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In 2016, there were 2.9m reports of workplace injuries in the US alone, according to the latest data from the Bureau of Labor Statistics. With a rate of 2.9 cases per every 100 employees, worker safety is a hot-button issue for businesses’ risk management.
Employers are estimated to pay nearly US$1bn every week for direct workers’ compensation costs, according to the Occupational Safety and Health Administration. And that’s not to mention the social impact of workplace injuries and illnesses.
But the advent of new technology like artificial intelligence and the Internet of Things has the potential to usher in a new era of risk management.
Wearables are among the innovations currently making their way through the pipeline at the newly launched Marsh Digital Labs. The San Francisco-based operation is bringing risk management and the insurance industry into the tech world by acting as an incubator for experimentation and innovation in emerging tech.
The goal is to utilise cloud-based platforms to develop new, reusable, test-and-learn tools. “The labs include a machine-learning garage to create next-generation risk models, intelligent automation, and cognitive capabilities,” says Asha Vellaikal, a Silicon Valley stalwart who’s now heading up Marsh Digital Labs. “A cutting-edge design studio enables seamless integration with clients’ own digital applications to conduct pilots in the market.”
Worker safety is one of the problems currently being addressed. “Workers’ compensation is a huge issue. There’s a lot of workers’ safety problems, especially in certain industries like manufacturing, where there’s a lot of repetitive motion,” says Vellaikal. “We’re working with an IoT smart belt start up to develop a product for proactive risk identification.”
The smart belts are thin, black belts that slip easily around employees’ waists. Each belt has about ten sensors that generate data about how an employee moves – from the angle that they bend and how much they’re twisting.
“What Marsh is doing is collecting that data and analysing it,” says Vellaikal. “All of a sudden, we have a view on a single employee for all the time they’re wearing the belt, and we also have an aggregate view. Let’s say we have 30 employees in the same job wearing the belts. We now have a way to compare them to see who is bending the most and who’s experiencing quite a bit of repetitive motion, and then we have a pre-loss indicator to identify which particular employee may be at risk of injury going forward.”
This technology is still in the testing phase over at the labs, but there are already real-world examples of how businesses have benefited from it. For example, data gathered from materials inspectors at an airport who were wearing the belts raised a red flag because they were bending too much. It was discovered that because the serial numbers on the baggage tags were so small, the handlers had to bend lower than comfortable to read them.
For the employer, it was a simple fix – they made the serial numbers bigger. What could have cost big money down the road was proactively managed well before any problem arose.
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