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🎲 '''Stochastic model''' is a mathematical framework that incorporates randomness and probability distributions — rather than fixed, deterministic inputs — to simulate the range of possible outcomes for uncertain insurance variables such as [[Definition:Claim | claim]] frequency, [[Definition:Loss | loss]] severity, [[Definition:Catastrophe | catastrophe]] impacts, and [[Definition:Investment | investment]] returns. In the insurance industry, stochastic modeling is foundational to [[Definition:Enterprise risk management (ERM) | enterprise risk management]], [[Definition:Capital modeling | capital modeling]], and [[Definition:Reserving | reserving]] because it acknowledges that the future does not follow a single path: losses can cluster, tail events can materialize, and correlations between risks can shift in ways that deterministic projections simply cannot capture.

⚙️ A stochastic model works by running thousands — sometimes millions — of simulations, each drawing random values from calibrated probability distributions that represent the key risk drivers. In a [[Definition:Catastrophe model | catastrophe model]], for example, the stochastic engine generates a synthetic catalog of hurricanes, earthquakes, or floods, each with randomized characteristics like landfall location, intensity, and path, then calculates the resulting insured losses across an insurer's [[Definition:Portfolio | portfolio]]. In [[Definition:Dynamic financial analysis (DFA) | dynamic financial analysis]], stochastic techniques project an insurer's balance sheet over multiple future years under thousands of economic and underwriting scenarios, producing distributions of outcomes for [[Definition:Surplus | surplus]], [[Definition:Solvency | solvency ratios]], and [[Definition:Profit | profitability]]. The output is not a single number but a probability distribution — enabling decision-makers to ask questions like "What is the probability that losses exceed $500 million?" or "At the 99.5th percentile, how much capital do we need?"

🧮 The strategic value of stochastic models lies in their ability to quantify uncertainty and tail risk with a rigor that simpler approaches cannot match. Regulators increasingly expect insurers to employ stochastic techniques: the NAIC's [[Definition:Own risk and solvency assessment (ORSA) | ORSA]] framework and Europe's [[Definition:Solvency II | Solvency II]] regime both contemplate stochastic internal models for setting [[Definition:Risk-based capital (RBC) | capital]] requirements. [[Definition:Reinsurer | Reinsurers]] use stochastic output to price [[Definition:Excess of loss reinsurance | excess-of-loss]] and [[Definition:Catastrophe bond | catastrophe bond]] structures, while [[Definition:Rating agency | rating agencies]] evaluate the sophistication of an insurer's stochastic modeling capabilities as part of their [[Definition:Financial strength rating | financial strength rating]] assessment. As computational power has grown and cloud-based platforms have reduced infrastructure costs, even mid-market [[Definition:Insurance carrier | carriers]] and [[Definition:Managing general agent (MGA) | MGAs]] can now access stochastic tools that were once the exclusive province of the largest global [[Definition:Reinsurer | reinsurers]].

'''Related concepts'''
{{Div col|colwidth=20em}}
* [[Definition:Catastrophe model]]
* [[Definition:Dynamic financial analysis (DFA)]]
* [[Definition:Capital modeling]]
* [[Definition:Enterprise risk management (ERM)]]
* [[Definition:Monte Carlo simulation]]
* [[Definition:Own risk and solvency assessment (ORSA)]]
{{Div col end}}

Definition:Stochastic model - Revision history

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