CyberCube Unveils Version 4.0 Of Account Manager
CyberCube Unveils Version 4.0 Of Account Manager
Version 4.0 of Account Manager, a software-as-a-service (SaaS) program for single risk re/insurance underwriters, has been made available by cyber risk analytics company CyberCube.
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In addition to new data and usability improvements, the most recent edition of Account Manager also offers score and signal improvements.
In order to assist carriers in proactively assessing their CAT exposure at the point of underwriting, new financial quantification insights have also been developed, according to the firm, employing CyberCube’s sector-leading catastrophe loss modeling for single-risk evaluation needs.
John Anderson, CyberCube’s Principal Product Manager for Account Manager, said: “For too long, insurance carriers have been relegated to using solutions designed for other purposes to inform their decisions.
CyberCube Unveils Version 4.0 Of Account Manager
“Account Manager Version 4.0 is the next-gen solution underwriters have been demanding. It combines many elements including our world-class CAT model, attritional and large loss model, proprietary security signals, and data, in a highly intuitive and underwriter-centric layout. This tool empowers underwriting teams, enabling meaningful guideline setting, and is prime for adoption.”
Mohammad Al Boni, CyberCube’s Lead Data Scientist, said: “We strive to continuously study and analyze our data and analytics, applying several advanced techniques such as multivariate statistical analysis and predictive forecasting. Account Manager Version 4.0 demonstrates the high degree of rigor we apply to ensure we are validating and producing cutting-edge analytics.”
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Earlier in the year, CyberCube also launched an updated version of Portfolio Manager (PM), a cyber risk modeling platform used by re/insurers and brokers. The new version focuses on deepened modeling of two key scenario classes, cloud outage and mass malware, and also includes general data updates on company firmographics, technical dependencies, and security scores.
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