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🌐 '''Cyber catastrophe model''' is a quantitative framework designed to estimate the [[Definition:Aggregate loss | aggregate losses]] that could arise from large-scale, correlated [[Definition:Cyber risk | cyber events]] affecting many [[Definition:Insured | insureds]] simultaneously — functioning for the [[Definition:Cyber insurance | cyber insurance]] market in a role analogous to what [[Definition:Catastrophe model | natural catastrophe models]] play for [[Definition:Property insurance | property]] [[Definition:Underwriter | underwriters]]. Unlike natural peril models that draw on decades of meteorological and seismological data, cyber catastrophe models must contend with a threat landscape that evolves rapidly, limited historical loss data, and attack vectors that can propagate across industries and borders within hours. Firms such as [[Definition:RMS | Moody's RMS]], [[Definition:AIR Worldwide | Verisk AIR]], CyberCube, and Kovrr have developed competing approaches that blend actuarial science, cybersecurity expertise, and scenario simulation to produce [[Definition:Probable maximum loss (PML) | probable maximum loss]] and [[Definition:Exceedance probability curve | exceedance probability]] estimates for portfolios of cyber risk.

⚙️ These models typically operate by defining a catalog of catastrophic scenarios — such as a widespread [[Definition:Ransomware | ransomware]] campaign targeting a common operating system, the compromise of a dominant [[Definition:Cloud computing | cloud service provider]], or the exploitation of a critical vulnerability in widely deployed software — and then simulating how each scenario propagates through an insurer's book of business. Key inputs include the [[Definition:Exposure | exposure]] characteristics of insured entities (industry, size, technology stack, [[Definition:Cybersecurity control | security posture]]), the conditional probability of each entity being affected given a particular event, and the financial severity of outcomes including [[Definition:Business interruption insurance | business interruption]], [[Definition:Data breach | data breach]] costs, and [[Definition:Contingent business interruption insurance | contingent business interruption]]. Because systemic cyber events can generate loss correlations far higher than those seen in traditional casualty lines, the tail of the loss distribution is where these models deliver their most critical — and most uncertain — outputs.

🔑 For the insurance industry, cyber catastrophe models matter because they underpin nearly every major decision in the rapidly expanding cyber market: how [[Definition:Reinsurance | reinsurers]] price cyber [[Definition:Treaty reinsurance | treaties]], how primary carriers set [[Definition:Accumulation management | accumulation limits]], how [[Definition:Rating agency | rating agencies]] and regulators assess [[Definition:Capital adequacy | capital adequacy]], and how [[Definition:Insurance-linked securities (ILS) | ILS]] investors evaluate [[Definition:Catastrophe bond | cyber catastrophe bonds]]. Without credible cat modeling, insurers struggle to differentiate between manageable attritional [[Definition:Loss | losses]] and portfolio-threatening systemic events, which in turn constrains capacity and pushes pricing toward conservatism or, worse, toward mispricing. The field is still maturing — model-to-model variance remains wide, and the absence of a deep historical loss catalog forces heavy reliance on expert judgment and forward-looking threat intelligence — but the trajectory is clear: as the cyber insurance market scales, the sophistication and adoption of these models will be a primary determinant of how much capacity the global market can sustainably deploy.

'''Related concepts:'''
{{Div col|colwidth=20em}}
* [[Definition:Cyber insurance]]
* [[Definition:Catastrophe model]]
* [[Definition:Accumulation management]]
* [[Definition:Probable maximum loss (PML)]]
* [[Definition:Cyber risk]]
* [[Definition:Catastrophe bond]]
{{Div col end}}

Definition:Cyber catastrophe model - Revision history

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