Cyber Risk Frequency, Severity and Insurance Viability

TL;DR

This paper analyzes cyber risk frequency and severity using regression models to assess insurance viability, providing new insights into the factors influencing cyber loss events and the insurability of cyber risk in the US industry context.

Contribution

It introduces a comprehensive data-driven analysis of cyber risk factors and insurability using advanced regression models and utility frameworks, addressing key gaps in cyber insurance research.

Findings

Identifies significant covariates for cyber loss frequency and severity.

Assesses the impact of industry sector and company size on insurability.

Provides a framework for evaluating cyber insurance viability based on empirical data.

Abstract

In this study an exploration of insurance risk transfer is undertaken for the cyber insurance industry in the United States of America, based on the leading industry dataset of cyber events provided by Advisen. We seek to address two core unresolved questions. First, what factors are the most significant covariates that may explain the frequency and severity of cyber loss events and are they heterogeneous over cyber risk categories? Second, is cyber risk insurable in regards to the required premiums, risk pool sizes and how would this decision vary with the insured companies industry sector and size? We address these questions through a combination of regression models based on the class of Generalised Additive Models for Location Shape and Scale (GAMLSS) and a class of ordinal regressions. These models will then form the basis for our analysis of frequency and severity of cyber risk loss processes. We investigate the viability of insurance for cyber risk using a utility modelling framework with premium calculated by classical certainty equivalence analysis utilising the developed regression models. Our results provide several new key insights into the nature of insurability of cyber risk and rigorously address the two insurance questions posed in a real data driven case study analysis.