DEFEND WEBINAR | Agentic AI in Action: The Playbook from Best-Performing Fraud and Risk Teams
Find the fraud you have never seen before
Detect anomalous, coordinated, and emerging behavior without waiting for historical fraud labels. DataVisor's patented Unsupervised Machine Learning learns from the population itself to surface patterns that rules and labeled models have not been taught to recognize.
Learn the pattern from the population itself
Identify abnormal and coordinated behavior without waiting for labels, then connect it to real-time decisions, investigations, and strategy.
Start finding risk before the first confirmed case
Identify abnormal behavior without requiring a library of known fraud examples, giving teams earlier visibility into new attacks and changing tactics.
See the pattern across the population
Analyze peer groups, behavioral dimensions, clusters, velocity, and connected activity to reveal coordinated behavior that can look normal when each event is viewed alone.
Keep detection aligned with a moving environment
Use behavioral baselines and population-level analysis to identify meaningful deviations as legitimate and fraudulent activity evolves.
Connect model output to the rest of the risk stack
Use unsupervised risk scores and patterns in real-time decisions, investigations, graph analysis, rules, and downstream strategies.
Show the unknown becoming visible
Use an anomaly distribution, peer-group or cluster visualization, and a real emerging-pattern example to show how previously unseen behavior separates from the population.
signals
& baselines
detected
intelligence
Learn the pattern from the data itself
Ingest behavioral, transaction, device, identity, and relationship features.
Establish peer groups, baselines, and multidimensional patterns.
Identify abnormal or coordinated behavior.
Produce risk intelligence for decisions and investigations.
Use confirmed outcomes to strengthen future detection where supported.
Put unsupervised intelligence into live decisions
DataVisor combines patented unsupervised ML with supervised models, rules, graph intelligence, and device signals so emerging-threat detection can influence production decisions in real time.
Detect attacks that change faster than training data
Apply unsupervised ML to zero-day fraud, fraud rings, mule activity, account takeover, promotions abuse, payment fraud, and other attacks where historical labels provide an incomplete picture.
Give analysts context behind the score
Detail approved anomaly explanations, peer-group context, cluster views, adaptation behavior, controls for legitimate behavioral change, and the workflow for converting discoveries into strategies.
- Anomaly and peer-group explanations
- Cluster and pattern visualization
- Adaptation controls for legitimate behavior change
- Workflow for promoting discoveries into strategies
Common questions
Does unsupervised machine learning require labeled fraud data?
No. DataVisor's unsupervised approach is designed to identify abnormal and coordinated behavior without requiring historical fraud labels.
How does unsupervised ML reduce false positives?
By evaluating behavior in context — such as peer-group patterns, multiple signals, and relationships — unsupervised analysis can distinguish meaningful anomalies from simple one-dimensional thresholds.
Can unsupervised ML work with rules and supervised models?
Yes. DataVisor combines unsupervised intelligence with other detection layers so teams can use known-pattern controls and emerging-pattern detection together.
Watch emerging fraud become detectable
Walk through how DataVisor identifies an anomalous population, connects the pattern, and turns the intelligence into action.