Quantifying Liability Accumulations for Clash Scenarios
Katerina ChristopoulouMay 16, 2022
The occurrence of large and complex events has historically shaped the insurance market. They typically erode capital and introduce large degrees of uncertainty – creating unprecedented conditions that then initiate dramatic changes to the industry with severe impacts on multiple lines.
As a result, there is an increasing awareness and a desire to understand systemic risks. The fallacy of examining events as “standalone” is being proven wrong with frightening frequency; interconnectedness is a key factor in all risks.
This awareness – together with societal changes, emerging threats, new business practices, as well as new and evolving insurance products – adds complexity to an already challenging risk landscape.
In an effort to make better, rational, data-driven risk decisions, insurers have recognized the need to increase their efforts to measure the risk not only to their property portfolios but across all lines of business. This need has been compounded by insurance regulators and rating agencies requiring the industry to understand and quantify the underlying risks.
The COVID-19 pandemic is a recent example in which governments, regulators, planners, and the insurance industry worked together to manage the impact on the overall financial system. After all, the insurance industry’s responsibility is to provide stability and to pay the claims that fuel economic recovery after disasters.
Pushing the Boundaries
RMS is at the forefront in the understanding of systemic risk with recent investments in state-of-the-art modeling methodologies and toolsets. Recently, RMS collaborated with the Centre for Risk Studies at the University of Cambridge to push existing boundaries in modeling event losses to cover lines of businesses outside of the traditional property domain of most catastrophe models.
This project laid the foundation for the quantification of liability modeling and property-liability clash in life and non-life insurance. The outcomes of the project were the precursors of new and innovative R&D for liability initiatives.
At the core of these initiatives, is a complete Liability Risk Management Framework, which quantifies liability accumulations against a myriad of possible scenarios. Figure 1 provides an example of a clash scenario between property and casualty lines from an earthquake.
This end-to-end framework supports the essential risk management disciplines of exposure data capture, loss modeling, and reporting yet is simple enough to be transparent and flexible to allow for easy customization. To calculate losses in a consistent and objective manner, we developed explicit mechanisms to understand and measure litigation risk.
Event-Specific Loss Calculations
How liability losses develop is highly specific, based on the coverage afforded (terms and conditions), the scenario that has occurred, and the insured’s business operations – as well as what they may have (negligently) done or not done. Every scenario is unique. When a scenario occurs, it sets conditions, which may cause coverage(s) to trigger in specific ways.
Understanding the potential impact of a scenario requires specific and rigorously parameterized calculations to quantify loss – rather than broad-brush, top-down calculations. The key components behind the Liability Risk Management Framework that allow for such a bottom-up approach to liability loss quantification are coverage trigger pathways and a probabilistic litigation model.
Coverage Trigger Pathways
Coverage trigger pathways (CTPs) are scenario- and coverage-specific mechanisms that describe how different liability insurance coverage payouts are triggered by a given event, or liability trigger (Figure 2). Liability triggers can be considered for any natural catastrophe or man-made event that can result in consequential liability.
The severity of an event is responsible for the activation, or not, of a policy through the triggering of liability coverages. We developed and continuously update an extensive database of CTPs that cover a wide range of perils.
Probabilistic Litigation Model
The probabilistic litigation model simulates the litigation journey from legal filing to resolution (Figure 3). The model takes as an input a mean number of cases and the loss is sampled at an account-coverage level from a loss distribution that considers several lawsuit outcomes pre- and post-trial.
In addition, historical data has been analyzed in order to define the outcome and cost distributions for each liability type. The probability of an insured business participating in the loss process depends on its location, size (as measured by revenue), and industry sector.
Stress Scenario Modeling: Prudential Regulatory Authority Case Study
To comply with regulatory requirements and ensure resilience, insurers must regularly stress test their portfolios to assess risk materiality. The U.K. Prudential Regulatory Authority (PRA) will conduct a general insurance stress test beginning this month that includes natural catastrophe and cyber scenarios. As part of the RMS initiative, we have applied our tools and framework to perform an analysis of the PRA’s California earthquake stress scenario.
The scenario involves the sequence of two severe, correlated earthquakes affecting the San Francisco Bay Area. The first event ruptures the Hayward Fault. This is followed by an event in the nearby Rodgers Creek Fault.
The modeled aggregate economic liability losses for just one of these earthquakes are expected to be 25 to 45 percent of the combined property and liability losses. Figure 4 shows the distribution of modeled industry losses among the examined insurance lines for the Hayward Fault event.
Growing Importance of Liability Clash Scenarios
RMS has continued to expand the model library of liability clash scenarios to cover a broad spectrum of threats ranging from natural catastrophes (including climate conditioned) to man-made and climate change litigation events. We recognize that this area of modeling is new but growing in importance to the market and individual insurers.
If you are interested in this work and wish to discuss it further or explore partnership opportunities, please contact us.
New Ways of Modeling Property-Liability Clash and Uncovering Hidden Risk
Senior Principal Modeler, RMS
Katerina is a senior principal modeler and joined RMS in 2011. Apart from her involvement in the GEAC project during her time at RMS, she developed various property exposure models for climate hazard perils, mainly in Europe. Katerina also worked at Pembroke Managing Agency, where she was responsible for the validation of external catastrophe models and the quantification of non-modeled risks.
Before moving to the U.K., she held positions as Land Surveyor and GIS engineer in the Hellenic Military Geographical Service and Geopaeikonisis Ltd. Katerina has over 20 years of experience as a Geomatics Engineer and during her career to date, she has worked on large-scale private and publicly, EU funded projects.
Katerina is a Chartered Geomatics Surveyor and a corporate member of the Royal Institute of Chartered Surveyors (MRICS). She holds a PhD in Geographic Information Science from UCL and a master's degree in GIS from University of Leeds.