This paper focuses on cyber risk, an emerging threat that significantly affects numerous sectors in today’s interconnected world. Among the strategies aimed at enhancing resilience and minimizing the impact of this risk, insurance contracts emerge as a potential solution. We present a heterogeneous generalized susceptible-infectious-susceptible model designed for pricing cyber risk insurance contracts. The model accurately captures the dynamics of cyber threats and evaluates the financial implications for insurance providers. It introduces an innovative method that distinguishes between critical and noncritical nodes within a network, enabling precise fortification against threats while optimizing resource allocation. Our findings show that the proposed method allows us to measure potential losses and reveals how the network’s structure influences the propagation of infections. This insight can be leveraged to enhance the overall security posture of the network. A numerical analysis, simulating the network structure of a small- to medium-sized enterprise validates the effectiveness of this approach.
Clemente, G., Cornaro, A., Belvedere, S. (2025). Pricing Cyber Risk Insurance Coverages by Means of Epidemic Models and Network Theory. VARIANCE, 18.
Pricing Cyber Risk Insurance Coverages by Means of Epidemic Models and Network Theory
Cornaro, A;
2025
Abstract
This paper focuses on cyber risk, an emerging threat that significantly affects numerous sectors in today’s interconnected world. Among the strategies aimed at enhancing resilience and minimizing the impact of this risk, insurance contracts emerge as a potential solution. We present a heterogeneous generalized susceptible-infectious-susceptible model designed for pricing cyber risk insurance contracts. The model accurately captures the dynamics of cyber threats and evaluates the financial implications for insurance providers. It introduces an innovative method that distinguishes between critical and noncritical nodes within a network, enabling precise fortification against threats while optimizing resource allocation. Our findings show that the proposed method allows us to measure potential losses and reveals how the network’s structure influences the propagation of infections. This insight can be leveraged to enhance the overall security posture of the network. A numerical analysis, simulating the network structure of a small- to medium-sized enterprise validates the effectiveness of this approach.I documenti in IRIS sono protetti da copyright e tutti i diritti sono riservati, salvo diversa indicazione.