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Effect of Cyber Incident Frequency Distributions on Insurance Pricing

Abstract

Cyber incident frequency distributions describe the patterns and probabilities associated with the occurrence of cyber-related events such as data breaches, ransomware attacks, system intrusions, and other cybersecurity incidents. In cyber insurance, the frequency of incidents is an important actuarial consideration because it influences the expected number of claims that insurers may receive during a policy period. The choice of an appropriate frequency distribution may therefore affect the accuracy of expected loss estimates and the pricing of cyber insurance products. This study will examine the effect of cyber incident frequency distributions on insurance pricing. It will assess how alternative statistical distributions used to model cyber incident frequencies influence estimated claim costs and resulting insurance premiums. The study will also compare pricing estimates generated under different frequency distribution assumptions and determine how variations in incident occurrence patterns affect premium adequacy. The study will focus on cyber incident frequency distributions, insurance pricing, cyber insurance claims, cyber risk, incident occurrence, claim frequency, expected losses, premium estimation, probability distributions, risk assessment, loss modelling, and actuarial pricing. Relevant cyber insurance and incident data will be analysed to identify suitable frequency patterns and evaluate their relationship with insurance pricing. Probability models such as Poisson and Negative Binomial distributions may be considered where appropriate for modelling incident frequencies. A quantitative research approach will be adopted for the study. Historical cyber incident and insurance claims data, including incident frequencies, claim counts, claim amounts, policy exposures, coverage limits, and loss experience, will be analysed. Descriptive statistics, goodness-of-fit analysis, probability distribution modelling, correlation analysis, comparative analysis, regression analysis, and sensitivity analysis will be used to assess the effect of alternative frequency distributions on insurance pricing estimates. The study is expected to reveal that the choice of cyber incident frequency distribution may have a significant effect on insurance pricing estimates. Frequency distributions that provide different estimates of the likelihood of multiple cyber incidents may produce different expected claim costs and premium requirements. The magnitude of the effect may depend on incident frequency patterns, portfolio exposure, claim severity, data characteristics, policy coverage, and the statistical distribution selected for modelling. The study will be useful to actuaries, cyber insurance companies, underwriters, risk managers, cybersecurity professionals, pricing analysts, regulators, and researchers. It may provide useful information for selecting appropriate probability distributions, improving cyber risk assessment, estimating expected claims, determining adequate premiums, and strengthening actuarial pricing models. The findings may also assist insurers in developing pricing approaches that reflect observed patterns of cyber incident occurrence. The study concludes that cyber incident frequency distributions are important components of actuarial cyber insurance pricing because differences in frequency assumptions can influence expected losses and premium estimates. It is therefore recommended that insurers regularly evaluate the suitability of frequency distributions using historical incident and claims data, apply appropriate statistical tests, and conduct sensitivity analysis to support reliable cyber insurance pricing and effective risk management.

Keywords: Cyber incident frequency distributions, insurance pricing, cyber insurance, cyber risk, cyber incidents, claim frequency, expected losses, premium estimation, probability distributions, loss modelling, actuarial pricing, risk assessment, insurance claims, frequency modelling, cyber risk management.