SAS' Mary Osborne, Ali Dixon Ricke, and Franklin Manchester break down what insurers still need to learn about generative AI.
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
Rising chronic conditions, escalating demands for health care services, and the dual crises of staff shortages and burnout make the urgency of streamlined and efficient processes more critical than ever. The future of care lies in advanced technologies. Whether it’s a routine check-up or a complex medical condition, integrating AI
The time is now for insurance companies to lead the way in addressing climate risk
With a new year around the corner, it’s time to start thinking about what tech trends are coming in the months ahead. 2025 will be interesting, because now that AI has been around long enough to go from novel to normal, we should stop marveling at what it can do
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,
At the very heart of the financial world lies a commodity so vast it’s almost immeasurable ... and it’s growing exponentially with potential yet unrealized. Big data – complex structured and unstructured datasets arriving from innumerable sources – is reshaping the global banking industry. Used effectively, big data can support the delivery
An epic battle is taking place in the world of banking fraud and financial crime. Banks and fraudsters are using the latest technology, and the winner of the war has yet to be determined. While AI and GenAI hold incredible transformative potential for banks and are being used in countless
Many of us have fond memories of a Family Feud host saying, “And the survey says…!” as the game show’s answer board lit up, bringing joy or disappointment to the contestants. For some, the host who comes to mind is Richard Dawson or Ray Combs, while today’s fans likely think
As data decays, it becomes less useful. See how synthetic data for insurance can help.
Climate risks threaten insurers' profitability and financial stability. See how insurers can adapt.
Fraud knows no boundaries. It’s one of the biggest challenges related to tax governance around the world. Tax crimes have serious consequences – tax losses, tax base erosion, national security risks and economic disruption. The effect is far-reaching. Governments are strained to fill the gap between revenues and spending and
It’s no surprise that MarTech in 2024 seemed to be dominated by GenAI, GenAI and … more GenAI. Perhaps this is a bit of an exaggeration, but you can’t deny this topic was a headliner in the MarTech space, along with AI and customer data platforms. Here are my top
As we move into 2025, AI continues to transform industries in unprecedented ways, driving efficiency, innovation, and productivity. But with this rapid advancement come critical ethical questions. How can we ensure that AI systems protect the rights and well-being of individuals? Manufacturing and agriculture are two essential industries where answering
Generative AI (GenAI) is here to stay – there’s no question about it. A recent SAS survey of 1,600 organizations found that 54% have begun implementing It, and 86% plan to invest in it within the next financial year. As organizations integrate AI into their workflows, a critical question arises:
"Generative AI (GenAI) initiatives should support broader public goals and needs," says SAS' Ensley Tan. While governments recognize GenAI's potential to improve operational efficiency and citizen experience, there is more to it than setting up projects and expecting them to work. Tan, SAS Asia-Pacific Lead for Public Sector Consulting, said public
Managing the explosion of paper documents is complicated and expensive. Intelligent Document Processing (IDP), also known as Document Intelligence, addresses those challenges among other benefits.
Remember the first time you held a smartphone in your hand? It wasn’t just a fancy phone – it was the beginning of a revolution. Before smartphones, we used separate calls, cameras, emails, and navigation devices. But almost overnight, this gadget transformed everything: how we communicate, consume media, work and
As businesses in the UK and Ireland rapidly adopt generative AI, strategic insights from the latest SAS study reveal the roadmap to successful integration and the hurdles to overcome. GenAI is rapidly transforming how businesses operate, innovate, and interact with customers and employees alike. However, as the technology proliferates, so
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
Data science continues to be a pivotal force driving innovation across industries. From enhancing customer experiences to optimizing operational efficiencies, the role of data science is expanding, bringing with it new challenges and opportunities. This article explores the emerging trends and technologies that are shaping the future of data science
Ever since generative AI burst onto the scene, it has sparked a whirlwind of ethical concerns. Unlike traditional AI, which typically analyzes and makes predictions based on existing data, GenAI creates entirely new content – videos, text, audio, code and more. This creative power introduces a new level of risk,
Everyone is talking about artificial intelligence (AI) for a good reason. It’s already revolutionizing the world as we know it. And governments are well-poised to use AI to improve services and operations. But we can expect the AI conversation to shift into 2025. Attend any conference of government leaders, and
Five years ago, there was no conflict in Ukraine. People went into offices for work. No one was fighting over toilet paper. Everyone’s concept of AI was loosely anchored by Hollywood films like Terminator or War Games. And, insurers enjoyed relatively stable profits, consistent inflation/interest rates, and manageable change volume.
Another promise of innovation and advancement? Not in 2025. The hype of market disruption and transformation might be the content that catches attention, but it’s not always the most accurate description of the industry. For far too long, industry experts have praised the ideas of future-forward growth and promised year-over-year
As we peer into 2025 looking for signs of what’s to come, one thing is certain – the global banking industry will continue its evolution. Historically, banks have responded to change more slowly; the industry’s risk-averse culture drives a calculated and measured approach. However, the volatility, uncertainty, and challenges of
Model cards have been around for a few years now and while their purpose is clear – to increase machine learning transparency and to create a way to communicate usage, ethics-informed evaluation, and limitations – they're still evolving. Many companies have tried their hand at creating their own version of
The commercial real estate crisis is looming for insurers and banks. Learn how AI decisioning can help.
Want to deploy digital twins as part of your predictive maintenance strategy? You should, for countless reasons that have been identified elsewhere. But like anything else worth doing in business, it’s a process – you need to know where your organization lands on the maturity curve, and where it’s going.
In a world rich in data, data enthusiasts and problem solvers can have greater success and innovate faster with flexibility in choice. To code or not to code. The answer aligns with the problem and the data talent working to solve it. What does innovation look like inside your organization?
