A special “thank you” to Mark Wells for making this blog a “manager’s choice” on LinkedIn’s Work Comp Analysis Group.
If predictive modeling is not already influencing your premiums, chances are it will soon.
Predictive modeling is a statistical approach that looks at traditional variables, such as employer’s history of losses, and adds non-traditional factors, such as the average salary of employees. Just as credit scoring is a proven risk indicator for personal automobile coverage, considering a company’s financial health can indicate the degree of workers’ compensation risk.
There’s a lot employers need to know about predictive modeling.
1) Premiums will more accurately reflect each employer’s specific experience. Predictive modeling is a more finely-tuned instrument than traditional pricing approaches. That’s because it gives weight to new and traditional factors.
This is great news for progressive employers doing everything they can to foster a safe and healthy workforce, boost overall morale and help injured workers return to work. Efforts such as more early claims reporting, which helps employees get better medical treatment and heal faster, are more quantifiable through predictive modeling and can positively affect premiums.
Conversely, employers who view comp as a price of doing business are in for unpleasant surprises. They could very well find themselves in the high risk pool.
Traditionally, workers’ compensation insurers priced on a “univariate” classification basis. This means insurers would look collectively at employers and their employees in the same class. An employer’s experience modification factor is then used to reflect its actual risk history.
Conversely, employers who view comp as a price of doing business
are in for unpleasant surprises.
Another factor can be the affordability and quality of health insurance coverage. This can determine the incentives for cost shifting medical treatment to workers’ compensation. Healthier employees tend to recover faster, which shortens the duration of workers’ compensation claims. Therefore, initiatives taken to improve employee health, such as wellness programs and disease management, which can help reduce co-morbidities such as diabetes, smoking and obesity, are also possible factors.
Other employer-specific factors that can be fed into predictive models including payment history, loss control initiatives, age of company, average age of workers and length of employment because these have been shown to affect outcomes. Income levels of employees can be an indicator of employee morale based on the assumption that better compensated workers are happier.
Personally, I am excited! Predictive modeling will give employers even more financial incentive to pay attention to employee health and safety on and off the job. That can only be a good thing.
Be the first to know by clicking on the “follow” button on the bottom right corner.