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🎲 '''Stochastic simulation''' is a computational technique that uses random sampling to generate a large number of possible outcomes for uncertain processes, enabling insurers and reinsurers to build probability distributions of losses, capital needs, and financial results rather than relying on single-point estimates. In insurance, the approach is foundational: it underpins [[Definition:Catastrophe model | catastrophe modeling]], [[Definition:Dynamic financial analysis (DFA) | dynamic financial analysis]], [[Definition:Reserving | reserve variability studies]], and the [[Definition:Internal model | internal models]] that carriers use to calculate regulatory capital under frameworks such as [[Definition:Solvency II | Solvency II]] and [[Definition:C-ROSS | C-ROSS]]. By explicitly modeling randomness — in claim frequency, severity, timing, investment returns, and correlations among risks — stochastic simulation captures the tail outcomes that matter most for an industry whose core business is absorbing uncertainty.

⚙️ The most common implementation is the [[Definition:Monte Carlo simulation | Monte Carlo method]], in which a computer draws random values from specified probability distributions thousands or millions of times to simulate potential scenarios. A [[Definition:Property catastrophe reinsurance | property catastrophe]] model, for example, might generate tens of thousands of synthetic hurricane seasons, each producing a different pattern of landfalls, wind intensities, and insured losses. An [[Definition:Actuarial model | actuarial reserving]] model might simulate thousands of possible future claim development paths for a portfolio of [[Definition:Liability insurance | liability]] claims. Each simulated scenario yields a set of financial outputs — gross losses, [[Definition:Reinsurance recovery | reinsurance recoveries]], net income, capital consumption — and the aggregate of all scenarios forms an empirical distribution from which key risk metrics are derived: [[Definition:Value at risk (VaR) | Value at Risk]], [[Definition:Tail value at risk (TVaR) | Tail Value at Risk]], [[Definition:Probable maximum loss (PML) | probable maximum loss]], and [[Definition:Return period | return period]] loss estimates.

📈 Stochastic simulation has moved from the domain of specialist catastrophe modelers into the mainstream of insurance decision-making. Pricing actuaries use it to set [[Definition:Premium | premiums]] that reflect the full distribution of expected outcomes, not just the mean. [[Definition:Chief risk officer (CRO) | Chief risk officers]] rely on stochastic output to conduct [[Definition:Stress testing | stress tests]] and [[Definition:Own risk and solvency assessment (ORSA) | ORSA]] analyses required by regulators across Europe, the United States, and Asia. Reinsurance buyers use stochastic loss profiles to optimize their [[Definition:Reinsurance program | reinsurance programs]], selecting attachment points and limits that balance cost against protection at targeted probability levels. The increasing availability of cloud computing has dramatically reduced the time needed to run large-scale simulations, making stochastic methods accessible even to mid-sized [[Definition:Managing general agent (MGA) | MGAs]] and [[Definition:Insurtech | insurtechs]] that would previously have lacked the computational infrastructure.

'''Related concepts:'''
{{Div col|colwidth=20em}}
* [[Definition:Monte Carlo simulation]]
* [[Definition:Catastrophe model]]
* [[Definition:Value at risk (VaR)]]
* [[Definition:Dynamic financial analysis (DFA)]]
* [[Definition:Risk modeling]]
* [[Definition:Probable maximum loss (PML)]]
{{Div col end}}

Definition:Stochastic simulation - Revision history

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