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,
AI is no longer a futuristic concept – it’s a mainstay in our daily lives, both personally and professionally. In the business world, AI is revolutionizing workflows, driving efficiency and speeding up processes. However, as organizations rush to benefit from this modern technology, they must prioritize the ethical and transparent
Before rushing to invest in generative AI (GenAI), organizations must pause and take a step back. GenAI is powerful and has shown potential to revolutionize multiple industries – but it’s not a silver bullet. Now that we’ve finally gotten past the hype phase, it’s time to look at the realities
AI is at its best when it is used to enhance productivity and improve the lives of those it affects. When used correctly, AI can also save lives. That’s the vision driving a new project at SAS, where applied AI models and cameras create a simulated work environment focused on
Stop bias in its tracks – learn about the value of synthetic data for insurance.
Synthetic data generation has intrigued across industries for its many use cases, including fraud detection, clinical trials, worker safety and law enforcement. One of the main benefits is the low cost of creating synthetic data, which is often cheaper than collecting actual demographic, psychographic or behavior-based information. With such data,
Across the world, investigators and law enforcement officers are tackling a rapidly evolving and expanding workload fueled by an increase in complex modern-day crimes. As technology alters the type and methodology of the crime itself – the evasion of tax payments, theft of public funds, erroneous disbursement of benefits, gaming
The ancients’ practice of publicizing set-in-stone personal records would run anathema to modern data privacy laws. These days, in lieu of using contemporary personally identifiable records, I anonymized a 4,000-year-old tax record from ancient Babylon to describe three principles for effective data anonymization at scale: Embracing rare attributes: values and
As the old saying goes, “You wait ages for a bus and then two [or possibly three]come along at once.” This saying can be updated to reflect life in our increasingly digital world: "You wait ages for a genuine disruptive technology and then two [or possibly three]arrive simultaneously." This phrase
Synthesizing data? Who does that? Aren’t we supposed to be running the experiments and measuring things to produce real data? While generally true, there are scenarios in which the use of generative AI (GenAI) is beneficial. Let’s explore the benefits via “what if” scenarios. Before we begin, it’s important to
