ABSTRACT Insurers and reinsurers providing capacity to cyber insurance risks have now realised that current pricing models, though effective to date, do not accurately estimate an actuarially fair premium. Increased cyber risk exposure from connected devices, the volume of unstructured data, limited loss experience, and evolving risk complexity have contributed to the challenges of accurately modelling cyber risks. Most current models are based on reported or economic losses collected from secondary sources. The urgent need to develop a hybrid pricing model that integrates loss exposures and qualitative risk perceptions for cyber insurance policies is evident. This paper proposes a machine‐learning approach for modelling cyber risks using neural networks. We developed a model that accurately estimates the probability of loss for various cyber risks across industry segments. We developed a multilayer neural network model to predict the likelihood of cyber risk. We used a structural equation model to examine the aggregate effects of cyber risk on associated exposures. The outputs of both models can be used to estimate the organisation's financial liability and determine appropriate insurance coverage. Our findings show that system vulnerability, user awareness, and cyber risk mitigation significantly affect cyber risk exposure, and that the models' predictive ability is statistically significant. Furthermore, the results of these models were highly useful in building cyber risk resilience and developing actuarial pricing for the selected sectors or industries.
Doss et al. (Thu,) studied this question.