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Monte Carlo simulation

Reviewed by Apex Insurance Brokers · Last reviewed 2026-06-22

Reviewed by Apex Insurance Brokers · Last reviewed 2026-06-22

~2 min read

Reviewed by Apex Insurance Brokers · Last reviewed 2026-06-22

Category: Risk identification & assessment · Reviewed by Amy Price, Account Executive · Last reviewed

Monte Carlo simulation is a computational technique that estimates the distribution of an outcome by repeatedly sampling input variables from their underlying probability distributions and evaluating the model for each sample. Named after the Monaco casino district, it was developed by Stanislaw Ulam and John von Neumann on the Manhattan Project in the 1940s.

Application to insurance

Monte Carlo is the dominant numerical technique for:

Key concepts

Regulatory anchor

Under Solvency II (Directive 2009/138/EC, Article 101), the Solvency Capital Requirement is calibrated to the 99.5% one-year Value at Risk. Internal models almost universally use Monte Carlo to evidence this.

References

Cross-references


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