Our framework is based on an extensive analysis of the characteristics of historical cyber risk events including triggers, propagation and impact. The framework uses Kovrr’s proprietary event catalog with more than 100,000 synthetic events for loss simulation which vary from cloud provider outage in a specific territory to massively distributed global ransomware attacks and other tail events.
Visibility into Potential Accumulations and Tail Events
Achieve clear visibility to probable maximum losses due to potential large loss and catastrophic cyber events. Leverage Kovrr’s impact based modeling framework to assess known knowns and known unknowns by applying probabilistic and deterministic cyber risk scenarios, including Lloyds RDS.
Insights into New Emerging Vulnerabilities
The Kovrr platform reflects emerging exposures and allows (re)insurers to control risk accumulation by notifying them when pre-defined risk thresholds are crossed.
Silent Risk Exposure
Gain visibility to risk associated with potential cyber triggered claims within any commercial P&C insurance book.
Monitoring Portfolio Aggregations
Calculate aggregated exposure to cyber risk in your portfolio while taking into account systemic risk stemming from different types of policies within various lines of businesses.
Stress Test any P&C Portfolio Against Cyber Catastrophes
(Re)insurers can assess their cyber exposure in any type of (re)insurance’s book using a list of predefined scenarios, both historical and synthetic. They can also custom build their own scenarios. For example, a reinsurer can quantify the exposure of its commercial property book to a large scale business interruption event such as the NotPetya ransomware attack.
Kovrr’s cyber risk modeling platform delivers global (re)insurers transparent, data driven insights into their affirmative and non-affirmative cyber risk exposures. The Kovrr platform is designed to help underwriters, exposure managers and catastrophe modelers understand, financially quantify and manage cyber risk by utilizing AI-powered risk models.