Modelling and predicting enterprise-level cyber risks in the context of sparse data availability - Daniel Zängerle; Dirk Schiereck (The Geneva Papers on Risk and Insurance - Issues and Practice, 2022)
Despite growing attention to cyber risks in research and practice, quantitative cyber risk assessments remain limited, mainly due to a lack of reliable data. This analysis leverages sparse historical data to quantify the financial impact of cyber incidents at the enterprise level. For this purpose, an operational risk database—which has not been previously used in cyber research—was examined to model and predict the likelihood, severity and time dependence of a company's cyber risk exposure. The proposed model can predict a negative time correlation, indicating that individual cyber exposure is increasing if no cyber loss has been reported in previous years, and vice versa. The results suggest that the probability of a cyber incident correlates with the subindustry, with the insurance sector being particularly exposed. The predicted financial losses from a cyber incident are less extreme than cited in recent investigations. The study confirms that cyber risks are heavy-tailed, jeopardising business operations and profitability.
Keywords: 330; Cyber risk modelling; Cyber risk management; Cyber insurance; Vine copula; Sparse time series