Knowledge Graph & Link Analysis

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.

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Fraud & Risk Platform/Knowledge Graph
Solution Pillars

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.

Connect the entities

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.

Identities · accounts · devices · payments
Follow the links

Move beyond the first suspicious account

Explore binding, shared-value, contact, and transaction relationships across multiple hops to understand how activity is connected.

Multi-hop relationship analysis
Find the network

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.

Clusters · shared infrastructure
Bring graph intelligence into the decision

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.

Graph signals feed detection & decisions
Product Proof

Start with one entity. Reveal the network.

A graph expands from a suspicious account into linked devices, identities, payment relationships, and second-hop connections — surfacing the related investigation in the same view.

Suspicious
account
→
Linked
devices
→
Network
cluster
→
Investigation
From one suspicious account to a full network — linked devices, identities, and payment flows, all in one connected view.
How It Works

Build relationships as activity happens

1

Create entities from incoming data.

2

Establish configured relationships and transaction edges.

3

Update the graph as new activity arrives.

4

Analyze multi-hop connections and suspicious clusters.

5

Use the resulting intelligence in investigations and risk strategies.

Graph intelligence that does more than visualize

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.

Find what single-entity controls miss

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.

Fraud Rings Mule Networks Account Takeover Shared-Device Activity Collusion
Make the network pay off

Quantify detection and review efficiency

Faster investigations
Linked-entity context cuts down the manual cross-referencing that slows fraud-ring and mule-network reviews.
Deeper visibility
Multi-hop link analysis surfaces coordinated networks that isolated, single-account alerts cannot show.

DataVisor Knowledge Graph brings connected-account, device, and payment context into every investigation, helping teams resolve fraud-ring and mule-network cases that isolated alerts alone cannot explain.

Customer Proof

Link analysis that changes the investigation

Published Case Study

A loan provider using DataVisor link analysis reported a 20% increase in fraud detection, connecting accounts, devices, and payment flows to catch coordinated activity that isolated alerts missed.

Connected Platform

Give the graph richer inputs and more ways to act

Connect dEdge, Identity & Risk Signals, Data Orchestration, supervised and unsupervised ML, Real-Time Decisioning, Fraud Pattern Intelligence, and Case Management.

Device & Behavioral Intelligence — dEdge → Identity & Risk Signals → Real-Time Data Orchestration → Supervised Machine Learning → Unsupervised Machine Learning → Real-Time Decisioning → Fraud Pattern Intelligence → Investigations & Case Management →
Graph at production scale

Keep relationships current and explainable

Knowledge Graph scales to production data volumes, updates relationships in real time as new activity arrives, and applies configurable, multi-hop link analysis that investigators can review and explain.

  • Real-time graph updates as activity arrives
  • Configurable relationship and entity types
  • Multi-hop analysis at production scale
  • Graph intelligence integrated into real-time decisioning
  • Explainability of graph-derived risk signals
FAQ

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.

Follow the connections

See how one suspicious entity becomes a network

Walk through a live graph investigation and see how DataVisor surfaces the relationships behind coordinated fraud.

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