![]() ![]() A county-level or even CRESTA-level analysis may be far too broad given the granularity of the event footprints and the sensitivity to the specific locations impacted. However, when an event occurs, using preaggregated data is inevitably going to limit the accuracy of the analysis. The original exposure data may be rich in detail but may need to be preaggregated to a specific level, such as at the county level, to handle the data volumes. To address this challenge, many reinsurers have created homegrown data warehouses that summarize their data across all cedants. The data volumes are significant, spanning hundreds or even thousands of client exposure databases, each with multiple treaty layers on top. For most reinsurers, answering these questions requires collecting and analyzing data from a wide range of tools, data sources, and systems. ![]() In a recent blog for insurers, I looked at the importance of real-time event response and exposure management in this blog, I will focus on reinsurance and the need to generate net loss figures. When a catastrophic event such as a hurricane or an earthquake strikes, an insurance business relies on the exposure management team to answer the big questions: What level of loss is the business looking at, how much will be recovered from our reinsurance, and how do we communicate this? ![]()
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