Graph analytics, behavioral signals, machine learning, and investigation workflows. Designed around the relationships behind modern fraud.
Book a meetingProduct concept shown with synthetic data. Current capability and deployment scope are confirmed during evaluation.
Bring accounts, devices, identities, payments, and counterparties into a common graph. Make shared infrastructure and indirect connections visible.
Combine graph patterns with behavior, machine learning, and explicit business policies. Design decisions around the requirements of each workflow.
Investigate whether apparently separate accounts represent related actors. Preserve the distinction between a shared signal and a confirmed identity.
Explore the network surrounding an alert, review event history, and assemble the evidence behind a decision. AI-assisted summaries support your analysts.
A risk score is a starting point. Your policy determines the action, and your team keeps control of consequential decisions.
Illustrative decision outcomes. A signal or score does not establish that an entity has committed fraud.
GraphShield is a graph-powered fraud intelligence platform in development. It is designed to connect accounts, devices, identities, transactions, and payment signals so risk teams can understand coordinated fraud.
Fraud actors often reuse devices, payment instruments, identities, and counterparties. Graph analysis makes those relationships visible, revealing suspicious networks that isolated event scores can miss.
The platform is designed to complement an existing fraud stack or act as a connected intelligence layer. During a meeting, we can discuss the data, decision points, and investigation workflows that fit your environment.
The intended architecture supports event-based risk scoring for account creation, login, payments, transfers, and payouts. Availability, latency requirements, and integration scope are established during technical evaluation.
The product is designed to surface contributing signals, related entities, graph paths, behavioral changes, and rule triggers. AI-assisted explanations support analyst judgment and should be verified against source evidence.
We discuss your fraud challenges, data sources, and current workflows, then explore a relevant product walkthrough and evaluation scope. There is no account to create and no payment required to request a conversation.
Bring your risk challenge. Let’s map a path forward.