Big data analytics: Mining your catastrophe claims data for competitive advantage

Big data analytics: Mining your catastrophe claims data for competitive advantage


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Speaker: Karen Clark

The traditional catastrophe models are a “one size fits all” product, and the same assumptions are applied to all insurers.  But no two insurers are the same with respect to catastrophe losses.  The same level of property damage can result in very different claims due to different insurance-to-value assumptions, policy conditions, and claims handling practices.

This session will illustrate how insurers are using the newer open loss modeling platforms to leverage their detailed catastrophe claims data.  Open catastrophe models are more predictive and accurate than the traditional models because the model components can be properly verified, and they can be customized to be more reflective of an insurer’s own claims and loss experience.  

This session will demonstrate the following:

  • How the catastrophe model damage functions are constructed, including mean damage ratios and secondary uncertainty
  • Collecting and preparing exposure and loss data for the detailed catastrophe claims analysis
  • Obtaining the event intensity footprints
  • Matching the claims to the event intensities
  • Analyzing, interpreting, and using the results

With open catastrophe models, actuaries are not limited to the property specific information included in the traditional models because any variable captured in the exposure data can be tested against the detailed claims data.   Insurers that capture richer data can leverage that information for competitive advantage in risk selection.

Advanced open loss modeling technology enables actuaries to take on a wider role in providing more credible information for pricing and underwriting catastrophe exposed lines of business.  Actuaries can provide more actionable information to senior executives before, during, and after the events.  This session will include actual examples of model customizations and how they resulted in more accurate catastrophe loss estimates and impacted the decision making process.

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