DEFEND WEBINAR | Agentic AI in Action: The Playbook from Best-Performing Fraud and Risk Teams
Expose the network behind the transaction
Connect identities, accounts, devices, IPs, payment flows, and transactions to reveal relationships that isolated alerts cannot show. DataVisor Knowledge Graph helps teams uncover fraud rings, mule networks, shared infrastructure, and hidden risk across multiple hops.
Turn relationships into detection, not just visualization
Connect entities, follow the links across hops, surface the network, and bring the resulting intelligence into real-time decisions.
Build the relationship layer behind risk
Represent identities, accounts, transactions, devices, IPs, emails, phone numbers, addresses, bank accounts, and other relevant entities in a connected graph.
Move beyond the first suspicious account
Explore binding, shared-value, contact, and transaction relationships across multiple hops to understand how activity is connected.
Surface coordinated fraud and mule activity
Use link and network analysis to identify clusters, shared infrastructure, and suspicious relationships across accounts that may appear normal individually.
Turn relationships into real-time risk context
Use graph-derived intelligence in investigations and, where configured, as features or signals that strengthen real-time detection and decisioning.
Start with one entity. Reveal the network.
Show an actual graph expanding from a suspicious account into linked devices, identities, payment relationships, and second-hop connections, then open the related investigation.
account
devices
cluster
Build relationships as activity happens
Create entities from incoming data.
Establish configured relationships and transaction edges.
Update the graph as new activity arrives.
Analyze multi-hop connections and suspicious clusters.
Use the resulting intelligence in investigations and risk strategies.
Make relationships part of detection
DataVisor connects Knowledge Graph to machine learning, device intelligence, rules, and real-time decisioning so relationships can influence how risk is detected and investigated, not simply how it is displayed.
Investigate coordinated risk across the network
Use Knowledge Graph for fraud rings, mule networks, account takeover, payment fraud, shared-device activity, collusion, and other schemes that depend on relationships between entities.
Keep relationships current and explainable
Detail supported graph scale, real-time update behavior, relationship configuration, multi-hop analysis, automated clustering/community detection, and explainability of graph-derived risk.
- Real-time graph updates as activity arrives
- Configurable relationship and entity types
- Multi-hop analysis at production scale
- Automated clustering and community detection
- Explainability of graph-derived risk signals
Common questions
What entities can DataVisor Knowledge Graph connect?
DataVisor can represent identities, accounts, transactions, devices, IPs, emails, phone numbers, addresses, bank accounts, payment flows, and other configured entities.
Can DataVisor identify fraud rings and mule networks?
Yes. Multi-hop linkage and network analysis can reveal shared infrastructure and connected behavior across accounts, helping investigators identify coordinated networks.
Is Knowledge Graph only for investigations?
Knowledge Graph supports investigation and link analysis, and graph-derived intelligence can also strengthen detection and decisioning where configured.
See how one suspicious entity becomes a network
Walk through a live graph investigation and see how DataVisor surfaces the relationships behind coordinated fraud.