Spring has sprung, which means wedding season is just around the corner. 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?
The average wedding in the United States 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. So if you’re looking for information on wedding insurance – either buying it or offering it – check out our “Wedding Insurance 101” infographic, based on an article by Milliman consultant Elizabeth Bart.
The average wedding in the United States costs $35,329. And organizing a wedding involves a lot of variables that introduce the risk of financial loss. In the article “Getting hitched without the hitch,” Milliman consultant Elizabeth Bart discusses wedding insurance coverage that can mitigate a couple’s financial risk.
Here’s an excerpt:
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 (https://www.travelers.com/personal-insurance/wedding-insurance/why-wedding-insurance.aspx). 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.
As the cyber liability insurance market catches up with constantly evolving exposures, opportunities also continue to present themselves. In a recent Risk & Insurance article, Milliman’s Tom Ryan and Elizabeth Bart discuss some of the cyber market’s challenges and opportunities. They also discuss the sector’s current state and what lies ahead.
Here is an excerpt:
Tom Ryan, principal and consulting actuary at Milliman, describes the cyber insurance market as both “crystalizing and diversifying.”
“There are at least 40 different policy forms in use right now for cyber liability,” he said. “It’s like comparing apples to oranges to kumquats. However, Insurers are now in the process of smoothing out the wrinkles and developing some standardization of language and coverage.” …
…Insurers benefit by going beyond coverage and offering risk management tools and services to their insureds.
“Some carriers are getting really savvy about cyber. They want to avoid the losses as much as their insureds do,” [Elizabeth] Bart said. “So they get the right people in the right place. The right lawyers, the right PR team, and the right IT vendors.”
“We are seeing a lot of experts come into the insurance industry with knowledge of the hardware and software components of internal systems,” Ryan said. “They have a better understanding of how hacking happens.”
Limited capacity in cyber liability insurance is another hurdle that companies and insurers must navigate. The formation of an industry cyber insurance pool could increase options on the market and reduce the risk incurred by individual insurers. Tom’s article “Cyber liability insurance: As the market heats up, is it time to cool off in a pool?” provides more perspective.
Exposure to cyber risk concerns many organizations. However, sparse insurance-specific data make it difficult for actuaries to price cyber risk for small- and mid-size organizations. In this article, Elizabeth Bart explains three cases where she used untraditional methodologies to develop cyber insurance services. The excerpt below highlights one of the three situations.
A new cyber writer
A European cyber reinsurer with a focus on small- to mid-sized businesses started offering a cyber insurance product in the United States that is similar to its European one. Its original filing submission to states’ departments of insurance (DOIs) was based on reinsurance pricing which responded to competitive rates in the market. However, the DOIs rejected a majority of the filings because they lacked actuarial support.
For the same reasons cyber insurance buyers have a hard time comparing policies, comparing rate filings among competitors is equally challenging. With each insurer offering different cyber coverages, services, and limits, the severities, frequencies, and underwriting guidelines tend to be very different.
Relying on publicly available, generally accepted, nonspecific cyber frequency and severity information was the most straightforward way to support the premiums. There was not sufficient credible historical loss data available from the insurer, there was nothing comparable from other insurers’ filings, and, as mentioned, there was no aggregate insurance industry data available. With the insurer’s focus on small- to mid-sized businesses, the competitive marketplace was dictating premiums under $10,000 (depending on the size of the insured and the coverage limits). For this pricing, the Ponemon Institute’s often referenced $15 million loss event is not a likely scenario for this group.
By working with a combination of the client’s own reinsurance data and by data mining publicly available cyber data specific to the target insured, we were able to determine applicable frequencies and severities to demonstrate the appropriateness of the originally filed rates and successfully get approval for the premiums in all states.
Self-insureds are experiencing benefits assessing risks and controlling costs using predictive analytics. In this Insight article, Elizabeth Bart discusses how these tools can help self-insureds mitigate claim losses.
Here is an excerpt:
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 perspective on how self-insureds can benefit from predictive analytics, watch this Google Hangout.
Insurance companies capture and store large amounts of data that influence key business decisions. Predictive analytics allows insurers to identify granular patterns in data that can lead to better business practices.
In this Google Hangout, Milliman’s Elizabeth Bart, Michael Paczolt, and Terry Wade discuss how self-insureds can benefit from the use of predictive analytics.
Milliman offers predictive analytics and modeling solutions that produce clear, actionable results, and critical strategic insights. To learn more, click here.