Artificial intelligence, and more specifically machine learning, is being deployed in the insurance space in some very exciting ways -- from assessing underwriting risks to determining pricing to evaluating claims. But with these advances come sizable risks, some of which are already surfacing. Insurers need to take a proactive approach to mitigate risks so that they don't wind up experiencing the same financial and reputational difficulties that other industries have seen. In 2019, Apple launched a branded credit card in partnership with Goldman Sachs. Before long, users noticed that women were generally being offered lower preapproved credit lines than men.

The trend toward digitization has been building momentum over the past few years. Cash apps, digital wallets and card-not-present (CNP) transactions are replacing cash, checks and physical credit cards. Meanwhile, fraudulent activity across digital channels is ramping up. Risk is high, as many consumers choose to shop online and use contactless payment methods to avoid unnecessary exposure to the virus. Unfortunately, fraud and risk teams lack experience and historical data around emerging digital payment options -- two of the key factors that traditional rules-based fraud solutions use to fight fraud.

Let us consider a scenario: one night, an executive responsible for operations for a remote downstream oil and gas refinery gets a call from one of their subordinates saying things started acting up ever since they plugged in a USB they brought from home. Multiple processes have become unstable and commands sent to equipment are not executed as requested. Panicking, they say there has been a cyber attack on the supervisory control and data acquisition (SCADA) system. Valves, pumps, and compressors connected to the system are going haywire, and the organisation's legacy systems were not equipped to prevent whatever new malware snuck into the system. Production comes to a halt for two days.

Banks are employing artificial intelligence (AI) and machine learning (ML) tools to strike that balance.

Consumers want their digital banking experiences to do more than just provide security, and processes that are not seamless could frustrate them into seeking alternatives. This means that the financial services market is facing a Goldilocks conundrum. Authentication measures cannot be so rigorous that they alienate legitimate customers, but they also cannot be so lax that bad actors gain access with ease. The balance has to be just right. Banks are employing artificial intelligence (AI) and machine learning (ML) tools to strike that balance.

Managing today's supply chain requires a large amount of diverse data from multiple resources to meet the complex operational requirements and customer demands. Massive excel spreadsheets, customer complaints, out-of-stock notifications, and backorders are the data typically considered to analyze the number of products to be bought, manufactured, and delivered. Machine learning makes it possible to discover the patterns in supply chain data through algorithms that spontaneously pinpoint the most influential factors to a supply network's success. Detecting trend patterns in the supply chain data can revolutionize any business type. These algorithms are finding new patterns in the supply chain data every day without the need for manual intervention to guide the analysis.

Cybersecurity and artificial intelligence now seem to go hand in hand as complementary weapons against crime, but what lies ahead? Manish Bahl is the senior director for Cognizant's Centre for the Future of Work. He is responsible for thought leadership around code halos, digital transformation and the future of work. He works with IT and business decision-makers to provide vision on digital transformation and its effects on business, people, technology and culture. "Cognizant's Centre for the Future of Work is a dynamic thought leadership powerhouse that examines how work is changing, and will change, in response to the emergence of new technologies, new business practices, and new workers," he explains.

DAVOS, SWITZERLAND – When global leaders and captains of industry gathered in Davos last week for the 2018 World Economic Forum, AI and the economy of tomorrow dominated the discussions. Coupled with the launch of two new reports into the future of work at the summit, it is clear that AI and reskilling are already central considerations for policymakers and businesses alike. This much was evident in the coverage of the event. Both the British Prime Minister Theresa May and French President Emmanuel Macron announced government-funded innovation measures around securing'safe and ethical AI'. Meanwhile, Google CEO Sundar Pichai boldly claimed that artificial intelligence will prove more important to the fate of humanity than our mastery of fire, and Alibaba founder Jack Ma argued automation technologies could'kill a lot of jobs'.

The I-7 Innovators' Strategic Advisory Board on People-Centered Innovation is the engagement group launched last May during the G7 Summit in Taormina (par. The group is in charge of providing guidance on emerging innovation issues. The creation of this group is an experiment proposed by the Italian G7 Presidency with the goal of driving attention towards the multiple challenges that innovation poses and cannot be faced only at national level. Each country and the EU have designated their own group of experts. We encourage Canada to consider continuing this experiment during their imminent Presidency. How can AI help governments make better decisions and deliver policies and services more effectively?

The I-7 Innovators' Strategic Advisory Board on People-Centered Innovation is the engagement group launched last May during the G7 Summit in Taormina (par. The group is in charge of providing guidance on emerging innovation issues. The creation of this group is an experiment proposed by the Italian G7 Presidency with the goal of driving attention towards the multiple challenges that innovation poses and cannot be faced only at national level. Each country and the EU have designated their own group of experts. We encourage Canada to consider continuing this experiment during their imminent Presidency. How can AI help governments make better decisions and deliver policies and services more effectively?