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To meet the demand for more accurate Expected Loss estimates from catastrophe models, Datum Companies announces the release of Eurus, a catastrophic weather predictive platform.  Nearly two years in the making and powered by a combination of Big Data analytical technologies—including wavelets, support vector machines, principal components analysis and neural networks—the Eurus platform understands complex, non-linear relationships between millions of historical weather feature data points using machine learning to calculate catastrophic event probabilities. 

“The approach that property catastrophe modeling companies currently use to calculate the hazard component of their models isn’t sufficiently predictive, so legacy models shouldn’t be used in isolation if industry participants want to accurately price catastrophic risk,” says Sean Bourgeois, Datum Companies president and CEO. “As we all know, the property catastrophe models that insurance and reinsurance companies have been using for years have poor track records when it comes to estimating medium- to long-term expected loss, so the industry instead applies simple multiplication factors to expected loss results to estimate catastrophic risk. Additionally, due to the mechanics of these models, frequent revisions have been necessary which reset probable maximum loss calculations. We developed Eurus® to measurably boost predictive accuracy.”  

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