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Syst."],"published-print":{"date-parts":[[2020,12,31]]},"abstract":"<jats:p>The fact that \u201ccyber risk\u201d is indeed a collective term for various distinct risks creates great difficulty in communications. For example, policyholders of \u201ccyber insurance\u201d contracts often have a limited or inaccurate understanding about the coverage that they have. To address this issue, we propose a cyber risk categorization method using clustering techniques. This method classifies cyber incidents based on their consequential losses for insurance and risk management purposes. As a result, it also reveals the relationship between the causes and the outcomes of incidents. Our results show that similar cyber incidents, which are often not properly distinguished, can lead to very different losses. 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