2 résultats pour « Cyber risk classification »
Cyber risk classifications often fail in out-of-sample forecasting despite their in-sample fit. Dynamic, impact-based classifiers outperform rigid, business-driven ones in predicting losses. Cyber risk types are better suited for modeling event frequency than severity, offering crucial insights for cyber insurance and risk management strategies.
This paper argues that traditional cyber risk classifications are too restrictive for effective out-of-sample forecasting. It recommends focusing on dynamic, impact-based classifications for better predictions of cyber risk losses, suggesting that risk types are more useful for modeling event frequency rather than severity.