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Related Experiment Videos

Improving Catastrophe Modeling for Business Interruption Insurance Needs.

Adam Rose1, Charles K Huyck2

  • 1Price School of Public Policy and Center for Risk and Economic Analysis of Terrorism Events (CREATE), University of Southern California, Los Angeles, CA, USA.

Risk Analysis : an Official Publication of the Society for Risk Analysis
|January 6, 2016
PubMed
Summary
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Business interruption (BI) loss estimation in catastrophe (CAT) modeling needs improvement. This study proposes a framework using facility-level data and resilience tactics to enhance accuracy for BI insurance needs.

Area of Science:

  • Risk Management
  • Insurance Analytics
  • Disaster Resilience

Background:

  • Catastrophe (CAT) modeling for property damage is advanced, but business interruption (BI) modeling remains underdeveloped.
  • Current CAT models use simplistic relationships to estimate BI losses from property damage.
  • BI loss estimation is complex, influenced by public and private decisions on resilience tactics during recovery.

Purpose of the Study:

  • To propose a framework for enhancing hazard loss estimation for business interruption (BI) insurance.
  • To improve the accuracy of BI loss predictions by incorporating facility-level data and resilience strategies.
  • To address the limitations of current CAT models in translating property damage into BI losses.

Main Methods:

  • Developing a framework for improved hazard loss estimation in BI insurance.
Keywords:
Business interruptionCAT modelingdisaster lossesinsuranceresilience

Related Experiment Videos

  • Advocating for enhanced data collection at the individual facility level within companies.
  • Analyzing the effectiveness of specific resilience tactics (e.g., inventory access, operational relocation, accelerated repair).

    • Main Results:

    • The proposed framework allows for better matching of facilities with specific resilience tactics.
    • Improved data granularity enhances the accuracy of BI loss estimation.
    • Demonstrated the impact of enhanced estimation methods through a hurricane case study.

    Conclusions:

    • Facility-level data and resilience analysis are crucial for accurate BI loss estimation.
    • The proposed framework offers a more sophisticated approach to BI modeling in CAT insurance.
    • Implementing these improvements can lead to more reliable BI insurance products and risk management strategies.