catastrophe modeling News
Can Karen Clark Move Forward the Cat Modeling Industry She Helped Create?
Karen Clark scored a bonafide first when she launched catastrophe modeling company Applied Insurance Research (AIR) in 1987. With a focus on computer simulations, it was the only operation of its ...
Open Platform vs. Open Source
Karen Clark noted that her company's RiskInsight "open platform" currently is the only one in the industry already used by insurers and reinsurers. But what about the Oasis framework, launched by 21 ...
Leadership Insight: Karen Clark
Clark on Catastrophe Modeling: "The tricky thing about cat modeling is it is not all about the science and not all about the software. It is about having the right mix and then making sure that ...
Climate Change Modeling on Cusp of Paradigm Shift
In the face of growing interest in climate change impacts, several big catastrophe modelers said they've heard from more clients interested in receiving climate-related data and they believe the ...
AIR Worldwide Vet to Lead Berkshire Hathaway Specialty Insurance’s Cat Engineering/Analytics Arm
Berkshire Hathaway Specialty Insurance brought in an AIR Worldwide executive to lead its new catastrophe engineering/analytics division. Akshay Gupta's will be head of Catastrophe Engineering & ...
Karen Clark Rolls Out New U.S. High-Res Storm Surge Model
Karen Clark & Co. is rolling out a new U.S.-focused high-resolution storm surge model. The catastrophe risk company said the new model brings advances to the market including fully transparent ...
What Happens Inside the Oasis Kernel?
Oasis is a flexible plug-and-play framework. As illustrated below, it allows hazard, vulnerability and user-interface specialists to plug in so that their products can be used: To produce damage ...
Sunlight Drives Away the Shadows: The Value of Open Source
In its 20-plus years, catastrophe risk modeling had developed nearly insurmountable barriers to alternative views of risk. Except for the largest companies, insurers and reinsurers have had little ...

