DEFEND WEBINAR | Agentic AI in Action: The Playbook from Best-Performing Fraud and Risk Teams
Turn confirmed outcomes into sharper fraud decisions
Build and deploy machine learning models that learn from your labeled outcomes and apply that intelligence to real-time risk decisions. DataVisor connects model development, features, explainability, deployment, and monitoring to the same platform where strategies run.
Bring model intelligence into the decision layer
Train on your labeled outcomes, move scores directly into production strategies, and keep performance visible after deployment.
Use known fraud and good activity to sharpen detection
Train models on labeled behavior and the features that matter to your business so detection reflects the patterns present in your customers, products, and channels.
Move from score to action
Use model scores directly within real-time strategies alongside rules, unsupervised ML, graph intelligence, device signals, and other context.
Keep model and strategy intelligence aligned
Use the platform's feature layer to make relevant risk intelligence available across models and rules, reducing duplicated feature logic and inconsistent definitions.
Keep performance visible after deployment
Track model behavior and approved performance indicators so teams can understand when a model needs review, comparison, or retraining.
Show the model lifecycle
Demonstrate model configuration or creation, performance evaluation, explainability or feature contribution, and deployment into a live strategy.
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performance
strategy
From labeled outcomes to live risk scores
Prepare labeled outcomes and features.
Train and validate the model.
Review performance and explainability.
Deploy the model into decision strategies.
Capture new outcomes and monitor performance over time.
Keep model intelligence operational
DataVisor brings supervised models into the same environment as real-time features, unsupervised detection, rules, graph intelligence, and decisioning so a model becomes part of a broader risk strategy instead of another isolated score.
Apply supervised models where outcome history is valuable
Use supervised ML for account takeover, application fraud, card and payment fraud, ACH/wire risk, scams, and other use cases with meaningful labeled outcomes.
Make performance understandable and controllable
Detail approved capabilities for explainability, feature contribution, validation, champion/challenger testing, drift monitoring, version governance, retraining, and bring-your-own-model support.
- Explainability and feature-contribution views
- Champion / challenger testing
- Drift monitoring and retraining workflows
- Model version governance and audit history
- Bring-your-own-model support
Common questions
What data is needed to train a supervised fraud model?
Supervised models learn from labeled outcomes and the associated features. The exact data requirements depend on the use case, available history, and target decision.
Can supervised and unsupervised ML be used together?
Yes. DataVisor can combine supervised models trained on known outcomes with unsupervised detection designed to surface new and coordinated behavior.
How are model decisions explained?
DataVisor supports model explainability capabilities; final page copy should enumerate the approved feature-contribution and explanation views available for the relevant model workflow.
Move from model score to business action
Walk through how DataVisor trains, evaluates, deploys, and uses supervised intelligence in a real-time fraud decision.