Generative AI (GenAI) has moved past its experimental phase. It is now deeply embedded in core business workflows, from sophisticated financial modeling and predictive analytics to dynamic content creation and strategic decision-making. This means the very definition of a “job-ready” professional has dramatically changed. Global surveys reveal the same trend:
Shrinking budgets, rising citizen expectations and increasingly sophisticated fraud schemes have stretched resources thin. Meanwhile, outdated systems and manual processes make it harder for frontline workers to keep pace. According to a global study commissioned by SAS, a striking 85% of policymakers and public sector leaders cite the fight against
Fraud remains a huge challenge for governments and inspectors at all levels, as fraudsters today are more successful than ever. Technologies such as analytics and AI have tremendous potential to support investigations. Why are fraudsters so successful these days? Partly because they seek easy targets, work hard to stay off
Think of data engineering as plumbing for the digital world. All those smart dashboards, AI models and analytics reports don’t work unless someone builds the pipelines that bring clean, usable data to them. That someone is a data engineer. Why data engineers are suddenly in the spotlight AI is everywhere today
Machine learning engineers are among the most sought-after professionals in the data analytics and AI space today. But behind the buzzwords and job titles lies a role that requires data literacy and a range of skills in statistics, software deployment, critical thinking and ethical judgement. If you're wondering what this
If you have a background in pharmacy, life sciences, biotechnology, or statistics – and you’re curious about the world of clinical research – there’s one career path you might want to explore: clinical SAS programming. Maybe you’ve heard the term before and wondered what it really involves. The good news?
The financial services industry is undergoing a period of profound change, driven by a dynamic economic landscape, increased regulatory scrutiny, changing consumer behavior and rapid technological advances. Banks operating in this environment are under increasing pressure to transform their risk modeling and decision-making ecosystems in order to remain competitive. This
What insurance companies need to know about the Fundamental Rights Impact Assessment (FRIA) The EU Artificial Intelligence Act (EU AI Act) is ushering in a new era of accountability and transparency for organizations deploying AI systems – particularly in high-impact sectors like insurance. The Fundamental Rights Impact Assessment (FRIA) is
The financial industry is evolving at an unprecedented pace. Regulatory frameworks are becoming more complex, customer expectations are shifting and financial risks are emerging in new and unexpected ways. Because of this, relying on outdated risk management systems is no longer sustainable. Banks must adopt data-driven strategies to remain competitive
UK banks are under growing pressure to manage risks more effectively, whether from economic shifts, technological advances or geopolitical uncertainty. The Bank of England’s Prudential Regulation Authority (PRA) sent a letter to the CEOs of UK deposit holders outlining its supervisory priorities for 2025. The letter sets expectations for stronger
