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Getting Hitched Without the Hitch

When things go wrong with a wedding, they can go really wrong:

Valentine’s Day is the traditional end to what is known in the wedding blogosphere as “engagement season.” These engagements tend to last just over a year, averaging 14.5 months, according to theknot.com. Those 14.5 months are a whirlwind of activity during which couples are setting their date, working on guest lists and putting down deposits to ensure that everything goes smoothly on the big day.

But what if there is trouble in paradise—and someone calls off the wedding? Or weather prevents the parents of the groom from making it to the ceremony? Or the venue closes? Or the photographer gets lost? Or the caterer doesn’t show up? Or a drunk uncle damages property at the reception hall? What happens then?

See also: A Closer Look at the Future of Insurance  

The average wedding in the U.S. costs $35,329 (ranging from $12,769 in Mississippi to $88,176 in Manhattan). Pulling off a typical wedding involves a lot of variables–which all introduce the possibility of financial loss. There are multitudes of vendors: venue, caterer, baker, musician, florist, officiant, bridal salon, hair stylist, make-up artist and photographers to name a few, all of which will likely require a deposit. On the day itself, inclement weather could keep important guests from arriving or could even postpone the wedding. Finally, as with most social events that typically serve alcohol, guest behavior can cause unpredictable property damage.

For such an important life event, at such a high price point, it’s worth protecting your investment. Many insurance companies have wedding liability products to help. Wedding insurance can combine a number of different coverages and can range from only $95 to $500 depending on the types and level of coverage provided. Wedding insurance is easy to purchase online (or over the phone). For example, Travelers offers a Wedding Protector Plan and has a quiz to help gauge the riskiness of your wedding. Other insurers, such as WedSafe and Wedsure, also make it easy to find a quote and buy wedding insurance online.

The most commonly selected wedding coverage is liability coverage. This is typically purchased in situations where the selected venue requires the couple to cover property damage and bodily injury. In addition, certain venues may require the purchase of liquor liability coverage to protect against any alcohol-related incidents.

In the event of a necessary cancellation or postponement, financial losses can be mitigated by cancellation/postponement coverages. Massive amounts of rain and snow can cancel flights, close roads and even damage or close venues. A severe illness or injury could befall the couple or a parent, grandparent, child or officiant. Sudden military deployments can also cause wedding cancellations. All of these are “necessary” cancellations/postponements, and insurance exists to protect against any financial losses they may cause.

Some wedding insurance products will also protect against problems with the venue or other vendors going out of business, or vendors arriving late-or not arriving at all. Typically, the policy would reimburse the deposits, and, if alternate vendors can be arranged, the unexpected expenses incurred by the couple to avoid a full cancellation or postponement may also be covered.

Wedding insurance purchasers should be sure to check if a prospective policy will cover a subsequently canceled or postponed honeymoon, as well.

Additional wedding insurance provisions may include coverages for wedding attire, gifts and photography/videography. Attire coverage will pay to replace (or repair) any loss or damage occurring before the wedding or to reimburse a reasonable market value for any damage occurring after the wedding. This would cover, for example, airlines losing luggage with the wedding attire or the bridal salon going out of business before the wedding dress was delivered.

Gift coverage will reimburse the couple for loss or damage to wedding gifts before, during and after the wedding while at home, at the wedding or in transit. This would cover any physical damage to gifts while on display at the wedding or a theft of non-monetary gifts.

With respect to photography coverage, loss events can range from the contracted photographer not showing up, cameras being stolen (along with the film/digital memory card) or defective film/memory card use. This coverage excludes photographs not meeting expectations but does cover the costs of reconvening your wedding party for “do over” photographs or even a retaking of the official video at a restaging with the principal participants–including new flowers and a new wedding cake.

Not only can the cancellation or postponement of such an important event be monetarily taxing, but it can also be emotionally taxing. Some wedding insurance will even cover professional counseling (if recommended by a physician) for as long as a year.

All insurance policies have exclusions, and wedding insurance is no different. Engagement rings aren’t covered, but wedding bands are. Other common exclusions include anything asbestos- or lead-related, any abuse/molestation/harassment/sexual conduct (alcohol-fueled or not), fireworks, war, nuclear, neglect or any intentional loss.

And, no, for the most part, wedding insurance will not cover cancellations due to a “change of heart” on the part of the bride or groom; cold feet do not count as a trigger for this insurance.

See also: A Wedding’s Lessons on Customer Insight  

One insurer, Wedsure, will reimburse any “innocent party financiers, other than the bride or groom, if the wedding is canceled due to a Change of Heart by the bride or groom, 365 days or more from the date of the first covered event” [emphasis added]. However, because the average engagement length is only 2.5 months longer than this, it’s unlikely that there are many qualifying losses under this coverage.

Planning the perfect wedding can be stressful and expensive. The typical wedding costs more than the average mid-size car, and just as many things can go wrong with it. Purchasing wedding insurance can help relieve the additional stress of worrying about what happens when something goes wrong. It won’t do anything, though, about those cold feet.

Novel Solution for Driverless Risk

The route to a fully autonomous vehicle market seems long and fitful in the eyes of many. But it is likely to become a reality faster than many are prepared to accept. Like IBM, Kodak and many other companies once confronted with a rapidly changing market, we, too, now face disruptions in the auto market, perhaps unlike any since the invention of the auto. As liability increasingly shifts from the human driver to systems and software – a trend highlighted by recent reports of the first autonomous fatality – original equipment manufacturers (OEM) will come to the forefront as primary holders of automobile-related insurance risk. How they manage this risk will help determine the success and acceptance of the autonomous vehicle market in the years to come.

A new age

Skeptics of an early adoption of fully autonomous vehicles have a point. In their short history, autonomous vehicles have faced a wide array of challenges including skittish maneuvering ability in wet weather, gaps in infrastructure, regulatory and legal shortcomings, market acceptance, risk of hacking, consumers’ privacy and ethical choices. The list goes on, but so do advances in technology.

There are dozens of advances such as braking assistance, blind spot detection, pre-collision warning systems, electronic stability control and vehicle-to-vehicle communication that have been adopted over the years or are now making their way into the latest models. These technologies have been largely accepted and often embraced by consumers who have come to view them as something more than just a convenience.

See also: Connected Vehicles Can Improve Claims  

In fact, few dispute the potential safety advantages of fully self-driving cars. Active safety systems that eliminate the human element from the driving equation have already been shown to prevent accidents. According to the Insurance Institute for Highway Safety (IIHS), automatic braking can reduce rear-end crashes by 40%, and front collision warning systems can lower rear-end accidents by 23%.1 But this is just the tip of the iceberg: 94% of auto accidents are caused by human errors such as speeding, driving under the influence and driver inattention, according to a 2015 survey by the National Highway Transportation Safety Administration.2

The U.S. market is expected to see several thousand autonomous vehicles sold in 2020, which will grow to nearly 4.5 million vehicles sold in 2035, according to IHS Automotive forecasts, an industry research firm.3 The slow methodical 11-year turnover in U.S. car ownership is likely to fall by the wayside as convenience or safety features entice consumers to purchase a self-driving car sooner than they would otherwise do. These early purchasers could be setting up a cycle of more rapid adoption as car buyers decide to forgo the thrill or pleasure of driving for the safety of their families and the ability to be more productive (or just catch up on sleep and social media). Further, there may be no need for car ownership at all in a new shared economy including on-demand autonomous shuttles.

Shifting responsibilities

Assessing liability in the near future will admittedly be a tricky matter as a mix of driving modes, ranging from no autonomy to full autonomy, populate the roadways. Accidents that involve human driver to human driver will morph into dozens of combinations of human drivers with various levels of semi-autonomous drivers and eventually fully autonomous cars. Questions of liability will need to sort out not only the comparative negligence of a human operator’s actions but also the capability of software and sensors. As the ever-diminishing role of human drivers gives way to the rise of autonomous vehicles, the importance of personal auto insurance will likewise be replaced by product liability.

Google, Mercedes and Volvo have already said they will accept responsibility for accidents that are caused by malfunctions in the technology in their cars, a move welcomed by federal regulators that see the commitment as a way to smooth the introduction of vehicles with these new technologies. While these carmakers’ pledges may, in fact, be redundant, they are a harbinger of the shift in demand for product liability.

But carmakers’ step up in accountability is only one link in the manufacture of autonomous vehicles, which can involve dozens of suppliers for software, systems and devices which enable the positioning data and predictive response algorithms to be accurate and effective. Enhanced sensing and response time capabilities will drive new demands on hardware and software performance. How will liability be spread among potentially dozens of interlocking but legally separate entities?

See also: Plunging Costs for Autonomous Vehicles  

Currently, as part of the general purchasing conditions, the supplier will indemnify and hold the manufacturer harmless from and against any and all loss, liability, cost and expense arising out of a claim that a defect in the design or manufacture of the product caused personal injury or damage to property. However, suppliers are not always completely responsible for the design or validation of the components they provide, but rather can be directed by the carmaker to either model or test the component according to the carmaker’s predetermined specifications. Thus, the parties may have a shared financial burden of failure and need to negotiate the consequences at project inception. The process of assigning responsibility and managing indemnification often involves a team of resources that do not contribute to the carmakers’ underlying business function of making people mobile.

This relationship is likely to evolve as the importance of the car’s electronic control unit (ECU) grows ever more critical as the brain center for programming features that ultimately determine how the car responds. Even now, validating software code – a function paramount in detecting errors – is less defined as compared with hardware. How the validation process will evolve under all possible control scenarios is extremely difficult to imagine. But one change in the process is becoming clear: As the software algorithms become more integral to the success and failure of autonomous vehicles, carmakers have started to keep a tight rein on the integration of software and hardware. As willing as carmakers may be to absolve consumers of the responsibility for accidents that stem from the fault of their technology, they are unlikely to extend a similar courtesy to their suppliers. And why should they if the cause of the accident can be traced to a supplier’s defective sensor or software?

Nevertheless, untangling the web of responsibility can be a distraction from the business focus and could become an impediment to progress. What is a relatively well-established practice in other fields for passing the liability down the supply chain to the source of the failure is likely to become much more complicated and nuanced in the realm of autonomous vehicles as cars become increasingly dependent on an integration of sophisticated technologies.

Likewise, the ways in which risk is shared under product liability are likely to be increasingly difficult to manage. In an autonomous world, the insurance program would ideally be structured such that suppliers not only have skin in the game but also have a more transparent line of sight to the cost they are contributing to the potential liability. The question the industry needs to ask is: Is there a better way to share the cost of risk among the carmaker and its suppliers reflecting the shifted responsibility?

Enter a SPLASh pool

One option is to create an insurance pool for each autonomous carmaker. Under a Supplier Product Liability Autonomous Share (SPLASh) pool, the carmaker would assume all the product liability risk for accidents stemming from the autonomous technology and cede the risk to the SPLASh pool. To be viable, all suppliers – or “swimmers” – along with the carmaker would need to participate in the pool, which would operate as a funding vehicle for the risk. Each year, the pool would be funded commensurate with the expected losses, and losses would be paid directly from the fund, eliminating the manufacturer’s role of managing indemnification from the suppliers.

Like more traditional risk pools used by a range of organizations from public entities that share their law enforcement exposure to a group of hospital systems that manage their professional liability risk, a SPLASh pool would also have a management function, presumably overseen by the manufacturer, as well as various insurance-type functions from actuaries, to calculate the premium and reserves; claims handlers (internal or outsourced) to pay and manage claims; and lawyers to interpret coverage, among others. In this way, autonomous technology may be paving a new road but with the experience and insight of well-traveled insurance professionals who understand the different approaches to managing risk.

Funding would reflect the supplier’s risk profile with low risk suppliers like those that provide cameras for parallel parking – the minnows of the pool – paying less than high risk “whale” suppliers such as a software developer. The pool can be structured according to frequency and severity of risk. Such an arrangement could consist of all pool members participating in a structure where more frequent, low-severity claims are grouped (Fund A) separately from less frequent, high-severity claims (Fund B), both meeting risk transfer.

Each fund would have per occurrence loss limits and require member contributions based on actuarial projections, perhaps at first based on fault rates from engineering systems output, until credible loss data develop. Various features such as aggregate limits, loss ratio caps, overflow between funds and member assessments can be used to tailor the insurance coverage with a clear desired outcome – to motivate innovators to develop quality products.

See also: Here Comes Robotic Process Automation  

The arrangement builds in a high level of transparency as suppliers with bad loss performance would be required to contribute more to Fund B than others. Moreover, consistently poor swimmers could be replaced by suppliers with better performance.

This concept blends well with the current warranty programs offered by car manufacturers. Like those programs offered today, dealers provide details of new and used warranty programs available to the consumer, covering defects in material or workmanship for 48 months or 50,000 miles, whichever comes first, for example. The carmaker would budget a certain amount of costs toward warranty replacement and then track the records and claims to more accurately predict future replacement costs as well as pinpoint components that are failing, assuming that the problem can be isolated. If costs are higher than expected (outside of the normal failure rate), the manufacturer can push further costs to the supplier at the source or remove them from the assembly line altogether.

Buckle up

A SPLASh pool can pave the way to managing carmakers’ risk in the future. The product liability exposure from autonomous vehicles shouldn’t be a roadblock to the increased safety and mobility that self-driving cars can bring to millions of people. The insurance industry will need to demonstrate its creativity and foresight in managing risk to keep innovation on the right track.

11 Questions for Ron Goetzel on Wellness

We thank Ron Goetzel, representing Truven Health and Johns Hopkins, for posting on Insurance Thought Leadership a rebuttal to our viral November posting, “Workplace Wellness Shows No Savings.” Paradoxically, while he conceived and produced the posting, we are happy to publicize it for him. If you’ve heard that song before, think Mike Dukakis’s tank ride during his disastrous 1988 presidential campaign.

Goetzel’s rebuttal, “The Value of Workplace Wellness Programs,” raises at least 11 questions that he has been declining to answer. We hope he will respond here on ITL. And, of course, we are happy to answer any specific questions he would ask us, as we think we are already doing in the case of the point he raises about wellness-sensitive medical events. (We offer, for the third time, to have a straight-up debate and hope that he reconsiders his previous refusals.)

Ron:

(1)    How can you say you are not familiar with measuring wellness-sensitive medical events (WSMEs), like heart attacks? Your exact words are: “What are these events? Where have they been published? Who has peer-reviewed them?” Didn’t you yourself just review an article on that very topic, a study that we ourselves had hyperlinked as an example of peer-reviewed WSMEs in the exact article of ours that you are rebutting now? WSMEs are the events that should decline because of a wellness program. Example: If you institute a wellness program aimed at avoiding heart attacks, you’d measure the change in the number of heart attacks across your population as a “plausibility test” to see if the program worked, just like you’d measure the impact of a campaign to avoid teenage pregnancies by observing the change in the rate of teenage pregnancies. We’re not sure why you think that simple concept of testing plausibility using WSMEs needs peer review. Indeed, we don’t know how else one would measure impact of either program, which is why the esteemed Validation Institute recognizes only that methodology. (In any event, you did already review WMSEs in your own article.) We certainly concur with your related view that randomized controlled trials are impractical in workplace settings (and can’t blame you for avoiding them, given that your colleague Michael O’Donnell’s journal published a meta-analysis showing RCTs have negative ROIs).

(2)    How do you reconcile your role as Highmark’s consultant for the notoriously humiliating, unpopular and counterproductive Penn State wellness program with your current position that employees need to be treated with “respect and dignity”? Exactly what about Penn State’s required monthly testicle check and $1,200 fine on female employees for not disclosing their pregnancy plans respected the dignity of employees?

(3)    Which of your programs adhere to U.S. Preventive Services Task Force (USPSTF) screening guidelines and intervals that you now claim to embrace? Once again, we cite the Penn State example, because it is in the public domain — almost nothing about that program was USPSTF-compliant, starting with the aforementioned testicle checks.

(4)    Your posting mentions “peer review” nine times. If peer review is so important to wellness true believers,  how come none of your colleagues editing the three wellness promotional journals (JOEM, AJPM and AJHP) has ever asked either of us to peer-review a single article, despite the fact that we’ve amply demonstrated our prowess at peer review by exposing two dozen fraudulent claims on They Said What?, including exposés of four companies represented on your Koop Award committee (Staywell, Mercer, Milliman and Wellsteps) along with three fraudulent claims in Koop Award-winning programs?

(5)    Perhaps the most popular slide used in support of wellness-industry ROI actually shows the reverse — that motivation, rather than the wellness programs themselves, drives the health spending differential between participants and non-participants. How do we know that? Because on that Eastman Chemical-Health Fitness Corp. slide (reproduced below), significant savings accrued and were counted for 2005 – the year before the wellness program was implemented. Now you say 2005 was “unfortunately mislabeled” on that slide. Unless this mislabeling was an act of God, please use the active voice: Who mislabeled this slide for five years; where is the person’s apology; and why didn’t any of the analytical luminaries on your committee disclose this mislabeling even after they knew it was mislabeled? The problem was noted in both Surviving Workplace Wellness and the trade-bestselling, award-winning Why Nobody Believes the Numbers, which we know you’ve read because you copied pages from it before Wiley & Sons demanded you stop? Was it because HFC sponsors your committee, or was it because Koop Committee members lack the basic error identification skills taught in courses on outcomes analysis that no committee member has ever passed?

wellness-article

(6)    Why doesn’t anyone on the Koop Committee notice any of these “unfortunate mislabelings” until several years after we point out that they are in plain view?

(7)    Why is it that every time HFC admits lying, the penalty that you assess — as president of the Koop Award Committee — is to anoint their programs as “best practices” in health promotion? (See Eastman Chemical and Nebraska in the list below.) Doesn’t that send a signal that Dr. Koop might have objected to?

(8)    Whenever HFC publishes lengthy press releases announcing that its customers received the “prestigious” Koop Award, it always forgets to mention that it sponsors the awards. With your post’s emphasis on “the spirit of full disclosure” and “transparency,” why haven’t you insisted HFC disclose that it finances the award (sort of like when Nero used to win the Olympics because he ran them)?

(9)    Speaking of “best practices” and Koop Award winners, HFC’s admitted lies about saving the lives of 514 cancer victims in its award-winning Nebraska program are technically a violation of the state’s anti-fraud statute, because HFC accepted state money and then misrepresented outcomes. Which is it: Is HFC a best practice, or should it be prosecuted for fraud?

(10)    RAND Corp.’s wellness guru Soeren Mattke, who also disputes wellness ROIs, has observed that every time one of the wellness industry’s unsupportable claims gets disproven, wellness defenders say they didn’t really mean it, and they really meant something else altogether. Isn’t this exactly what you are doing here, with the “mislabeled” slide, with your sudden epiphany about following USPSTF guidelines and respecting employee dignity and with your new position that ROI doesn’t matter any more, now that most ROI claims have been invalidated?

(11)    Why are you still quoting Katherine Baicker’s five-year-old meta-analysis claiming 3.27-to-1 savings from wellness in (roughly) 16-year-old studies, even though you must be fully aware that she herself has repeatedly disowned it and now says: “There are very few studies that have reliable data on the costs and benefits”? We have offered to compliment wellness defenders for telling the truth in every instance in which they acknowledge all her backpedaling whenever they cite her study. We look forward to being able to compliment you on truthfulness when you admit this. This offer, if you accept it, is an improvement over our current Groundhog Day-type cycle where you cite her study, we point out that she’s walked it back four times, and you somehow never notice her recantations and then continue to cite the meta-analysis as though it’s beyond reproach.

To end on a positive note, while we see many differences between your words and your deeds, let us give you the benefit of the doubt and assume you mean what you say and not what you do. In that case, we invite you to join us in writing an open letter to Penn State, the Business Roundtable, Honeywell, Highmark and every other organization (including Vik Khanna’s wife’s employer) that forces employees to choose between forfeiting large sums of money and maintaining their dignity and privacy. We could collectively advise them to do exactly what you now say: Instead of playing doctor with “pry, poke, prod and punish” programs, we would encourage employers to adhere to USPSTF screening guidelines and frequencies and otherwise stay out of employees’ personal medical affairs unless they ask for help, because overdoctoring produces neither positive ROIs nor even healthier employers. And we need to emphasize that it’s OK if there is no ROI because ROI doesn’t matter.

As a gesture to mend fences, we will offer a 50% discount to all Koop Committee members for the Critical Outcomes Report Analysis course and certification, which is also recognized by the Validation Institute. This course will help your committee members learn how to avoid the embarrassing mistakes they consistently otherwise make and (assuming you institute conflict-of-interest rules as well to require disclosure of sponsorships) ensure that worthy candidates win your awards.

The Wellness Industry Pleads the Fifth

The wellness industry’s latest string of stumbles and misdeeds are on the verge of overwhelming the cloud’s capacity to keep track of them.

First, as readers of my column may recall, is the C. Everett Koop Award Committee’s refusal to rescind Health Fitness Corp.’s (HFC’s) award even after HFC admitted having lied about saving the lives of 514 cancer victims. (As luck would have it, the “victims” never had cancer in the first place.) Curiously, HFC’s customers have won an amazing number of these Koop awards, which are given for “population health promotion and improvement programs.” Why so many, you might ask? Is HFC that good? Well, HFC is not just a winner of the Koop Award. HFC is also a major sponsor. Perhaps it was an oversight that HFC omitted this detail from its announcement that both Koop Awards were won by its customers for 2012.

Second, the American Heart Association (AHA) recently announced its guidelines for workplace screenings. They call for much more screening than the U.S. Preventive Services Task Force does. As it happens, the AHA guidelines were co-written by a senior executive from Staywell, a screening vendor. Not just any vendor, but one that had already been caught making up outcomes.

Third, although the American Journal of Health Promotion published a meta-analysis that showed a degree of integrity rare for the wellness industry, it then hedged the conclusion. The analysis showed that high-quality studies on wellness outcomes demonstrated “a negative ROI in randomly controlled trials.” But the journal then added that invalid studies (generally comparing active, motivated participants to non-motivated non-participants) showed a positive return. The journal said that if you averaged the results of the invalid and the valid studies you got an ROI greater than break-even. However, the averaging logic leading to that conclusion is a bit like “averaging” Ptolemy and Copernicus to conclude that the earth revolves halfway around the sun.

How does the wellness industry respond to criticisms like these three? It doesn’t. The industry basically pleads the Fifth.

The industry knows better than to draw attention to itself when it doesn’t control the agenda. The players know a response creates a news cycle, which they will lose — and that absent a news cycle no one other than people like you are going to read my columns and notice these misdeeds.

One co-author of the AHA guidelines wrote to my Surviving Workplace Wellness co-author, Vik Khanna, and said the AHA would respond to our “accusation” but apparently thought better of it when the lay media didn’t pick up the original story.  (As a sidebar, I replied that saying a screening vendor was writing the screening policy was an “observation,” not an “accusation,” and recommended the editors check www.dictionary.com to see the difference.)

Similarly, in the past, I have made accusations and observations about the wellness industry both in this column and on the Health Care Blog…and gotten no response. So to make things extra easy for these folks, I dispensed with statements that needed to be rebutted. Instead, I asked some simple questions. I said I would publish companies’ responses, which would create a great marketing opportunity for them…if, indeed, their responses appealed to readers.

I posted the questions on a new website called www.theysaidwhat.net.  I got only one response, from the Vitality Group. The other wellness companies allowed the questions to stand on their own, on that site.

To ferret out responses, I then did something that has probably never been done before: I offered wellness companies a bribe…to tell the truth. I said I’d pay them $1,000 to simply answer the questions I posted about their public materials, which would take about 15 minutes.( If someone makes me that offer, I ask, “Where do I sign?” but I’m not a wellness vendor.)

Here’s how easy the questions are: Recall from a previous ITL posting that Wellsteps has an ROI model on its website that says it saves $1,358.85 per employee, adjusted for inflation, by 2019 no matter what you input into the model as assumptions for obesity, smoking and spending on healthcare. The company claims this $1,358.85 savings is based on “every ROI study ever published.” Compiling all those citations would require time, so I merely asked the company to name one little ROI study that supports this $1,358.85 figure. Silence.

I asked similar questions (which you can view on the click-throughs) to Aetna, Castlight, Cigna, Healthstat, Keas (which wins style points for the most creative way to misreport survey data), Pharos, Propeller Health, ShapeUp, US Corporate Wellness and Wellnet, as well as their enablers and validators, Mercer and Milliman. Propeller and Healthstat responded — but didn’t actually answer the questions. Healthstat seems to say that rules of real math don’t apply to it because it prefers its own rules of math. Propeller – having released the completely mystifying interim results of a study long before it was completed – said it looks forward to the study’s completion and didn’t even acknowledge that questions were asked.

In all fairness, one medical home vendor sent a response expressing a seemingly genuine desire to understand or clarify issues with its outcomes figures and to possibly improve their validity (if, indeed, they are invalid). As a result, I am not adding the vendor to this site; the idea is not to highlight honest and well-intentioned vendors. (The company would like its name undisclosed for now, but if anyone wants to contact it, just send me an email, and I will pass it along to the company for response.)

Likewise, there are good guys – Towers Watson and Redbrick, despite their high profiles, managed to stay off the list by keeping their hands clean (or at least washing them right before inspection). Allone, owned by Blue Cross of Northeastern Pennsylvania, even had its outcomes validated and indemnified. I will announce more validated and indemnified vendors in a followup posting.

As for the others, well, I am not saying that their historic and continuing strategy of pleading the Fifth when asked to explain themselves means that they know their statements are wrong. Nor am I saying that they are liars, idiots or anything of the sort. Something like that would be an “accusation.” Instead, I am merely making an “observation.”

It isn’t even my observation. It is credited to Confucius:  “A man who makes a mistake and does not correct it, is committing another mistake.”

Predictive Analytics for Self-Insureds

Predictive analytics is widely used in the insurance industry. Is it time for self-insureds to reap the benefits of predictive analytics and realize significant bottom-line improvements as well?

Self-insureds can use predictive analytics for employee cost benchmarking, early identification of late-developing claims, and budget and allocation decision-making tools. Currently, the key area of focus for self-insured risk managers is claim prevention. But even the best claim prevention methods are not enough to avoid all claims. Claims occur despite a company’s and risk manager’s best efforts. If traditional claim prevention is the only defense against losses, these companies will lag behind their contemporaries who, to keep claim costs down, are already using predictive analytics.
Primer on Predictive Analytics
Whether we know it or not, we have all encountered predictive analytics in our personal lives by simply browsing the Internet. Companies like Amazon leverage their immense amount of data to predict customers’ shopping preferences to drive additional sales. Likewise, the insurance industry, with its substantial volume of data, views predictive analytics as an essential capability. Forward-thinking self-insureds see the benefits of these tools and realize that they should be used to better understand their exposure and better control their losses.

Predictive analytics can improve both customer satisfaction and company profits. Last week, I bought a webcam from Amazon. The product page displays the specific details on the webcam and shows an assortment of additional items frequently bought by other customers who purchased this webcam. Amazon uses its customer purchase data to predict that webcam purchasers also routinely buy extension cables, microphones, and speakers along with their webcam. Thus, Amazon has effectively applied predictive analytics to identify cross-sell opportunities that benefit its customers (I was glad to be reminded that I would, in fact, need an extension cable) and increase its revenue (I spent an additional $5.99).

Amazon’s product pages are an easily visible example of predictive analytics. Similar to Amazon, the insurance industry has adapted predictive analytics to not only increase premiums but also to improve risk selection. Risk selection—being able to identify the good/profitable risks and the bad/unprofitable risks—is so important for insurance companies that the popularity of predictive analytics comes as no surprise. The range of ways insurance companies are using these tools includes evaluating the profitability of their accounts, assisting in effective underwriting and proper pricing, marketing to the appropriate client base, retaining their customers, and estimating the lifetime value of each customer.

It is important to note that predictive analytics is not just a data summary. Predictive analytics sorts through vast amounts of data to find relationships among variables to predict future outcomes. This data can often be seemingly disparate, or even appear to be unrelated; it also often combines the use of both internal company and data from outside the organization. For example, a commonly cited and successful example of such a relationship involved insurers finding a direct correlation between two variables: credit scores and auto claims. Also, the more data fed into an analysis, the more robust it will be. Self-insureds have quite a bit of employee data that greatly aids analyses’ predictive abilities. In addition to loss-specific data, payroll, human resources information, and other available third-party data should be utilized to its fullest extent for optimal results.

Applications for Self-Insureds
Self-insureds have numerous opportunities to benefit from predictive analytics. Three large workers’ compensation areas on which predictive analytics sheds light are: 1) identifying which employees cost more than industry and company averages, 2) predicting early on which claims are the most likely to have late-developing costs, and 3) constructing qualitative cost/benefit scenarios to help risk managers allocate their budgets effectively.

Self-insureds applying predictive analytics to their workers’ compensation claims, for example, have a number of employee variables to work with such as: employee age, length of employment, state of residence, employment type (full time/part time), salary type (hourly/salary, low wage/high wage), and claim type (indemnity/medical only). Predictive analytics can find relationships that will affect future claim activity on current and future employees.

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.: