People are starting to compile resolutions for the new year, focusing on evolving their own habits and goals. At SAS, we’ve also looked toward 2026 to gather predictions on how AI in the public sector might evolve over the next 12 months. Prediction: By 2026, governments will utilize large language
For years, “responsible AI” has lived comfortably as a corporate promise, a slide in a presentation, a talking point at a conference. But as the EU AI Act phases into force, that comfort is rapidly eroding. The regulation officially entered into force on August 1, 2024, but its obligations will
Financial institutions operate in a highly regulated, data-sensitive environment. At the same time, these institutions are under pressure to modernize credit scoring models that balance fairness, accuracy and compliance. The challenge? Most financial institutions lack access to the volume and diversity of high-quality, privacy-safe data needed to fuel these models.
Learn how parametric insurance works and why synthetic data could make models more accurate.
Experimentation is the engine of innovation. Whether optimizing manufacturing processes, testing new materials, or simulating policy outcomes, the ability to run controlled experiments is essential. Design of experiments (DOE) is a well-established statistical methodology that helps organizations systematically explore the relationships between variables and outcomes. However, traditional DOE has its
Synthetic data – algorithmically generated data that mimics real-world data – has emerged as a cornerstone in modern AI workflows. But its promise comes with persistent myths about its capabilities, limitations and reliability. Synthetic data is being explored across industries, from training machine learning models to helping businesses safeguard customer
Every AI success story starts with a single decision: to move beyond experimentation and commit to real-world impact. But moving from idea to enterprise-scale deployment isn’t just about algorithms – it’s about laying the right groundwork. In the first part of this series, we explored three ways to lay the
Get inspired by a SAS Hackathon team that used AI and IoT to tackle heat stroke.
Financial fraud is a high-stakes issue in banking, where schemes are becoming increasingly sophisticated and costly. As a result, detecting anomalies quickly and accurately is a top priority. But traditional data-driven fraud detection models face challenges such as data scarcity, privacy constraints, and model bias. This is where synthetic data
If you think of SAS as a data, AI and analytics powerhouse, Epic Games as the studio behind Fortnite and Georgia-Pacific as the company that makes paper towels and more, you’re not wrong. But you’re also missing the bigger story. One that – quietly and collaboratively – is reshaping how
