Catastrophe modeling

Catastrophe modeling (also known as cat modeling) is the process of using computer-assisted calculations to estimate the losses that could be sustained due to a catastrophic event such as a hurricane or earthquake. Cat modeling is especially applicable to analyzing risks in the insurance industry and is at the confluence of actuarial science, engineering, meteorology, and seismology.

Natural catastrophes (sometimes referred to as "nat cat") include:

Human catastrophes include:

The input into a typical cat modeling software package is information on the exposures being analyzed that are vulnerable to catastrophe risk. The exposure data can be categorized into three basic groups:

The output is estimates of the losses that the model predicts would be associated with a particular event or set of events. When running a probabilistic model, the output is either a probabilistic loss distribution or a set of events that could be used to create a loss distribution; probable maximum losses (PMLs) and average annual losses (AALs) are calculated from the loss distribution. When running a deterministic model, losses caused by a specific event are calculated; for example, Hurricane Katrina or "a magnitude 8.0 earthquake in downtown San Francisco" could be analyzed against the portfolio of exposures.

Recently, an effort to create and disseminate open multi-hazard cat risk modeling tools was initiated by the Alliance for Global Open Risk Analysis (AGORA).

Also Oasis Loss modelling Framework http://www.oasislmf.org has been created. It has been founded as a not for profit organisation funded and owned by the Insurance Industry to promote open access to models and to promote transparency. Open source code to be available.

Additionally, the insurance industry is currently working with the Association for Cooperative Operations Research and Development (ACORD) to develop an industry standard for collecting and sharing exposure data. To date, the industry has been operating on closed, proprietary data formats.

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