Cyber Catastrophe Modeling
Proprietary Cyber Catastrophe Modeling Framework
Kovrr has developed a proprietary cyber modeling framework, designed to estimate the impact probability and severity of cyber catastrophe events. The framework is used to generate an event catalog that represents historic events and probabilistic future catastrophic events with different levels of severity. The exposure of the portfolio is examined against all or specific scenarios and takes into account tail events ranging from cloud provider outage to wide-scale ransomware attacks.The expected damage is calculated based on a portfolio’s vulnerability assessment.
Kovrr enables (re)insurers to model and assess their catastrophic cyber exposure based on different levels of data granularity input. Kovrr’s dashboard empowers (re)insurers to comprehensively analyze their portfolios by examining exposure and risk on different portions of their portfolio and different types of scenarios. This analysis allows (re)insurers to reflect their risk approach in the model and can help determine onboarding approaches, risk appetite, and capital allocation strategies.
Quantifying Exposure for a Specific Event
(Re)insurers can easily assess the cyber exposure of any type of insurance book using a list of predefined scenarios, based on historical events, probabilistic events and on the current cyber threat landscape. For example, using Kovrr, a reinsurer can quantify the exposure of its property book to a large scale business interruption event such as the NotPetya ransomware attack or the Mirai IoT botnet denial of service attack.
Kovrr provides insurance professionals unique visibility into its modeling methodologies and the underlying data. With Kovrr’s transparent modeling, (re)insurers can build data-driven underwriting processes and exposure management teams are provided with valuable cyber risk knowledge.
Data, Data, Data
Kovrr monitors millions of ongoing incidents in real-time. The platform fuses, proprietary, open-source, and third-party data to build advanced AI machine learning engines to predict and price cyber risk. We create risk models from cyber incidents, intelligence exposure data, claims incidents data and access to a variety of 3rd party data providers.
There is no one size fits all cyber risk models. Cyber risk models must be aligned to (re)insurers’ insured assets and directly map to potential liabilities, derived from the insurance coverages. With Kovrr, (re)insurers can receive their own fully optimized models utilizing data that closely corresponds with the geography, industry, type of businesses, etc. With these models, (re)insurers can differentiate their offerings in the market.