By Katherine Westlake | March 15, 2021

When we first work with new Arium clients, the biggest challenge is usually dealing with the data and ensuring that it has all the necessary attributes to enable it to run in our tool. Once that is achieved and clients are able to start using the tool to explore their portfolio accumulations, we are often asked for advice on where to go next.

It is true that, in the London market at least, most exposure or catastrophe modeling managers are new to liability classes and often don’t know how they can demonstrate the value of exposure management processes to their internal stakeholders in the way that they are used to doing with property portfolios.

We usually recommend that they approach this by thinking about all their existing catastrophe risk management processes for property, and how they can be adapted to provide a framework for implementing regular internal reporting on liability exposures.

In this post, I will take a look at a few well-established natural catastrophe reporting approaches that the right tools can help you adapt for liability portfolio management. There is more to catastrophe modeling than reporting though, and some other suggestions for getting a deep understanding of your liability portfolio are touched on as well.

RDS and Custom Scenarios

A scenario-based approach to modeling is flexible and can be built out and developed to meet many familiar reporting requirements. In the first place we have the familiar Lloyd’s Realistic Disaster Scenarios (RDS) approach. Building in a modeling platform a suite of scenarios that is either well-established internally but manual and time-consuming to calculate, or that is determined by a regulator, ensures that familiar analyses are easily and consistently repeatable. This means the suite of scenarios can be run against different input exposure data, allowing for comparisons between monthly or quarterly portfolio snapshots, or between different books of business, such as between line of business or office.

Scenarios can also be built from historical events to provide further insights into portfolio management. Historical events have a record of past losses and culpable industries—such as Madoff, or Deepwater Horizon, or Enron—which forms the basis for creating forward-projected events. These can also be analyzed in different ways to generate useful portfolio management metrics. For example:

  • Sampling portfolio losses to a forward-projected historical scenario a sufficient number of times can enable the construction of a conditional exceedance probability (EP) curve
  • Grouping similar scenarios, such as grouping Vioxx recall with Fen-phen and others, to create a “pharmaceutical product” event set, and sampling across the group, can extend that conditional EP curve to cover a class of events
  • Applying a frequency metric across an event set will incorporate uncertainty around the event occurrence as well, making it possible to generate an event loss table (ELT) and additional analytics, such as tail value at risk (TVaR) curves.

“Maps” of Exposure Data and Portfolio EP Losses

A non-risk adjusted view of exposure can also be a useful way to visualize a portfolio. For property catastrophe modelers, this can be likened to the way in which aggregated exposures can be visualized by region on a map. Considering how exposure is distributed within industries can help to inform decision-making. If you create a template of “zones” based on exposed industries, this can be run regularly in the same way as you would carry out certain geospatial analytics on a property book, to identify the regions (industries) where there might be a concentration of loss to certain events and compare different views of your portfolio.

Once the base processes are established, being able to provide “maps” of exposure data and portfolio EP losses to internal groups are just a few clicks away. Being able to do this regularly embeds the systems and familiarizes end users with outputs, enabling all stakeholders to have the ability to interrogate the data and exposures in a useful way.

Expanded Scope of Analytics

Once regular reporting is embedded, the scope of analytics can be expanded to match other exposure management processes within the organization. The scenario approach makes it possible to stress test portfolios by adjusting certain event parameters to easily and quickly create counterfactual events from historical ones. Event sets can be built up by replicating an existing scenario, multiple times, in order to perturb the input parameters. And regular review of portfolio hot- and cold-spots, using exposure “maps,” can help to inform or validate underwriting policy and risk appetite, manage to underwriting guidelines, and ultimately help underwriters take advantage of areas with potential for capital efficient growth, through diversification.

Most catastrophe risk managers will already be familiar with all of the concepts outlined above. The process of adapting these from a system geared to property exposure management to one that also incorporates liability exposures doesn’t have to be daunting.

 Quantify the impact of liability accumulations to your portfolios with Arium

Categories: Best Practices

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