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🧮 '''Risk model''' is a quantitative framework that simulates the likelihood and financial impact of adverse events on an insurance portfolio, individual policy, or entire market. In the insurance industry, risk models range from [[Definition:Actuarial analysis | actuarial]] frequency-severity models used to price everyday [[Definition:Auto insurance | auto]] or [[Definition:Homeowners insurance | homeowners]] policies to highly complex [[Definition:Catastrophe model | catastrophe models]] that generate thousands of simulated hurricane seasons or earthquake scenarios to estimate tail-risk losses. At their core, all risk models share the same ambition: to convert uncertainty into a probability distribution that decision-makers can act on.

⚙️ Building a risk model typically involves four interconnected modules. A hazard component characterizes the peril — its intensity, frequency, and geographic footprint. A vulnerability component estimates how exposed assets respond to that peril, expressed as damage functions or loss curves. An exposure component catalogs the assets at risk, drawing on [[Definition:Risk location | location]] data, [[Definition:Total insured value (TIV) | insured values]], and structural attributes. Finally, a financial engine applies [[Definition:Policy terms and conditions | policy terms]], [[Definition:Deductible | deductibles]], [[Definition:Reinsurance | reinsurance]] structures, and [[Definition:Aggregate limit | limits]] to translate gross damage into net financial outcomes. Vendors like AIR, RMS, and CoreLogic supply licensed catastrophe models, but many large [[Definition:Insurance carrier | carriers]] and [[Definition:Reinsurer | reinsurers]] also develop proprietary models, particularly for emerging perils like [[Definition:Cyber risk | cyber]] and [[Definition:Climate risk | climate change]] where third-party tools are still maturing.

🔑 The reliability of any insurance operation hinges on the quality of its risk models. [[Definition:Underwriter | Underwriters]] use model output to set [[Definition:Premium | premiums]] and [[Definition:Underwriting guidelines | guidelines]], [[Definition:Chief risk officer (CRO) | chief risk officers]] use it to manage [[Definition:Risk accumulation | accumulations]] and purchase reinsurance, and [[Definition:Rating agency | rating agencies]] use it to assess capital adequacy. Yet models are simplifications of reality, and the industry has learned — sometimes painfully, as with underestimated [[Definition:Business interruption insurance | business interruption]] and [[Definition:Demand surge | demand surge]] losses — that blind reliance on any single model invites trouble. Best practice calls for running multiple models, stress-testing assumptions, and layering expert judgment on top of quantitative output, a discipline increasingly supported by [[Definition:Insurtech | insurtech]] platforms that make model comparison and sensitivity analysis more accessible.

'''Related concepts'''
{{Div col|colwidth=20em}}
* [[Definition:Catastrophe model]]
* [[Definition:Actuarial analysis]]
* [[Definition:Probable maximum loss (PML)]]
* [[Definition:Exposure management]]
* [[Definition:Stochastic modeling]]
* [[Definition:Deterministic model]]
{{Div col end}}

Definition:Risk model - Revision history

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