To determine needs and design of an effective Community-Based Catastrophe Insurance (CBCI) program, data and modeling are needed to quantify the risks at a fine degree of spatial resolution. This may be provided by private modeling firms, academics, reinsurance brokers or others with the relevant expertise. Such modeling would look at the full range of possible disaster events and estimate probabilistic impacts at a property level. The modeling can also be used to identify mitigation measures that a community could adopt to lower the probable maximum loss sufficiently to improve insurability or pricing.
Many communities are accustomed to modeling their risk for emergency management or other purposes related to disaster resilience using government modeling tools, for example, HAZUS, or engineering-based modeling methods, which can be highly granular but often deterministic (looking at specific scenarios to support risk reduction). While these risk modeling methods are very important and may well be essential to unlocking some of the benefits of CBCI (such as premium discounts for risk reduction), they will need to be supplemented by probabilistic catastrophe modeling approaches to which insurers and reinsurers are accustomed in order to move on to the next part of the CBCI implementation framework – detail the risk transfer solution. Converting existing engineering-based assessments of risk into catastrophe models — or running catastrophe models from scratch — requires technical expertise not usually maintained at the community level. Instead, communities will likely need to rely on partners for this work, namely specialist organizations.
“Analyzing Risk” is one step in the implementation process for CBCI, described in detail in the report, Community-Based Catastrophe Insurance: A Model for Closing the Disaster Protection Gap, from Guy Carpenter, MarshMcLennan Advantage and the Wharton Risk Management and Decision Processes Center.