Pricing of Cyber Insurance Contracts in a Network Model

Fahrenwaldt, Matthias Albrecht; Weber, Stefan; Weske, Kerstin

doi:10.1017/asb.2018.23

Cited by 51 publications

(26 citation statements)

References 28 publications

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“…To this end, dependencies between losses can also be captured by considering a model of epidemic spreading on the underlying network of firms. Fahrenwaldt et al [46] use a (Markovian) SIS-process to model the infectuous spread of a cyber vulnerability and subsequently an adapted counting process for the occurrence of attacks. Xu and Hua [47] use Markovian and Non-Markovian processes for epidemic spreading and propose to use a copula approach to capture the dependence among time-toinfection distributions.…”

Section: Interdependence and Network Modelsmentioning

confidence: 99%

A comprehensive model for cyber risk based on marked point processes and its application to insurance

Zeller

Scherer

2021

Eur. Actuar. J.

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After scrutinizing technical, legal, financial, and actuarial aspects of cyber risk, a new approach for modelling cyber risk using marked point processes is proposed. Key covariates, required to model frequency and severity of cyber claims, are identified. The presented framework explicitly takes into account incidents from malicious untargeted and targeted attacks as well as accidents and failures. The resulting model is able to include the dynamic nature of cyber risk, while capturing accumulation risk in a realistic way. The model is studied with respect to its statistical properties and applied to the pricing of cyber insurance and risk measurement. The results are illustrated in a simulation study.

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Section: Interdependence and Network Modelsmentioning

confidence: 99%

A comprehensive model for cyber risk based on marked point processes and its application to insurance

Zeller

Scherer

2021

Eur. Actuar. J.

View full text Add to dashboard Cite

show abstract

“…To this end, dependencies between losses can also be captured by considering a model of epidemic spreading on the underlying network of firms. [42] use a (Markovian) SIS-process to model the infectuous spread of a cyber vulnerability and subsequently an adapted counting process for the occurrence of attacks. Expected aggregate network losses are estimated using a polynomial approximation of claims and a mean-field approach.…”

Section: Interdependence and Network Modelsmentioning

confidence: 99%

A Comprehensive Model for Cyber Risk Based on Marked Point Processes and Its Application to Insurance

Zeller

Scherer

2020

SSRN Journal

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“…Another approach involves a network model in cyber risk estimation. Fahrenwaldt et al [ 13 ] suggest the pricing of cyber insurance contracts in a network model. The authors developed the first insured loss mathematical model generated by infectious cyber threats.…”

Section: Introductionmentioning

confidence: 99%

Pricing of cyber insurance premiums using a Markov-based dynamic model with clustering structure

2021

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Cyber insurance is a risk management option to cover financial losses caused by cyberattacks. Researchers have focused their attention on cyber insurance during the last decade. One of the primary issues related to cyber insurance is estimating the premium. The effect of network topology has been heavily explored in the previous three years in cyber risk modeling. However, none of the approaches has assessed the influence of clustering structures. Numerous earlier investigations have indicated that internal links within a cluster reduce transmission speed or efficacy. As a result, the clustering coefficient metric becomes crucial in understanding the effectiveness of viral transmission. We provide a modified Markov-based dynamic model in this paper that incorporates the influence of the clustering structure on calculating cyber insurance premiums. The objective is to create less expensive and less homogenous premiums by combining criteria other than degrees. This research proposes a novel method for calculating premiums that gives a competitive market price. We integrated the epidemic inhibition function into the Markov-based model by considering three functions: quadratic, linear, and exponential. Theoretical and numerical evaluations of regular networks suggested that premiums were more realistic than premiums without clustering. Validation on a real network showed a significant improvement in premiums compared to premiums without the clustering structure component despite some variations. Furthermore, the three functions demonstrated very high correlations between the premium, the total inhibition function of neighbors, and the speed of the inhibition function. Thus, the proposed method can provide application flexibility by adapting to specific company requirements and network configurations.

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Pricing of Cyber Insurance Contracts in a Network Model

Cited by 51 publications

References 28 publications

A comprehensive model for cyber risk based on marked point processes and its application to insurance

A comprehensive model for cyber risk based on marked point processes and its application to insurance

A Comprehensive Model for Cyber Risk Based on Marked Point Processes and Its Application to Insurance

Pricing of cyber insurance premiums using a Markov-based dynamic model with clustering structure

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