Introduction to Catastrophe Modeling

Understand Risk Modeler 2.0 cat modeling workflow


Catastrophe modeling is the process of using computer-assisted calculations to estimate the vulnerability of exposures. An exposure is an insured property, building, or other entity that may be affected by catastrophic events.

Risk Modeler enables you to use RMS peril models to simulate catastrophic events (e.g. fires, floods, earthquakes) and assess the financial impact of those events on your exposures. Exposures may be analyzed at the portfolio or account level.

This tutorial demonstrates a basic workflow for adding new accounts and locations to an exposure and analyzing those exposures using the Detailed Loss Module (DLM). The primary goal of this tutorial is help you to familiarize yourself with the DLM analysis workflow and to understand the relationships between objects in an exposure.

Process workflow

This is a five-step process:

  1. Manage exposure data in a cloud-based EDM.
    All cat modeling processes depend on the quality and quantity of exposure data in an EDM. Intelligent Risk Platform offers several methods for defining or importing exposure data in a cloud-based EDM.
  2. Prepare exposure data for analysis.
    Prepare exposure data for risk analysis by validating locations and looking up location and risk data for geocoded locations. Once an exposure has been geocoded and hazarded it is ready for analysis.
  3. Analyze exposures.
    Analysis estimates potential financial losses to account or portfolio based on its exposures. The scope of an analysis job is defined by the exposures identified, a model profile, and an output profile.
  4. Retrieve analysis results.
    The Metrics API provides services for viewing analysis results.
  5. Download analysis results.
    Workflow jobs enable you to download results data.

Next steps

The following pages provide step-by-step instructions for completing each stage of the catastrophe modeling workflow. The first step is to prepare the exposure data for analysis.