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
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
AI는 비즈니스와 기술 분야에서 여전히 가장 뜨거운 주제이지만, 아직 AI를 효과적으로 활용하여 가치를 창출하는 데에는 어려움이 많습니다. 여기 여러분의 능력을 발휘할 기회가 있습니다. 제 5회 'SAS 해커톤'에 참여하시고 최신 AI 기술을 활용한 가치 창출에 도전해 보세요! *SAS 해커톤 참가등록 : 등록 링크* 지금부터 8월 31일까지 등록할 수 있는 이번 해커톤은
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
Not too long ago, whenever I heard the word ‘hackathon,’ I thought of Wreck-It Ralph from the Disney movies. Ralph was a video game character tasked with wrecking buildings, while Fix-It Felix would repair them. In Wreck-It Ralph 2, Ralph broke the internet. I used to think hackathons were only
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
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
