DEFEND WEBINAR | Agentic AI in Action: The Playbook from Best-Performing Fraud and Risk Teams
See the attack taking shape before it becomes a loss trend
Identify known, emerging, coordinated, and previously unseen fraud patterns across connected entities and signals. DataVisor helps teams move from isolated events to the pattern behind them — then turn that intelligence into detection and action.
Cover the patterns you know — and the ones you don't yet
Detect established typologies, surface emerging behavior, connect the actors behind it, and operationalize what you find.
Detect established fraud typologies across channels
Apply intelligence to account takeover, application fraud, payment and card fraud, ACH/wire, scams, mule activity, fraud rings, check fraud, promotions abuse, and applicable AML monitoring patterns.
Surface emerging behavior without waiting for labels
Use Unsupervised Machine Learning to identify anomalous and coordinated activity that has not yet been captured in historical fraud labels or explicit rules.
Reveal coordination across seemingly separate events
Use Knowledge Graph and network analysis to connect shared devices, identities, accounts, timing, beneficiaries, transaction flows, and other relationships behind a pattern.
Operationalize the pattern
Move discovered intelligence into alerts, cases, rules, features, monitoring strategies, and real-time decisioning controls according to the supported workflow.
Make the pattern visible
Show an attack cluster or network visualization, the supporting signals behind it, and the workflow that takes the pattern from discovery into investigation or strategy.
cluster
From anomalous activity to an actionable pattern
Analyze behavioral, device, identity, transaction, historical, and relationship signals.
Detect anomalies, peer-group deviations, velocity, and coordinated behavior.
Identify the affected entities or network.
Investigate the pattern and supporting evidence.
Operationalize the intelligence into controls and decisions.
Find coordination that single-method controls miss
DataVisor combines patented Unsupervised Machine Learning, Knowledge Graph, behavioral and device intelligence, rules, and supervised ML to analyze patterns across entities and channels.
Expose the common thread
Anchor the page in a coordinated attack where individual accounts appear legitimate but shared devices, identities, timing, beneficiaries, or transaction flows reveal the network.
Give teams control over what happens after discovery
Detail the approved pattern library, customer-specific pattern configuration, Vera-assisted rule/feature creation, recommendation explainability, and workflow for promoting a discovered pattern into production controls.
- Approved pattern library across typologies
- Customer-specific pattern configuration
- Vera-assisted rule and feature creation
- Recommendation explainability
- Workflow for promoting patterns into production
Common questions
Can DataVisor detect new fraud patterns without historical labels?
Yes. DataVisor's Unsupervised Machine Learning is designed to identify anomalous and coordinated behavior without requiring labeled fraud history.
Does Fraud Pattern Intelligence only detect known typologies?
No. DataVisor combines known-pattern controls with unsupervised and network intelligence designed to surface emerging and coordinated behavior.
How does a discovered pattern become a fraud strategy?
Discovered intelligence can be investigated and operationalized through supported rules, features, alerts, cases, monitoring strategies, and decisioning controls.
From cluster to control
See DataVisor reveal an anomalous network, explain the supporting signals, and turn the discovery into an actionable fraud strategy.