SAS® Intelligent Decisioning
SAS Intelligent Decisioning automates analytics-based decision making so organizations can function more efficiently while improving interactions with customers, suppliers, partners and employees. Likewise, organizations that are highly regulated − such as financial services, health care and insurance − can more easily achieve compliance as a result of documented, traceable decisions.
Enterprise data throughput
• Ability to deliver more than 5,000 real-time transactions per second:
• Ability to achieve response times of 10 milliseconds per transaction.
• Simple integration with a variety of third-party applications at the data level.
Decision flow builder
• A centralized, graphical drag-and-drop interface lets you assemble business rules, custom code and models into complete decision flows, minimizing the need to write deployment code that joins these pieces together.
• To define decisions, you can browse existing repositories of data, models and business rules and select from existing assets.
• Create custom code within a decision flow to integrate with business application REST APIs, databases, web service calls and open source Python.
• To control decision orchestration, add condition logic (i.e., IF-THEN-ELSE) and use outputs from any preceding rule or model.
• From a decision flow, you can easily navigate to the business rule editor to simplify editing and rule-logic updates using deep linking.
• Ability to drill through from decision flow to model repository simplifies model selection and model inspection.
• The enhanced rule list view provides compressed, easy-to-read rules for readily identifiable logic definitions.
• Version control for entire decision flow simplifies testing and validation.
• Reduce risk and improve outcomes by automating operational decisions.
• Capitalize on customer and operational needs – with the right action, at the right time, to the right channel.
• Streamline business rule management.
• Manage high-volume customer interactions and other enterprise decisions effectively and consistently.
• Standardize analytical model use and deployment in one governed environment.