Swiss Re has upgraded its Life Guide underwriting manual, introducing an enhanced Coronary Artery Disease (CAD) calculator.
This tool is now available to more than 25,000 Life Guide users across 800 companies in 100 countries.
The updated CAD calculator is designed to streamline the underwriting process, enabling underwriters to more accurately assess the appropriate premiums based on an individual’s cardiovascular health. The calculator helps underwriters evaluate various risk factors, including CAD diagnosis, medical history, treatment types, and comorbidities.
The update also features a new interface that consolidates CAD ratings within Life Guide, along with an interactive 3D heart model that assists underwriters in visualizing risk more effectively.
With CAD being one of the leading causes of death worldwide, it remains a key area of focus for medical underwriters. The previous version of the CAD calculator, released in 2018, was one of the most widely used tools within Life Guide, averaging 300,000 hits annually on a global scale.
The enhanced CAD calculator is part of Swiss Re's broader efforts to update Life Guide annually. These updates include new rating pages aligned with the latest medical research and improvements in the manual's usability, navigation, and functionality.
Life Guide is ranked as the industry’s top global underwriting manual, according to NMG Consulting studies. It is supported by Swiss Re’s experts, who ensure that the manual reflects the latest developments in medical, regulatory, and technological fields.
Life Guide combines medical and underwriting expertise with analytics and actuarial insights to support clients in making informed decisions.
Meanwhile, Swiss Re, in collaboration with Bellwether, also recently announced that it is using AI and comprehensive data harvesting to enhance underwriting capabilities for insurers facing wildfire risk.
Bellwether’s technology harnesses 600 layers of geodata from sources like Google, including detailed information on canopy, vegetation, precipitation, and wind speed. These datasets train machine learning models to forecast wildfire risk in specific areas accurately
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