The SAS Data Science Blog
Advanced analytics from SAS data scientists
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
Authors: Madhuri Kulkarni, Anand Surana, and Sudeshna Guhaneogi Imagine skipping the hours-long wait for your favorite amusement park ride by knowing when peak riding times are? Or how about having an itinerary that accounts for everyone’s favorite park activity? Ready for a wild ride? In this post, we explore how
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.
Authors: Bahar Biller, Jagdishwar Mankala, and Jinxin Yi Managing spare parts inventory is a critical aspect of asset performance management, especially in industries where equipment downtime is costly. This post, based on a real-world project with a major aircraft manufacturer, explores how to optimize spare parts inventory under uncertainty. We
Enforce SAS code standards using SASjs and GitHub Actions to maintain secure, readable, and maintainable code. Automated linting blocks non-compliant code from merging into protected branches.
Accurately identifying lag structures between related time series is essential in public health forecasting, particularly during epidemics where delays between infections and hospitalizations affect planning. Using a simulated SEIR model and SAS Viya’s PROC TSSELECTLAG, distance correlation is shown to outperform Pearson correlation by correctly identifying nonlinear lag relationships—such as the true seven-day lag between new infections and hospital admissions.