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🎲 '''Monte Carlo simulation''' is a computational technique used extensively in insurance to model the probability distribution of outcomes — such as aggregate [[Definition:Claims | claims]] costs, [[Definition:Catastrophe loss | catastrophe losses]], or [[Definition:Investment portfolio | portfolio]] returns — by running thousands or millions of randomized scenarios. Unlike deterministic models that produce a single-point estimate, Monte Carlo methods generate a full range of possible results along with their associated probabilities, giving [[Definition:Actuary | actuaries]], [[Definition:Underwriting | underwriters]], and [[Definition:Risk management | risk managers]] a far richer picture of the uncertainty they face.

🔄 The process begins with defining the key random variables — for instance, [[Definition:Claims frequency | claim frequency]], [[Definition:Loss severity | severity]], [[Definition:Interest rate | interest rates]], or [[Definition:Inflation | inflation]] trends — and specifying probability distributions for each based on historical data and expert judgment. A computer then draws random samples from those distributions and calculates the resulting outcome for each trial. After repeating this tens of thousands of times, the accumulated results form a probability distribution of the target metric. In [[Definition:Reinsurance | reinsurance]] pricing, for example, a Monte Carlo engine might simulate hurricane seasons by varying storm counts, intensities, and landfall locations to estimate the likelihood that losses breach a specific [[Definition:Attachment point | attachment point]]. Similarly, insurers apply it in [[Definition:Enterprise risk management (ERM) | enterprise risk management]] to calculate [[Definition:Value at risk (VaR) | value at risk]] and [[Definition:Tail value at risk (TVaR) | tail value at risk]] for regulatory [[Definition:Capital adequacy | capital]] requirements under frameworks like [[Definition:Solvency II | Solvency II]].

📈 The real power of Monte Carlo simulation lies in its ability to capture the compounding effect of multiple sources of uncertainty simultaneously — something closed-form analytical solutions often cannot do when variables are correlated or distributions are non-standard. For an insurer writing [[Definition:Property insurance | property]] and [[Definition:Casualty insurance | casualty]] lines across multiple geographies, understanding how a single catastrophic event might trigger correlated losses across the book is essential for setting [[Definition:Reinsurance program | reinsurance programs]] and maintaining [[Definition:Solvency | solvency]] buffers. Advances in computing power and [[Definition:Cloud computing | cloud infrastructure]] have made it practical to run increasingly granular simulations in near-real time, transforming Monte Carlo from a periodic strategic exercise into an everyday decision-support tool.

'''Related concepts'''
{{Div col|colwidth=20em}}
* [[Definition:Catastrophe modeling]]
* [[Definition:Stochastic modeling]]
* [[Definition:Actuarial science]]
* [[Definition:Value at risk (VaR)]]
* [[Definition:Enterprise risk management (ERM)]]
* [[Definition:Loss distribution]]
{{Div col end}}

Definition:Monte Carlo simulation - Revision history

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