The real goal of predictive analytics for self-insureds is to help guide and support risk managers’ decisions. Predictive analytics can be applied to both ‘pre-claim’ and ‘post-claim’ loss prevention methods. To aid in claim prevention, pre-claim-focused analyses are used to highlight high-risk (high-cost) employees. The loss costs of various groups are compared to each other, a company average, and to average industry loss costs provided by the National Council on Compensation Insurance (NCCI). Loss categories higher than company or NCCI averages get closer examinations for loss drivers and mitigation strategies. A higher-than-average cost for newly hired employees may signal a need for more training. A higher-than-industry cost for claims in certain states may be noted. A particular type of injury may emerge as the most costly. The potential savings can be estimated as the difference between the current loss costs and the benchmark loss costs, times the percentage of employees or expected claims involved. For example, a self-insured entity may know that its newly hired employees experience a larger proportion of losses than employees with longer tenure. If it conducts an analysis and discovers that low-wage employees working in Illinois with less than six months of experience have substantially higher costs than the average employee, claim prevention resources could be specifically aimed at that employee demographic to control costs. Savings Opportunities A notable benefit of predictive analytics is that it provides quantitative cost-saving information to risk managers. Continuing with the prior example, assume 2,500 employees are newly hired, low-wage employees in Illinois and their average costs have been shown to be three times higher than the company average of USD $1.50/$100 of payroll. We can estimate that a reduction from $4.50 to $1.50 could create $2.25 million in savings. Asking senior management for $100,000 for more new hire training in Illinois facilities will be much easier with the quantitative support provided by predictive analytics. (2,500 employees with an average payroll of $30,000 save $3 = 2,500 x 30,000/100 x 3 = $2.25M) Not only can predictive analytics assist with reducing cost ‘pre-claim’ by focusing on exposure, it can also reduce costs once a claim has occurred. Knowing the easy-to-identify large claims will be second nature to risk managers, however, ‘post-claim’ predictive analytics can look into claim development details to find characteristics that late-developing, problematic claims (and often not the obvious large ones) have in common. After a loss has occurred, one of the most effective ways to manage costs is to involve a very experienced claims handler as soon as possible. The results of effective ‘post-claim’ predictive analyses will assist in implementing cost-saving claims triage. Because the best resource post-claim is good claim management, predictive analytics can get late-developing, problematic claims the timely attention they need to contain the ultimate costs or even settle the claim. Loss savings based on predictive analyses extend beyond claim cost reduction. Being able to quantitatively show potential savings and concrete mitigation plans will make a positive impression on senior company management and excess insurance carriers. Demonstrating shrewd knowledge of the loss drivers and material plans to reduce the losses can aid in premium negotiations with excess carriers for all future policy years. And if the insurer or state is holding any collateral, the predictive analytics' results can be used by the self-insured in negotiating. The key to unlocking further potential cost savings in your self-insured plan is readily available in your own data. Predictive analytics is the tool that will help risk managers make better claim reduction decisions and produce actionable items with real cost savings now and in the future. Risk managers and self-insured companies can look forward to possible benefits such as loss cost reductions along with reductions in excess premium and collateral, and quantitative information to help them with budgeting and allocation. As more self-insureds begin applying predictive analytics to control costs, companies that are not using these tools will be at a competitive disadvantage. For more information on predictive analytics, watch this video of a Google hangout with Michael Paczolt and Terry Wade of Milliman, Inc.:Predictive Analytics for Self-Insureds
Predictive analytics is the tool that will help risk managers make better claim reduction decisions and produce actionable items with real cost savings now and in the future. |
The real goal of predictive analytics for self-insureds is to help guide and support risk managers’ decisions. Predictive analytics can be applied to both ‘pre-claim’ and ‘post-claim’ loss prevention methods. To aid in claim prevention, pre-claim-focused analyses are used to highlight high-risk (high-cost) employees. The loss costs of various groups are compared to each other, a company average, and to average industry loss costs provided by the National Council on Compensation Insurance (NCCI). Loss categories higher than company or NCCI averages get closer examinations for loss drivers and mitigation strategies. A higher-than-average cost for newly hired employees may signal a need for more training. A higher-than-industry cost for claims in certain states may be noted. A particular type of injury may emerge as the most costly. The potential savings can be estimated as the difference between the current loss costs and the benchmark loss costs, times the percentage of employees or expected claims involved. For example, a self-insured entity may know that its newly hired employees experience a larger proportion of losses than employees with longer tenure. If it conducts an analysis and discovers that low-wage employees working in Illinois with less than six months of experience have substantially higher costs than the average employee, claim prevention resources could be specifically aimed at that employee demographic to control costs. Savings Opportunities A notable benefit of predictive analytics is that it provides quantitative cost-saving information to risk managers. Continuing with the prior example, assume 2,500 employees are newly hired, low-wage employees in Illinois and their average costs have been shown to be three times higher than the company average of USD $1.50/$100 of payroll. We can estimate that a reduction from $4.50 to $1.50 could create $2.25 million in savings. Asking senior management for $100,000 for more new hire training in Illinois facilities will be much easier with the quantitative support provided by predictive analytics. (2,500 employees with an average payroll of $30,000 save $3 = 2,500 x 30,000/100 x 3 = $2.25M) Not only can predictive analytics assist with reducing cost ‘pre-claim’ by focusing on exposure, it can also reduce costs once a claim has occurred. Knowing the easy-to-identify large claims will be second nature to risk managers, however, ‘post-claim’ predictive analytics can look into claim development details to find characteristics that late-developing, problematic claims (and often not the obvious large ones) have in common. After a loss has occurred, one of the most effective ways to manage costs is to involve a very experienced claims handler as soon as possible. The results of effective ‘post-claim’ predictive analyses will assist in implementing cost-saving claims triage. Because the best resource post-claim is good claim management, predictive analytics can get late-developing, problematic claims the timely attention they need to contain the ultimate costs or even settle the claim. Loss savings based on predictive analyses extend beyond claim cost reduction. Being able to quantitatively show potential savings and concrete mitigation plans will make a positive impression on senior company management and excess insurance carriers. Demonstrating shrewd knowledge of the loss drivers and material plans to reduce the losses can aid in premium negotiations with excess carriers for all future policy years. And if the insurer or state is holding any collateral, the predictive analytics' results can be used by the self-insured in negotiating. The key to unlocking further potential cost savings in your self-insured plan is readily available in your own data. Predictive analytics is the tool that will help risk managers make better claim reduction decisions and produce actionable items with real cost savings now and in the future. Risk managers and self-insured companies can look forward to possible benefits such as loss cost reductions along with reductions in excess premium and collateral, and quantitative information to help them with budgeting and allocation. As more self-insureds begin applying predictive analytics to control costs, companies that are not using these tools will be at a competitive disadvantage. For more information on predictive analytics, watch this video of a Google hangout with Michael Paczolt and Terry Wade of Milliman, Inc.:
