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
Most people associate generative AI (GenAI) with large language models (LLMs). While LLMs focus specifically on generating text, GenAI encompasses a wider range of content generation tasks beyond just language, including images, music and more. Broadly speaking, GenAI uses machine learning algorithms to analyze and learn from existing data sets
In 2024, we will witness the proliferation of synthetic data across industries. In 2023, companies experimented with foundational models, and this trend will continue. Organizations see it as an emerging force to reshape industries and change lives. However, the ethical implications can't be overlooked. Let’s explore some industries I think
Katie King has interviewed subjects from many walks of business life for her books: academics, venture capitalists, executives from high-profile brands and telecommunications companies. Among them, one that made a lasting impression was an artist: Ai-Da. King interviewed the artificial intelligence-powered humanoid robot artist for her 2022 book AI Strategy
Over the last year, generative AI has captivated the public imagination. Many of us have become newly acquainted with the concept of an approaching Singularity coined by John von Neumann or Nick Bostrom’s Paper Clip thought experiment. Fortunately, Microsoft’s office assistant, Clippy, has yet to dutifully transform our planet into
As you read this, someone you know may be in the hospital for an acute illness. Treatments for life-threatening illnesses are often based on a combination of existing protocols and staff experience. And that’s great when hospitals are running smoothly and are adequately staffed. But too often these days, hospitals
As a member of the SAS Data Ethics Practice, I was excited to collaborate with teams at the SAS Hackathon to learn more about their ideas for trustworthy AI. Artificial Intelligence has the potential to make a difference in the real world, and partnering with the hackathon teams was a
As in most other sectors, health care is changing at lightning speed. Access to data makes it possible to speed up clinical trials, develop more personalized medication, make quicker and better diagnoses, improve the quality of patient care and save lives. The pandemic has sped up digital transformation in every
Data is crucial for the development of artificial intelligence (AI) applications. However, the rapid availability of data is a challenge due to increasingly strict privacy regulations. A possible solution is to use synthetic data. Gartner predicts by 2024 that 60% of the data used to develop AI and analytics applications
A common barrier to quantitative research, especially in health and financial areas, is the inability to share sensitive data due to confidentiality and privacy. It can be difficult and time consuming to get permission to share the data, which means useful research is delayed or not even attempted. However, collaborators seeking
