How does graph analytics help with fraud detection?

Graph analytics detects fraud by mapping entities—accounts, transactions, identities—as connected relationships instead of isolated records, making coordinated schemes visible. Kineviz GraphXR applies this with a visualization-first approach: analysts explore relationships in multi-dimensional graph views, use interactive filtering to remove noise, and trace AI-derived insights back to source data — the AI augments the analyst's field of view; the analyst still draws the conclusions — producing conclusions that are explainable, auditable, and evidence-backed rather than black-box outputs. When a case needs outside context, SeekerXR brings in open-source intelligence from 80+ OSINT and proprietary sources.

Why use a graph database for fraud detection instead of traditional tools?

Graph databases store data as entities and relationships, so multi-hop connections that reveal fraud rings stay intact instead of being flattened into rows and joins. Kineviz builds on this with a connection-driven data model: entities, links, and inferred connections form a navigable system that both AI and human analysts can reason over, making it faster to uncover hidden patterns and validate findings.

What is link analysis in fraud investigation?

Link analysis is the technique of visualizing connections between people, accounts, and events to expose relationships that indicate coordinated fraud. In Kineviz, analysts perform link analysis in an interactive graph environment with no-code Cypher querying, exploring how signals connect across the graph, refining hypotheses in real time, and moving from suspicious signals to evidence-backed conclusions.

How do investigators detect fraud rings and collusion with network visualization?

Investigators detect fraud rings by visualizing shared attributes and connections that link seemingly unrelated actors into one coordinated network — and Kineviz's built-in community detection, centrality, and shortest-path algorithms surface those clusters automatically. A real-world example from Kineviz: a horse racing organization's trust and integrity team uses the platform to detect and respond to collusion in sports betting, turning a difficult, time-consuming process into faster, more intuitive investigations.

Can AI be used for fraud detection, and how do you trust its output?

Yes—and the right role for AI is amplifying investigators, not replacing them: it accelerates fraud detection, but trustworthy results require transparency, not black-box scores. Kineviz turns structured and unstructured data into a connected system AI can reason over; AI-generated structure surfaces what matters, while analysts visually explore the relationships behind each insight and trace them to source data, keeping conclusions traceable, explainable, auditable, and ready for decisions.

What does a fraud analyst's investigation workflow look like in a graph tool?

A graph-based fraud workflow is iterative: connect data, visualize relationships, filter noise, test hypotheses, and document evidence. Kineviz supports this with intelligence-based workflows that are iterative and customizable—analysts combine intuitive interfaces with multi-dimensional graph visualization and interactive filtering, accelerating iteration from initial signals to validated, decision-ready findings.

Do fraud analysts need to know a query language to use graph visualization software?

No—Kineviz supports no-code Cypher querying, so analysts can query the graph without writing query-language syntax. Combined with seamless data integration and visual exploration, this lets fraud teams see how AI-derived insights connect across the graph and drill into relationships directly, rather than depending on engineers to translate every investigative question into code.

A cyber-threat investigation on a dark globe in Kineviz — attack streams converging on the United States and Canada

✦ Kineviz Agent

The agent for people who can’t take AI at its word.

Every observation the Kineviz agent makes stays connected to the source data behind it, so your analysts can check the work themselves.

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Validation

Trace every finding to its source.

Each observation the agent makes is a claim about your data, and every claim arrives with the records that support it. Select a finding and see exactly which nodes, edges, and fields it drew from.

Your analysts review the evidence, not just the summary. They can confirm a finding, challenge it, or throw it out — with the source data right in front of them.

Evidentiary standard

Built for work that has to hold up.

Law enforcement, fraud investigation, intelligence, compliance. If you can’t defend it in front of a judge, you can’t use it. The trail from conclusion back to source has to be complete.

That’s the standard the agent is designed around. Every step it takes is recorded, and every conclusion is traceable.

What it does

Analysis and applications, in plain language.

Conversational analysis

Describe what you want to see. The agent runs the analysis, builds the model, and draws the visualization in your graph — filtering, community detection, time-series charts, whatever the investigation needs.

You: Show me every account within two hops of the flagged wallet, colored by transaction volume.

Application development

The agent builds inside Kineviz, too. Custom interfaces, dashboards, workflows, and reports — tools shaped to your team’s investigation, ready to use, no engineering ticket required.

You: Build a triage dashboard for new alerts, with a one-click case report.

See what your data has been trying to tell you.

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