SAS empowers modern demand planners with AI-driven tools like SAS Visual Forecasting and SAS Intelligent Planning to meet rising customer expectations through accurate, responsive, and scalable demand planning solutions.
SAS Viya now includes built-in bias mitigation in its machine learning procedures to help users develop ethical and trustworthy AI models by automatically detecting and reducing bias during training.
SAS is transforming the motor insurance claims process by integrating of tools - blending data management, ML, and UX into 1 complete system.
Health care fraud, waste, and abuse (FWA) cost the industry billions each year—but what if we could outsmart it? Enter SAS Payment Integrity for Health Care: Detect and Prevent, a groundbreaking solution that is redefining how we can tackle FWA. As the first to offer models-as-a-service in a Commercial Off-The-Shelf
This guide explains how businesses can successfully implement generative AI by focusing on narrow use cases, curating data, leveraging AI agents, safeguarding sensitive information, monitoring for bias and toxicity, and ensuring model accuracy and relevancy.
As agentic AI systems evolve through protocols like MCP and A2A, traditional security practices must be adapted to address new risks such as goal misalignment and tool instruction abuse. This article explores practical threat modeling strategies, including goal alignment cascades and distinguishing between parameter-only vs. instruction-enabled tool calls.
SAS's Ann Kuo walks you through how SAS Tech Support developed an email classifier to clean up spam and misaddressed emails using SAS Viya's NLP-based text classifier
SAS' Sophia Rowland breaks down the roles of each team member in a long-term machine learning project and how they can better combine their efforts to increase efficiency and efficacy
AI is increasingly prevalent in our daily lives, and this trend is unlikely to change anytime soon. This comes with risks, but by understanding these risks, we can build AI systems that mitigate them.
SAS' Mary Osborne, Ali Dixon Ricke, and Franklin Manchester break down what insurers still need to learn about generative AI.
