Once upon a time, the price of a business insurance policy was determined, in large part, by an underwriter’s assessment of an organization’s risk. The underwriter would weigh the perceived risk the insurance carrier was taking on and determine premiums for taking on that risk. But as more and more data becomes digitized, data is playing a more central role in insurance carriers’ determinations — not only in pricing a risk, but whether to even take on the risk in the first place. Enter predictive modeling.
Predictive modeling is essentially applying probability and statistics to data with thousands of variables to ultimately determine which variables correlate to an impact in risk and predict a particular organization’s risk profile. As a result, insurance carriers are now armed with the variables that statistically show whether your risk is more likely to result in a loss and can price premiums accordingly. And all of this information is determined before the underwriters even gets their hands on it.
Every insurance carrier has its own algorithm and mix of variables taken into consideration, which can also vary by line of insurance. Some of these variables can be controlled, while others cannot. For example, predictive modeling for workers’ compensation insurance may look at the location of the organization. The location, in turn, determines the geographical area from which you attract employees and then data on claims occurrences for that geographical area is taken into consideration. While you may be able to physically pick up your business and move it, that’s unlikely to happen — and certainly not before your next renewal — making this variable hard to control. Some of the variables are intuitive — such as whether you have a return-to-work policy — while other variables are not as intuitive — such as annual stewardship reporting from your insurance broker.
“It’s important for companies to understand the role of predictive modeling,” says Tom Boudreau, property/casualty practice group leader at Associated Benefits and Risk Consulting. “But even more importantly, they take action to ensure they get credit for all the variables they might have in place that would come out less risky in predictive modeling.” For example, Boudreau’s team frequently asks clients and prospects for a slew of documents, including employee handbooks, loss runs, policies, and contracts.
The team analyzes the information and comes up with recommendations. There will be variables in the organization’s favor and it is important that these are communicated to an underwriter so proper credit can be provided. And then there may variables that don’t exist or are not in the organization’s favor. Where the organization may be lacking, a plan is developed to increase those variables in its favor. “Those things could be relatively low cost, like a drug testing policy for work comp or mobile device policy for auto, which we can work with our internal HR Solutions team to draft," says Boudreau. "Or they can range all the way to the incredibly expensive, like having the entire building covered in sprinklers. We can tackle the ‘low-hanging fruit’ first and then develop priorities to try to accomplish as much as we can in the next three to five years that will make an impact.”
Predictive modeling is already being used extensively in workers’ compensation and auto insurance policies. Understanding the variables the insurance carriers use to evaluate your insurance risk is important information to help you impact your ultimate cost.
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Rebecca advises employers on leave policies, accommodations, discrimination and early intervention with workers’ compensation claims. While in private practice, she focused on defending workers’ compensation claims and handling Medicare-related issues arising from those claims. Rebecca received her
Rebecca advises employers on leave policies, accommodations, discrimination and early intervention with workers’ compensation claims. While in private practice, she focused on defending workers’ compensation claims and handling Medicare-related issues arising from those claims. Rebecca received her Bachelor of Business Administration degree with a major in human resource management from the University of Wisconsin-Eau Claire and received her law degree from Marquette University Law School.
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