Watson OpenScale tracks and measures outcomes from AI across its lifecycle, and adapts and governs AI to
changing business situations - for models built and running anywhere.
Measure and track AI outcomes
Track performance of production AI and its impact on business goals, with actionable
metrics in a single console.
Tune your AI for business
Apply business results to create a continuous feedback loop that improves and sustains AI
Govern and explain AI
Maintain regulatory compliance by tracing and explaining AI decisions across workflows,
and intelligently detect and correct bias to improve outcomes.
Credit Risk Modeling
Credit lenders can monitor risk models for performance, bias, and explainability to limit
risk exposure from regulations, and create more fair and explainable outcomes for customers.
Explainable claims processing
Insurance underwriters can use machine learning to more consistently and accurately assess claims risk, ensure fair outcomes for customers and explain AI recommendations for regulatory and business intelligence purposes.
Predict CSP asset failures
Data scientists can build machine-learning models and work with their IT operations teams to confidently recommend proactive asset maintenance for communications service providers (CSPs).
Explore reference material to get started and see what you can do in Watson OpenScale.