DEFEND WEBINAR | Agentic AI in Action: The Playbook from Best-Performing Fraud and Risk Teams
Detect the attack before there is a rule for it
Identify previously unseen fraud patterns before historical labels or explicit controls exist. DataVisor combines patented Unsupervised Machine Learning with behavioral, device, graph, and real-time intelligence to surface emerging attacks while there is still time to intervene.
Find the gap before known fraud
Start with the anomaly, connect the surrounding signals, reveal the cluster, and turn discovery into a repeatable control.
Surface behavior that does not fit the population
Use unsupervised analysis to identify abnormal activity without requiring a known fraud signature or labeled example.
Build context around the first suspicious event
Correlate behavioral, device, transaction, identity, velocity, and network signals to understand whether the anomaly is isolated or part of a coordinated attack.
Reveal related entities as the attack develops
Use clustering and Knowledge Graph linkage analysis to connect events, accounts, devices, and infrastructure that may look unrelated in isolation.
Move from first sighting to repeatable protection
Investigate the emerging pattern and operationalize the intelligence through supported strategies, rules, features, workflows, or decisioning controls.
Show the attack taking shape
Use a cluster-evolution visualization to show the first anomalous event, the emerging group, linked entities, supporting risk signals, and the resulting investigation or control.
correlation
Detect risk before labels catch up
Surface abnormal behavior through unsupervised analysis.
Correlate related signals and events.
Identify linked entities and emerging clusters.
Assess risk without waiting for confirmed fraud labels.
Investigate and convert the new intelligence into protection.
Find what rules and labeled models have not learned yet
DataVisor's zero-day approach uses unsupervised detection to identify abnormal behavior before an explicit rule or historical fraud label exists, then strengthens that signal with graph, device, behavioral, and real-time context.
Protect against fast-evolving fraud
Use zero-day detection for coordinated card or BIN attacks, new fraud-ring behavior, evolving account takeover, mule activity, and other polymorphic or AI-enabled attacks where the pattern changes faster than conventional controls.
Separate meaningful change from legitimate novelty
Explain the approved peer-group, score-distribution, linked-entity, and analyst context used to evaluate anomalies, along with the controls for feedback, tuning, and promoting new intelligence into future detection.
- Peer-group and score-distribution context
- Linked-entity context for evaluating anomalies
- Analyst feedback and tuning controls
- Workflow for promoting new intelligence into detection
Common questions
Can DataVisor detect fraud without historical labels?
Yes. DataVisor's Unsupervised Machine Learning is designed to identify abnormal behavior without requiring prior labeled fraud examples.
How does DataVisor control false positives in zero-day detection?
DataVisor evaluates anomalies with broader behavioral, device, transaction, and network context so risk is assessed using multiple dimensions rather than a single unusual signal.
What happens after DataVisor discovers a new attack pattern?
The pattern can be investigated and, through supported workflows, translated into strategies, rules, features, or other controls that make future instances easier to identify and stop.
Walk through zero-day fraud from anomaly to action
Explore how DataVisor surfaces an unknown pattern, connects the network, explains the risk, and helps teams turn the discovery into protection.