Social Inflation and Property/Casualty Insurance

Systemic Liability Events

High profile liability issues with systemic potential

High profile liability issues with certain notable defendants could be a source of high jury verdicts and defense costs for insurers. These are some of the potentially systemic liability issues to keep on your radar.

Football-related head injuries

Arium Football Head Injury Fig1 738x415

Portfolio modeling software can provide a framework to measure enterprise-wide casualty risk, and to monitor that risk over time. (Source: Arium)

Football-related head injuries

Over the past few years, there has been increased attention focused on football-related concussions and other head injuries, both at professional and non-professional levels. There are growing fears that football players are at greater risk for developing chronic traumatic encephalopathy (CTE) as well as other neurodegenerative diseases due to sustained concussive and subconcussive hits over the course of their careers.

What does this mean for liability insurers?

As former players age, and the long-term effects of playing football become more evident, there has been a growing number of lawsuits initiated against athletic organizations and helmet manufacturers regarding CTE, concussions, and other head injuries.

Bias in artificial intelligence (AI)

Liability Event Footprints Artificial Intelligence

Bias in artificial intelligence (AI)

Initially, it was thought the use of machine learning algorithms by employers would effectively combat biases in the recruiting, screening, hiring, retention, and promotion of employees. However, their growing use has begun to raise significant concerns they may not always be playing fair.

Machine learning algorithms can inadvertently incorporate the biases of their human creators and, even more so, the biases of the historical data sets on which they are trained. The algorithms continue to learn from these data sets, without the need for additional explicit instruction to guide their decisions. If the underlying data sets used for training the algorithm are biased toward a specific gender or other group, the algorithm will naturally provide biased results that mirror the data set it learned from—perpetuating historical discrimination on a mass scale in a way that is nearly invisible.

When discriminatory biases that run afoul of either federal or state law are uncovered, algorithms and training data sets may potentially be evidence of systemic bias that could assist in providing the basis for a class action-type lawsuit against an organization.

Opioid crisis-related losses

Opioid Epidemic Event Footprint Arium 738x415

Overlaying insurance portfolio data onto an event footprint helps to identify potential industries at risk for an insurer. (Source: Arium)

Opioid crisis-related losses

On October 26, 2017, the White House announced a Nationwide Public Health Emergency to address the reported increase in opioid-related deaths and addiction in the United States, often referred to as “America’s opioid crisis.”

According to the National Institute on Drug Abuse (NIDA), in 2016 more than 115 people per day died of opioid overdoses in the U.S. The opioid crisis, caused by prescription opioid misuse and addiction, has an estimated annual cost of USD 78.5 billion in the United States. Per NIDA, this number incorporates estimates of the cost of medical treatment, loss of earnings, pain and suffering, and death from overdose.

What does this mean for liability insurance?

The opioid crisis has the potential to become a systemic liability event because of the high number of opioid manufacturers and distributors, retailers, physicians, and hospitals involved in the marketing and distribution of such drugs.

Using the past to illuminate the future

Emerging Systemic Liability Events

Using the past to illuminate the future

History presents us with a robust catalog of past emerging risks, in which some risks anticipated to cause huge losses faded away, while others mushroomed into multi-billion dollar events. Which factors contribute either to making an emerging risk fade away into history – or to making history itself?

By leveraging our research on past emerging risks, we hope to develop a framework to enable the risk management community to identify and prioritize risks according to their threat potential.

Understanding scenario losses with Arium

Arium Casualty Analytics Platform

The rapidly changing legal, economic, and technological environments mean that the past is only a starting point for understanding the impact of future events.

Portfolio losses due to forward-projected historical events and emergent and emerging risks can be modeled by designing scenarios that impact multiple lines of business and industries throughout the supply chain.

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