Tag Archives: Michael Henk

Can blockchain enhanced security for insurers?

Blockchain technology may offer insurance companies the security that they have only dreamed about. The technology’s security components enable users to quickly identify whether a stream of data can be “trusted” for accuracy or not. In their article “Blockchain: An insurance focus,” Milliman’s Michael Henk and Robert Bell explain the basics of the technology. They also explore the benefits and limitations that blockchain could have on insurers.

Here’s an excerpt:

Blockchain technology also has the potential to limit fraudulent claims. False billings and tampered documents are less likely to “fall through the cracks” if the data is decentralized and immutable, which will reduce the amount of erroneous claims payments. Utilizing this technology will enable insurers to lower their loss adjustment expenses and pass on that savings to consumers in the form of lower rates. Furthermore, if this technology becomes widely used, it can help mitigate identity theft and other cyber liability losses.

Identity theft is the fraudulent acquisition and use of a person’s private identifying information. Usually this is done in order for the perpetrator to realize a financial gain. Because the data is encrypted at the financial transaction level, the technology minimizes the amount of identifying information available in the blockchain, thus minimizing the risk of identity theft.

The encryption protocol utilized by the blockchain technology has the capability to limit cyber liability as well. Cyber liability is the risk that personally identifiable information will be compromised by a third party storing an individual’s data. Current practice is to store this data in a central location with software to protect against hacking. With this technology, it enables data to be run and stored based on the current blockchain without unencrypting the underlying data because the chain itself can be independently verified through separate nodes….

…As with any emerging technology, these potential benefits do not come about without a few potential limitations, in addition to the security concerns discussed above. The most problematic of the limitations is scalability. In order for the insurance industry to utilize blockchain technology, it would take a remarkable amount of infrastructure.9 Currently, blockchain technology is limited by the amount of computing power available. In order for data to be decentralized, each node must be able to process the requisite data for each transaction for a growing number of participants. While smaller blockchains are currently successful with a limited number of participants, the insurance industry has a much larger population of participants that will need to have their data validated in a timely manner. This will mean not only more storage space, but also enough computing power to quickly be able to validate each new transaction or data point.

Brewing insurance solution for craft brewers

Craft beer companies need unique insurance solutions to address the distinct risks inherent in their industry. Companies can minimize the financial effects that these risks can create by purchasing specialized craft brewery coverage. In the article “Crafting insurance for the new brewery industry,” Milliman’s Michael Henk explores some of the larger risks a craft brewer faces along with the type of coverages the brewery should consider obtaining.

Here’s an excerpt:

Boiler/machinery liability
Boilers and machinery expose breweries to multiple liabilities. First of all, with production being reliant on machinery, any major breakdown could be devastating for business. When a brewery does not produce a lot of beer to begin with, even a temporary halt in production could have large consequences.

Along with a halt in production, brewers have the extra added risk of injuries if something more serious happens. An exploding boiler doesn’t just affect the production and finances of the brewery, but may also result in damages and injuries for workers, contractors, and tour-goers.

There are many steps that craft brewers can take to mitigate the potential economic impact of this risk. For the production side of the liability, brewers can obtain boiler and machinery coverage that will cover them for replacement or repair costs. Property insurance can also cover some of the loss of income from a breakdown in production.

Tour liability
One of the more interesting phenomena with respect to craft brewing is the great popularity of brewery tours, where breweries open their doors to the public (sometimes while the brewery is still in full operational mode). This serves craft breweries well as a marketing tool because it gets people in the door learning about and sampling the product. Popular tours sell out with regularity and have even become “must-see” tourist attractions in many cities. Macro-breweries have gotten in on the tour game as well. However, tours at larger breweries tend to avoid the production floor and tend not to include areas of the brewery that are currently operating.

With these production floor tours of active breweries comes unique liability. Paying customers are invited to walk around the brewery among the fermentation tanks and machinery (accompanied by a tour guide, of course). A brewer needs to make sure that conditions are safe for customers and take preventive measures.

In one specific case, a fermentation tank explosion during a tour led to customer injuries at a craft brewery in Texas.7 Not only was there an obvious halt in production in this case, but also two years after the incident, customers who were on the tour went to court for damages, citing pain and suffering as a result of the incident.

Brewers need to be covered for less “explosive” events as well. Slips and falls are a lot more likely, especially when the brewery tours contains stairwells and wet floors. Brewers must obtain general liability insurance with sufficient limits to cover the bodily injury caused to tour-goers or the potential property damage caused by them.

Can social media and data analytics expose insurance fraud?

Insurers are now perusing social media networks to detect insurance fraud and avoid paying large claims. Here’s some perspective from Michael Henk’s article “Insurance fraud and social media.”

With the immensely high rate of social media usage, some insurance companies are turning to this large source of public information to help combat insurance fraud. As of April 2016, there are 1.59 billion active Facebook users every month globally, while 400 million people use Instagram every month and 320 million users are active on a Twitter account monthly.5 Social media users account for a substantial portion of the population, and these users are generating a massive amount of data, an insurance company’s best friend.

Each day, 500 million tweets are posted—about 6,000 tweets a second.6 As of May 2013, 4.75 billion pieces of content were shared daily on Facebook. Currently, every 60 seconds on Facebook, 510 comments are posted, 293,000 statuses are updated, and 136,000 photos are uploaded.7 Unless social media users have adjusted the privacy settings on their accounts, all information that is posted is considered public. This means that insurance companies have free access to any non-private posts from their customers (or anyone else for that matter). The insurance industry thrives off of data for business purposes such as underwriting, and now, this social media “data” may help insurance companies succeed in combating insurance fraud.

Searching through these networks can become time-consuming and expensive though. Implementing data analytics in addition can optimize insurers’ time and results by sifting through extensive data and expediting a claims investigation.

While analyzing social media can occasionally result in a big win in the fight against insurance fraud, the costs will not always outweigh the benefits. However, if paired with data analytics, the likelihood of uncovering fraudulent claims can greatly increase for a company. Data analytics gives a company the opportunity to process large amounts of data and quickly identify important outliers or other potential indicators of fraud.

For example, geospatial analysis can be utilized when looking for fraud in wind, hail, or flood damage claims. This statistical analysis helps identify what geographical locations were most affected by a storm and where the company would expect to receive claims. It could also aid in identifying the likely severity of a claim based on the proximity to a storm event. Receiving a claim from outside the affected area or a very large claim from a minimally affected area should trigger the need for further investigation.

Analysts can also examine historical claims to identify typical frequencies and severities of policyholder claims. When a current period’s claims are compared to historical data, one can identify cohorts of policyholders who are either making more frequent claims or claims with higher loss amounts. Investigators may want to look further into these types of policyholders to look for further signs of possible fraud.

Pokémon GO and the non-augmented reality of risk

Pikachu and his friends have caused quite a frenzy recently. While people are enthralled with Nintendo’s Pokémon Go, the GPS-based augmented reality (AR) game presents several risks to its developer and its gamers. In his article “Pokémon Go and augmented reality: Not all fun and games,” Milliman consultant Michael Henk discusses some of these AR technology-related risks.

Concerning personal injury risks:

Firstly, AR products like “Go” provides yet another “distraction.” We’re all aware of the dangers of being “distracted.” Texting while driving is illegal in a number of cities and states throughout the country. However, drivers aren’t the only ones being distracted. Distracted walking is a growing problem, one that has arisen naturally with the increasing dependence on mobile electronic devices and one that “Go” is already contributing to. There are anecdotes all over social media about players so engrossed in catching virtual monsters that they’re running into walls and walking in traffic. …

…“Go” may lead to an increase in distraction-caused injuries and pedestrian-vehicle injuries, which is currently the fifth-leading cause of death for children ages 5 to 19. It’s not inconceivable to imagine an incident in which both the driver and the pedestrian are distracted, maybe by the same “rare” Pokémon.

What about cyber risks?

Aside from “IRL” (in real life) dangers, there’s a data security concern with some early installs. Some iOS installs of the software require the user to provide the app with full access to their google accounts, which allows access to their Gmail (theoretically being able to send e-mail from your account), files stored on Google Drive and Google Photos, among other content. The developer has responded and said this was done erroneously, and that permissions will be corrected soon, but it’s important to make sure that users know exactly what programs on their devices have access to. There are other concerns about downloading the program from non-official app stores as well, but that stands for all programs and is definitely not a “Go”-specific concern.

Legal risks?

…There’s a significant risk for trespass with AR games that utilize real-world locations. It remains to be seen whether an AR developer placing cyber-content on your property constitutes trespassing or if AR users are “engaged on a cyber plane on which you have no exclusive property claim.” There’s another legal concern with “attractive nuisance,” which states that property owners are responsible for eliminating dangerous conditions on their property which may attract children. “An individual who fails to rectify an attractive nuisance on their property is civilly-liable to injury a child sustains on it, even if the child was trespassing.” Sounds like something that may happen in the pursuit of a rare Pokémon.

Predictive analytics can make playing daily fantasy sports a homerun

While most daily fantasy sports (DFS) players usually swing and miss, big data management and predictive analytics have the capacity to increase a player’s chance of winning more consistently. In this article, Milliman’s Michael Henk and Nicholas Blaubach discuss the monetary success that some advance modelers are having on DFS websites using predictive analytics. The following excerpt highlights the steps necessary to build a DFS predictive model.

There are some basic steps that serve as general “rules of thumb” when we set out to develop our predictive model to make us millions in DFS.

First, we need an objective. We want our model to optimize our roster, giving us the most potential points. In our DFS example, we’d want a predictive model that will help us identify the best players for the cost (in order to stay under the salary caps) for any given contest.

Next, we gather our data… Gathering the data and getting it into a proper format for our predictive model is another story, but historical sports data is easy to find online. One thing to consider here is the traditional actuarial concern of credibility. If the data isn’t credible, it’s highly unlikely that we’ll be able to build a successful model from it….

After we choose the data to use, we need to select and transform the specific variables in the data set. The structure of the predictive (or independent) variables in relation to the target (or dependent) variable determines how well a model works. We can transform variables (by taking logarithms, for example) or bucket variables to see what gives us the best fit. Sports data can have hundreds (or even thousands) of variables….

Next, we process and evaluate our model. The key to good model performance is obviously getting the best fit. If we’ve done the other steps up to this point well, this step should run smoothly. Here we identify the ideal number of variables and use performance metrics to evaluate the model fits….

Once all of that is done, it’s important to not merely implement the model and ignore it. It requires routine maintenance. As time goes by and data continues to emerge, we need to take time to reinvestigate the data, update the models, and challenge some of our initial assumptions. The best models are continually updated and recalibrated, audited on a regular basis, and replaced when they are no longer effective.

Ride sharing is opening up new business opportunities for auto insurers

Transportation network companies (TNCs), better known as ride-sharing services, such as Uber, Lyft, and Sidecar, have created challenges for regulators and insurance companies. However, as cities and states work on governing this new mode of transportation, insurers should take advantage of the opportunity to develop new products covering ride-sharing drivers.

In their article “Ride sharing and insurance issues,” Milliman consultants Michael Henk and Peggy Brinkmann discuss the risks of insuring TNC drivers as well as the new opportunities for innovation. Here is an excerpt:

One of the central ambiguities in ride sharing is the question of who pays when there is an accident… To answer the coverage question, it is helpful to break down the vehicle use into four different categories:

1) Ride sharing driver isn’t logged in to the ride sharing service and is thus not available for hire.

2) Ride sharing driver is logged in to the ride sharing service and is available for hire, but has not yet found a passenger.

3) Driver and passenger have confirmed a ride share and the driver is en route for pickup.

4) Ride share is in progress.

There is general agreement that accidents arising in the first category would be covered by the personal auto policy, but the second category has been more controversial…

Even insurers that are denying claims related to ride sharing are concerned about covering the personal usage of vehicles also used for ride sharing…

Without access to standard personal auto coverage, though, ride-share drivers are in need of new products and/or rating plans. Today’s typical rating plan varies rates by pleasure, work, and business usage, and mileage is difficult to capture accurately. To address concerns that the ride-share vehicle personal usage risk might be higher than expected under current rating plans, carriers could introduce a new variation of business usage for ride-sharing vehicles. Carriers could also perhaps require ride-sharing vehicles to enroll in usage-based rating programs in order to ensure the most accurate data about vehicle usage to get appropriate rates for the personal use of these vehicles.