September 13th, 2018

Managing Catastrophe Model Change

Posted at 1:00 AM ET

powers_imelda_71x88Imelda Powers, Senior Cat Management Advisor, Asia Pacific, Global Strategic Advisory


Incorporating new hazard and claims insights can improve the estimates derived from catastrophe models. By re-analyzing historical events using the latest scientific methods or refining claims with more granular geographical and line of business breakdowns, we can update models with the latest expertise and data. But model changes that yield large swings in loss estimates for frequent events must be carefully scrutinized to understand assumptions and processes in order to truly support ownership of risk.

Effective model change management indicates mature analytics and an embrace of best practices. If a (re)insurer has invested time and resources in developing and understanding a model that is fit for its purpose, subsequent updates can employ the same efficient process. Not every model update implies a wholesale change in the established view of risk.

The US hurricane models are updated every two years at approximately the same time the Florida Insurance Commission approves models. As vendors have historical US hurricane data dating back to 1900, adding two additional years of data generally does not cause a material change in event frequency. However, the occurrence of an extreme event can expose previously hidden attributes in the peril – storm surges from Katrina, unanticipated magnitude exhibited by the Tohoku earthquake, or extreme liquefaction demonstrated in the Christ Church, New Zealand earthquake. At other times, model vendors might get access to new scientific or claims data that can be used to parameterize, calibrate and update their models.

The new attribute, which may not have manifested in past events due to milder intensities, must then be assessed to gain an understanding of its interaction with other model components and claims. The resulting updates to the model produce larger loss estimates for events where the new attribute is prominent, but little or no change to estimated losses for other events. Since US hurricane event frequency would change only modestly, the difference in loss forecast should be minimal for most return periods except the very largest. While perils with high frequency incur claims more often and their models may be updated more frequently, the incremental change in their loss impact should be modest.

The same cannot be said about exposures and models with few observed losses. The infrequent updates can have large implications for loss estimates.

Users expect model builders to carefully validate the distributions of model parameters and their appropriateness in view of a new catastrophe event or scientific discovery. New observations may strengthen existing assumptions or suggest changes, and model builders must be transparent about their expected impact by sharing validation exercises.

Whatever the scale, model change has an effect on institutions’ underwriting, pricing and capital management. However, it does not need to be unduly disruptive. The following best practices are already being adopted by some market participants:

  1. Vendors are willing to discuss inadequacies in existing model versions. A new scientific finding or new claims data may suggest material loss changes years before a model update is complete. This allows users to adjust for model shortcomings, sometimes with help from the vendors, without waiting for a new version release. When the vendor’s final update is completed, its results will likely be close to the user’s modified view of risks, demonstrating the value of questioning the status quo.
  2. Users can benefit by checking a model’s default output and adjusting it according to the user’s own informed view of risk. Many market participants are using variations of the default models – no longer do they all step to the same beat. Industry constituents can find competitive advantage by demonstrating transparent, defensible and customized model adaptation.
  3. There are advantages in calculating capital needs by using multiple methodologies. In some jurisdictions, standard formulas are available to calculate insurer solvency ratios; examples include the use of damage factors by line of business, peril and region under Solvency II in Europe and C-ROSS in China. A catastrophe model change does not have the same impact on companies who use multiple methods to calculate capital needs as those that rely solely on commercial models.
  4. Companies that effectively manage enterprise risk through diversification may be able to protect against shocks arising from model changes. For a global or national multi-line writer, a model change in one geographical area or line of business has less impact on overall capital management than it would for a company whose business is concentrated in that area or line.

In summary, best practices in model change management call for regular, systematic model validation and enhancement, sometimes before the release of new model versions; rigorous portfolio management limits the impact of individual release updates. (Re)insurers who have their own, customized view of risk and adjust models accordingly are generally less impacted by model changes, creating predictability and stability that favors profitable growth.

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*Guy Carpenter & Company, LLC provides this material for general information only.  The information contained herein is based on sources we believe reliable, but we do not guarantee its accuracy, and it should be understood to be general insurance/reinsurance information only. Guy Carpenter & Company, LLC makes no representations or warranties, express or implied. The information is not intended to be taken as advice with respect to any individual situation and cannot be relied upon as such.

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