can enrich scoring and prioritisation, helping you escalate cases that are part of a wider organised ring, even if a single claim doesn’t look high risk on its own.
How do you reduce noise in network views?
We apply entity resolution, configurable matching rules and thresholds to avoid over-linking. Investigators can also filter by relationship type, time window and materiality.
What’s a typical outcome from using link analysis?
Teams identify repeat participants, collusive groups and shared infrastructure (addresses, devices, suppliers), enabling faster triage and more targeted investigations.
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