Tag Archives: social media

Social media influencers bear reputational risk that insurance may cover

Influencer marketing is a lucrative business. Top social media influencers can earn upwards of $25,000 per post in partnership with a brand or company. Still, social media influencers must think about reputational risks that can have a measurable effect on their revenue.

In this article, Milliman’s Madeline Johnson discusses why individuals who rely on their name for income may need some type of reputation risk or business interruption insurance. She also explains the factors insurance companies should consider if they design an individual reputation risk insurance product.

Here is an excerpt from the article:

Starting with the premise that our “good name” translates to our own individual “brand,” protecting one’s individual reputation correlates to protecting one’s personal brand – and the corresponding income stream and overall marketability contained therein. Just as Bruce Springsteen insured his voice or Heidi Klum her legs, for many professionals and celebrities their income is often dependent on the individual reputation they have created. As social media usage increases, the potential for a negatively received public comment does too. A negatively received post has potential implications not only for the social media star but also potentially for the partner company or brand. These companies hire influencers and pay them to endorse their products or services on various social media venues. Reputation risk insurance could provide a financial safety net by providing coverage if a significant negative media event occurred that quantifiably affected an influencer’s future revenue stream….

… In exploring a structure for a reputation risk insurance product for individuals, an insurance company would need to consider the ramifications of insuring an influencer’s potentially poor choice in posting. In most insurance policies, the insurer is offering protection from an outside risk exposure, not an intentional communication on social media. From an insurer’s perspective, issues to consider include defining the specific social media coverage event excluding instances where protocols were not used and, most importantly, the ability to quantify the premium and loss coverage accurately. The insurer would need a methodology to estimate the predicted occurrence of the negative social media event to determine the risk of loss to the insurer. We would expect the actuarial value of the covered losses to be a key component to the policy. Insurance companies would need to structure the policy using a set of assumptions related to how much has been damaged or lost and for how long. Evaluating past social media influencer income streams versus changes after varying posts and videos to form a predictive view may be helpful in understanding risk exposure. A prudent approach to determining insurance terms and pricing is to perform an actuarial study to evaluate the frequency and severity data from similar past events. This can be accomplished by evaluating relationships between social media influencers that have partnerships with certain brands or products, costs of the ultimate drop in followers and sales, and any existing mitigation activities.

Emerging risk analytics: Application of advanced analytics to the understanding of emerging risk

This report by Milliman’s Neil Cantle uses advanced machine learning algorithms, such as deep neural networks, to analyse social media conversations about Brexit. The purpose of the study was to examine whether useful information could be extracted from social media in what is effectively real time on a key topic in a political economy.

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.