Near-Real-Time, Post-Earthquake Financial Decision-Making Applications

Abstract eng:
Post-earthquake financial decision-making has evolved considerably over the past decade. Insurers and reinsurers, private companies, governments, and aid organizations have shown increasing creativity in the use of near-real-time (NRT) earthquake information for their own loss estimation, financial adjudication, and situational awareness. Such financial analyses can be of significant benefit to stakeholders, facilitating risk transfer operations, fostering sensible management of risk portfolios, and assisting disaster responders. The main motivation for this paper is the elucidation and documentation of how existing and developing post-earthquake financial decision-making strategies make use of or depend on NRT earthquake information. A better understanding of the tools of the trade and specific needs of the financial sector can further enhance NRT earthquake information systems, which in turn may enhance the further development of creative financial instruments, resulting in additional beneficial risk management alternatives for at-risk communities. The advancement of post-earthquake financial instruments has been, in great part, made possible by the availability of rapid and accurate earthquake parameters and more quantitative geospatial hazard information. For example, the U.S. Geological Survey’s (USGS) earthquake information systems have evolved to further accommodate specific requirements, some particular to the financial sector. Herein, we describe several developments that streamline post-earthquake financial decision-making, primarily related to the USGS ShakeMap system. In particular, we discuss improvements to 1) event-specific metadata, data and product archiving, and technical documentation; 2) additional gridded parameters (including interpolated rock-motion estimates); and 3) improved ground motion reporting, including spatial variability characterization and enhanced directivity functions. Lastly, we describe the systematic collection of scenarios and historical ShakeMaps; they too facilitate the calibration of loss models and hindcasting.

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