Cyber Insurance Pricing Through Monte Carlo Simulation: A Case Study of the Egyptian Insurance Market

This study introduces a dual-model approach to pricing cyber risk insurance, specifically designed for Egypt’s insurance market, utilizing Monte Carlo simulation methods. The first model uses a probabilistic risk-based pricing framework, simulating claim costs under optimistic, moderate, and pessimistic scenarios with normally distributed loss values. Premiums are set by applying risk-adjusted multipliers to the average projected losses, helping ensure both profitability and adequate capital reserves. The second model focuses on annual cyber losses, combining Poisson-distributed attack frequencies with normally distributed loss severities to estimate yearly losses across five types of organizations: Low Risk, Moderate Risk, High Risk, Tech Firms (with high severity), and Retail Chains (with high frequency). Through 10,000 iterations per scenario, the study calculates key financial measures, including expected profits, loss probabilities, and five-year Net Present Values (NPVs). Findings reveal that while some sectors show stable profitability, others—particularly tech firms—face high chances of losses and negative NPVs, indicating potential underpricing. The research highlights the value of using scenario-based stochastic models, deductible options, and confidence intervals to enhance underwriting precision and portfolio resilience. The study concludes by recommending that Egyptian insurers adopt data-driven, risk-sensitive pricing strategies that factor in both loss frequency and severity, along with maintaining capital buffers to manage the volatility inherent in cyber risk effectively.