The use of Artificial Intelligence to Combat Public Sector Fraud

whitePaper | May 20, 2021

AI has become a strategic imperative. Early adopters are achieving massive returns, while others risk falling behind. Yet, with AI knowledge and resources scarce, most companies cannot copy the FAANG approach. Even if their vast resources were more readily available, it’s hard to reboot an organization without disrupting the bottom line. Most executives need to work with the resources available — their existing people, processes, and technology.

whitePaper | September 19, 2022

The field of artificial intelligence (AI) traces back to the 1950s. More recently, the increased availability of both data and computing power has bolstered rapid adoption of AI and machine learning (ML) solutions (collectively, AI/ML)

whitePaper | April 14, 2021

While the new era of remote work has provided some businesses with surprising productivity boosts as high as 13%, many are still struggling to secure their suddenly expanded network of remote endpoints and IT resources against a growing onslaught of cyberthreats.

whitePaper | January 4, 2022

When people hear AI they often think about sentient robots and magic boxes. AI today is much more mundane and simple—but that doesn’t mean it’s not powerful. Another misconception is that high-profile research projects can be applied directly to any business situation. AI done right can create extreme return on investments (ROIs)—for instance through automation or precise prediction. But it does take thought, time and proper implementation. We have seen that success and value generated by AI projects is increased when there is a grounded understanding and expectation of what the technology can deliver from the C-suite down.

whitePaper | June 22, 2022

Artificial intelligence (AI) and machine learning have revolutionized how organizations are using their data. Automated machine learning (AutoML) facilitates and improves the endto-end data science process. This process includes everything from preprocessing and cleaning the data, selecting and engineering appropriate features, tuning and optimizing the model, analyzing results, explaining and documenting the model, and of course, deploying it into production.

whitePaper | August 18, 2021

Among retail marketers, a reliance on traditional business intelligence tools has left the intuitive decision-making in the hands of the marketing team. Download this guide to learn how to solve this AI dilemma.