Jul/Aug 2015

The media and watercooler chatter alike increasingly focus on how advances in machine learning and artificial intelligence (AI) are boosting the ability of predictive analytics to benefit businesses’ bottom lines. Some of that talk ponders the potential for smart machines to replace humans in higher-complexity jobs. No doubt, smart machines are getting smarter. But even the smartest machines still lack fundamental human characteristics that are absolutely critical to enabling people to solve problems. One of these key capabilities is curiosity – surely a computer can’t replicate that, can it?

• Scott Zoldi

The healthcare industry in the United States is going through a major transformation and is becoming much more consumer friendly. This process of “consumerization” will drive healthcare executives to pay close attention to what patients feel and think about healthcare organizations and services – something the industry has ignored for decades. This trend should continue and be accelerated as we all start paying more out of pocket for healthcare services.

• Sagar Anisingaraju
• Mo Kaushal

It’s no secret that with the introduction of the Affordable Care Act (ACA), the healthcare landscape has changed dramatically for all involved in the industry, from doctors to patients to health insurance providers. These new regulations and changes have generated a significant shift in how members are able to approach their healthcare options, leading to a much more consumer-driven marketplace.

• Charlie Bitzis

Fields ranging from social media and finance to healthcare and government are turning to network science for greater insight. Still in its early stages of development, the discipline will continue to gain influence as the world becomes increasingly interconnected. Analytical platforms currently available help us to better understand the structure of networks. However, many questions about how complex systems operate remain unanswered, calling for openness to unexpected possibilities when making predictions about networks.

• Will Towler

A typical organization loses about 5 percent of its revenues due to fraud each year [1]. The total cost of non-health insurance fraud in the United States is estimated to be more than $40 billion per year [2]. Other popular fraud examples include credit card fraud, money laundering, healthcare fraud, telecommunications fraud, click fraud, tax evasion and many more. Given today’s fierce competitive environment, narrowing margins and increasing shareholder pressure, more and more firms are considering fraud detection and prevention as a key strategic priority. To continuously outsmart the fraudsters, who keep on perfecting their tactics thanks to new emerging technology (e.g., the Internet of Things), firms need new, efficient and effective tools such as big data and analytics.

• Bart Baesens
• Véronique Van Vlasselaer
• Wouter Verheyen