The objective of the research is to contribute to the actuarial literature on cyber risk assessment in order to provide possible solutions for the reduction of the gap between supply and demand of cyber insurance.

In particular, the aim is to achieve a better understanding in quantifying, managing and pricing cyber risk by means of: 

a) a deeper awareness of cyber risks and of the economic damages they produce; 

b) the introduction and validation of new actuarial techniques to allow insurers a more efficient management of this new class of risk;

c) The design of innovative insurance contracts and alternative ways of risk transfers to reduce the costs of insurance premiums. 

To this end, we will make use of actuarial models based on statistical techniques: starting from the classical Generalized Linear Model for actuarial pricing, we will then introduce a more sophisticated Integer Valued GARCH model to introduce time-dependent effects. To be more specific, we plan to estimate INGARCH models that assume data breaches to have a zero-inflated Negative Binomial distribution. Zero-inflation has the purpose of capturing the fact that, in some cases, the institution victim of the breach does not report the attack. To better capture the dynamics of data breaches, we plan to extend the baseline model by adding some Markov-Switching features. To the best of our knowledge, INGARCH models have never been used in actuarial applications. We will also improve loss estimates and risk pricing by looking for lead-lag relationships between cyber risks and economic variables (see De Giovanni, Leccadito, Pirra (2021)) for an example of such relation between data breach and Bitcoin metrics). We define a parametric insurance product as a possible way to overcome some of the issues to be faced in analyzing and managing cyber risks described in details in Eling et al. (2021, 2022) and discuss the implications through numerical applications.

The proposal was selected and the project awarded with a research grant of the AFIR-ERM IAA Section.