PointRight explains how strong data analytics can help captive managers build a profitable healthcare captive, even in a soft market.
Building a profitable healthcare provider captive to finance liability risk in a hard insurance market is relatively easy. In a hard market, even good risks must pay high premiums in the commercial market, so a captive can attract quality risks at premiums high enough to cover the losses of riskier members. However, in a soft market, providers with clean loss histories and excellent risk management often can find commercial coverage at rates below the captive’s standard rate. Without offering discounts to the best risks on renewal, a captive can find itself with a portfolio of unattractive risks with insufficient premium to cover the losses that will inevitably occur.
As such, a soft market forces a captive to offer different rates to different providers. Once the principle of a single rate for all members is violated, it makes sense for the captive to raise its rates—or impose significant deductibles—for members that are poor risks. Since such members will usually be able to find commercial coverage in a soft market, it is more feasible to ease a member out of the captive than it would be in a hard market where the member could be left with no coverage at all.
Quantifying risk for effective rates
Successful differential rate-setting requires a captive manager to quantify facilities’ differences in risk, give discounts and add surcharges. In a soft market, discounts may need to be greater than 50 percent to keep the best risks, while surcharges may need to exceed 100 percent to cover fully the expected losses of the worst risks. It is difficult for a captive manager to implement and justify such large deviations if the underwriting process has a large subjective element. Even though there is general agreement among captive managers and members about what factors make a healthcare provider have a higher or lower risk of a professionalliability suit, there is no consensus at all about the relative importance of each risk factor and how to combine risk factors to develop a loss cost estimate.
Imagine the difficulty of selling a 50 percent discount for a member with a recent loss, but a low risk of another loss; or a 100 percent surcharge for a member with a clean loss history, but a high risk of a future loss.
The captive manager who wants to differentiate rates aggressively to the level needed for success in an unstable soft market needs a quantitative risk model with ‘major league lift’. This is the kind of model where the expected loss cost for a facility assessed as highrisk will be more than 10 times the expected loss for one assessed as low-risk. Fortunately for captives that insure skilled nursing facilities in the US, such models exist as a result of the extraordinary richness of public domain data on all nursing homes that accept Medicare or Medicaid money. These data comprise a wide range of variables that mediate professional liability risk, including patient population structure, facility ownership, staffing ratios, processes and outcomes of clinical care, results of health department inspections, and consumer and employee complaints.
Model-based risk stratification
Since 2004, PointRight Inc, a data and analytics company specialising in the long-term care industry, has linked public domain data on nursing homes with an extensive database of professional liability loss histories extracted from captive, commercial carrier and third party audit loss runs. Based on these data, PointRight constructs statistical models that explain more than half of the variability in severe professional liability claims and enable risks to be classified into low-risk, average-risk and high-risk groups that are large enough for actuarially credible estimates of expected loss costs. In most regions of the US, the expected loss cost for highrisk nursing homes is more than 10 times that for low-risk facilities. PointRight models identify facilities that are low-risk despite recent losses, and those that are high-risk despite clean loss histories.
Captive managers can apply model-based risk stratification in the following ways to improve profitability and sustainability of the captive’s portfolio:
1. Offer aggressive discounts to low-risk facilities—greater than 50 percent if necessary to retain a desirable member
2. Impose surcharges or deductibles on high-risk facilities that cover expected loss costs or encourage the member to find coverage elsewhere
3. For both average-risk and high-risk facilities, identify factors that the facility can change to lower their future risk, which allows the captive manager to focus risk management dollars and human resources on the specific facilities and risk factors that will have the most impact to reduce future losses.
Benefits of quantitative risk models
Most facilities have opportunities for improvement that do not involve costs that are unrealistic in the current reimbursement environment. Calling attention to these opportunities is a valuableservice to all members; heeding the call could be considered as a requirement for retention for the highest-risk members.
"Successful differential rate-setting requires a captive manager to quantify facilities' differences in risk, give discounts and add surcharges."
Because PointRight models use public domain data already in its hands and loss history data already in the captive’s hands, a complete risk assessment for a single facility can be completed in minutes. The touch of an expert risk analyst is needed only to verify the reasonableness of the input variables—adjusting them if necessary for errors or recent changes—and to reach a conclusion about the most promising opportunities for short-term risk reduction. As a result, multi-facility members up for renewal can be evaluated in less than a day, and the captive can respond quickly and intelligently to a commercial carrier’s lowball bid to ‘poach’ a low risk chain that has been providing a significant portion of the captive’s cash flow.
The improvement in loss ratios that can be expected from implementing model-based rate-setting will depend on the competitiveness of the local insurance market, the loyalty of low-risk members, and the power of high-risk members to influence captive policy given the particular captive’s ownership and governance. Loss ratio improvements of 10 percent or more are attainable if member buy-in can be attained. While there is a cost incurred in outsourcing part of the underwriting process, this cost is offset not only by lower loss costs, but also by savings on the internal cost of underwriting. Commercial carriers supported by ongoing analytic services have been able to grow their books of business rapidly without adding any additional underwriting staff. Captives can expect similar efficiencies.
Mike Walton, executive vice president and healthcare practice leader for AmWINS, has promoted and used PointRight services for measuring and managing risk from underwriting through claims handling since 2001. “Group captives and single parent captives can both benefit from understanding the source of risk and the expected cost that a facility contributes to the captive,” said Walton.
“What’s different about PointRight is that it not only delivers a method of measurement that is easily understood, it also provides tools that help improve and monitor the changes in risk and resident care. We’ve introduced and encouraged our customer base to take advantage of all that PointRight has to offer, and providing PointRight insight is an example of how AmWINS offers more to its customers.”
Analytics at work
The following is an example of how PointRight identified an actual captive’s loss experience.
The challenge: A multi-state captive insurer providing professional liability coverage to 60 skilled nursing facilities charged all of its members a single rate of $750 per bed, based on a historical loss of $610 per bed—a loss ratio of 81 percent. The captive manager was concerned about adverse selection because members with clean loss histories were being wooed by commercial carriers offering them rates of $600 per bed.
The solution: PointRight undertook an analysis of the previous five years’ loss experience, which comprised 229 facility-years of exposure. The facilities were stratified into low, average, and high-risk groups based on the application of a PointRight predictive model of claim severity. In this historical study facilities could change risk groups from year to year when their risk factors changed. PointRight found marked differences in loss experience between the risk groups (see table below).
The results: The manager found that among the current 60 members there were 12 high-risk, 12 low-risk and 36 averagerisk facilities. The following chart shows the risk level of select facilities in the group and how risk changed over time.
High-risk members were offered a premium on renewal of $1100 per bed; all other members were offered a premium of $550 per bed. Faced with these new premiums the low- and average-risk members remained in the captive. The six high-risk members with clean loss histories found coverage elsewhere, while the six with recent losses renewed at the higher premium as it was lower than the rates available to them in the commercial market.
Renewal premiums were (12 x 62 x $550) + (36 x 95 x $550) + (6 x 132 x $1100) = $3,161,400. The expected losses on the renewal book were (12 x $8837) + (36 x $44,010) + (6 x $153,605) = $2,612,034. The expected loss ratio was 83 percent. Thus, this captive could expect to remain successful in the soft market.
By contrast, imagine this captive had maintained its rates at $750, and in this competitive market lost 24 of its 48 low- and averagerisk facilities to commercial carriers quoting $600 per bed, and three of its high-risk facilities (ones with clean loss histories in less litigious counties, perhaps). Its renewal premiums would drop to $2,452,500 while its expected losses would be $2,227,647, for an expected loss ratio of 91 percent. In addition to the higher loss ratio, the captive’s fixed expenses would be spread across a smaller base. In the first instance the captive remains viable, in the second it is likely to lose money.
Analytics make soft markets profitable
Hard markets offer obvious opportunities to captives, but soft markets offer opportunities as well. In particular, soft markets offer captives the chance to offload some of their least attractive risks to the commercial market. High-risk facilities with clean loss histories might find commercial policies that cost less than captive coverage that includes a surcharge. If they do, the facility and the captive are both winners.
Mary Chmielowiec, executive vice president, Insurance at PointRight. She can be contacted at: firstname.lastname@example.org
Barry Fogel, founder and executive vice president at PointRight. He can be contacted at: email@example.com
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