How to Use Analytics to Protect Your Employees and Bottom Line

Fewer than one-third of risk managers say they’re using analytics to manage casualty risk effectively, according to recent Marsh web survey.

About 150 risk executives responded during a recent Marsh webcast when asked to describe their company’s use of predictive analytics to manage casualty risks. Of those:

• 29% said their use of analytics was effective.
• 3% said it was ineffective.
• 26% said they weren’t sure.
• 42% said they don’t use predictive analytics to manage casualty risks.

And yet, thanks to advances in data quality and analytics, risk managers can find more value than ever in using customized metrics to measure and reduce total cost of casualty risk. They can use analytics to identify what’s driving costly claims, and then take steps to manage them more quickly and effectively. That’s especially true in workers’ compensation, where there’s a wealth of data to identify critical trends and prioritize strategies to address them.

In addition to aggregate group health, disability, and workers’ compensation data, many employers collect three types of information about their individual workforces:

• Injury and illness data, including frequency and severity of injuries, lost work days, and workers’ compensation claims costs.
• Employee discomfort data, collected via surveys asking employees to rate the frequency and severity of pain and discomfort in specific body parts.
• Predictive risk assessment data, which provides a forward-looking view of potential injuries based on the presence of known workplace risks.

Each of these data sets has its own biases. For example, employees sometimes feel pressure from supervisors to understate their pain and discomfort in surveys. But taken together — and used with emerging tools to analyze human body dimensions, functional movements, force testing, gait analysis, and other factors — you can make more informed decisions about workplace safety. For example, you can use this data to:

• Rework job functions.
• Design and retrofit equipment, tools, and workplace layouts.
• Improve training and education.

Using data to analyze workforce and major workers’ compensation cost drivers can also help you differentiate your risk profile to insurance carriers at renewal. Insurers have been using data and analytics for their own benefit to help identify best-in-class risks and to price coverage appropriately for years. If you’re able to demonstrate that you have a firm understanding of your key risks and have implemented loss prevention and mitigation plans to address them, you can stand out from your peers in a competitive market — and help drive more competitive premiums.

To learn more about the use of analytics in reducing costs, please join Marsh’s Workers’ Compensation Center of Excellence on September 16 for our webcast, Creating Safer Workplaces and Reducing Costs through Predictive Analytics and Technology.