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
En la era de la inteligencia artificial y el machine learning, el valor de los datos es incuestionable. Los modelos aprenden, predicen y toman decisiones a partir de los datos con los que son entrenados. Sin embargo, cuando los datos reales escasean o no pueden utilizarse por razones de privacidad,
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
When most people think of AI, they picture futuristic technology taking over decision-making processes. But according to Jared Peterson, VP of Platform Engineering at SAS, the real value of AI isn’t replacing humans – it’s changing the way you work and run your organizations. Peterson’s presentation at SAS Innovate 2025
The health care industry has more data than it can utilize in meaningful decision-support capabilities. Whether it is the volume, the velocity, or the variety of the data, wrangling insights from this incessant stream is a never-ending and complex task. Enter the age of AI, where an agent can synthesize
Generative adversarial networks (GANs) offer a promising solution by creating synthetic data that mimics real datasets, allowing developers to build models without exposing sensitive customer information.
Synthetic data has become a valuable resource in data science and machine learning. Superior quality, reliable synthetic data facilitates analysis and iteration at scale while mitigating privacy concerns associated with real data and can fill gaps where real data is scarce. Note, however, that “good” synthetic data is not defined
AI is reshaping insurance – from streamlining underwriting and fraud detection to fighting climate risk.
AI and automation – often referred to as hyperautomation – are evolving rapidly with industry experts emphasizing their increasing ability to operate independently and make intelligent decisions. By combining powerful generative AI with business expertise, organizations can accelerate and streamline their processes like never before. I recently sat down with Mayank
The world of data and AI is evolving at breakneck speed, with 2025 shaping into a year of breakthroughs and significant challenges. From AI model hallucinations to the role of synthetic data in innovation, industry leaders are grappling with complex issues that will shape the future of technology. I recently
There is no question that organizations worldwide are increasing their investment in AI. There is also little doubt that AI is starting to impact many different sectors. The health care and life sciences sectors are no exception, with many organizations investing in new technology. The real issue is how to
Every year, as Data Privacy Week sharpens the focus on protecting personal information, I’m reminded of a customer event a major North American bank hosted at SAS world headquarters. The bank’s chief data officer led a roundtable discussion on generative AI (GenAI) with a group of esteemed data and AI experts. The
Synthetic data has emerged as a powerful tool for overcoming the limitations of real-world data. The future holds great promise for accelerated innovation. With synthetic data, companies can now generate financial transactions, medical records or customer behavior patterns that maintain statistical relevance like real data. This emerging technology can help
Major global elections, volatile financial markets, extreme weather events, and sophisticated and costly cyberattacks are increasing operational risks across every industry. Generative AI (GenAI) is redefining how industries navigate this uncertainty and transforming potential risks into powerful opportunities. Organizations across industries are increasingly invested in GenAI – for instance, last
Fraudsters are relentless but tax agencies are tenacious in their pursuit of illicit acts. In recent years, synthetic identity fraud has emerged as a significant threat to businesses and tax agencies. Unlike traditional identity theft, where criminals steal real personal information, synthetic identity fraud involves creating entirely new identities by
In 2012, Harvard Business Review declared the data scientist the sexiest job of the 21st century. Here’s what we knew at the time: big data was (and still is to this day) an enormous opportunity to make new discoveries. We were in the boom of user-generated content from social platforms,
As data decays, it becomes less useful. See how synthetic data for insurance can help.
Fairness, transparency, integrity and competition are essential for managing public funds. We rely on departments to choose the best value from the private sector. Efficient public procurement improves services, infrastructure, and the economy. It must also be accountable to the public by protecting financial loss from fraud, waste, abuse, and
Insurers are racing to adopt GenAI, despite concerns. See where the industry is headed.
Data scarcity, privacy and bias are just a few reasons why synthetic data is becoming increasingly important. In this Q&A, Brett Wujek, Senior Manager of Product Strategy at SAS, explains why synthetic data will redefine data management and speed up the production of AI and machine learning models while cutting
Synthetic data generation, as its name suggests, is one component of generative AI. With this technology, marketers can generate artificial data sets that share the attributes and characteristics of real customer data. As marketers continue to expand their use of both traditional AI and GenAI, synthetic data generation reduces the
Dans le paysage technologique actuel, les données synthétiques, nouveau sous ensemble de l’IA générative, apportent de nouvelles pistes de réflexion pour la création des modèles d'intelligence artificielle. Contrairement aux données traditionnelles, pouvant être limitées par des contraintes de biais, de quantité, ou encore des contraintes de confidentialité et de conformité,
