Rapid Estimation for Cyber Insurance Premium Pricing for Company Decision-makers
Open AccessCyberattacks on enterprise and governmental information systems by bad actors including ransomware attacks have increased in recent years having an annual impact of $6.9 billion USD (Smith, 2022). One element of risk management for cyberattack loss is cyberattack insurance. Actuarial pricing of such insurance has been elusive due to lack of loss and claim data for the relatively new insurance line (Pate-Cornell & Kuypers, 2022). Application of statistical and machine learning techniques for premium estimation remain largely theoretical due to the aforementioned lack of data (Romanosky, et al., 2019). Lack of dependable models have led to the reality that cyberattack insurance premiums are not logically priced according to reasonable estimates of loss, but rather at levels designed to provide plenty of margin should a claim be made; or else issued with so many coverage exclusions as to greatly limit policy usefulness (Ralph, 2018). This praxis proposes a framework to survey the current insurance pricing methodologies and identify improvements, including constituent factors for a pricing model. Results from the framework are compared in some simple case studies with government-mandated pricing information supplied by insurers and accessible through the System for Electronic Rates & Forms Filing (SERFF) database (NAICa, 2022). Whereas typical pricing information through SERFF is too complex for consumer use, the proposed framework’s model is easy to use. The policy prices obtained from this framework model provide similar accuracy to SERFF but require only nine factor weightings in a spreadsheet, rather than responses to 40 pages of a rating manual. Sample price comparisons are offered to show that the model is applicable across a wide range of potential cybersecurity insurance clients. This simplification of cybersecurity insurance premium pricing will increase its usefulness as a risk mitigation tool, since customers can more easily determine policy cost.
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