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📊 '''Risk modelling''' is the process of using mathematical and statistical techniques to simulate, estimate, and quantify the potential frequency and severity of losses that an [[Definition:Insurance carrier | insurance carrier]], [[Definition:Reinsurer | reinsurer]], or portfolio may face. In insurance, risk models range from actuarial pricing models that inform [[Definition:Premium | premium]] calculations to catastrophe models that estimate losses from natural perils such as hurricanes, earthquakes, and floods. These models translate complex, uncertain exposures into probabilistic outputs — such as [[Definition:Probable maximum loss (PML) | probable maximum loss]] curves, [[Definition:Value at risk (VaR) | value-at-risk]] estimates, and [[Definition:Exceedance probability curve | exceedance probability distributions]] — that underpin virtually every strategic and operational decision an insurer makes.

⚙️ At its core, risk modelling combines hazard data, [[Definition:Exposure | exposure]] information, vulnerability functions, and financial structures to produce loss estimates. A [[Definition:Catastrophe model | catastrophe model]], for example, generates thousands of simulated event scenarios, applies damage functions to the insured assets exposed, and then passes the resulting losses through the insurer's [[Definition:Reinsurance | reinsurance]] and retention structures to determine net outcomes. Firms such as Moody's RMS, Verisk, and CoreLogic have long dominated natural catastrophe modelling, but the field has expanded to encompass [[Definition:Cyber risk | cyber risk]], [[Definition:Pandemic risk | pandemic risk]], and [[Definition:Climate risk | climate risk]]. Regulatory frameworks reinforce modelling discipline: [[Definition:Solvency II | Solvency II]] in Europe requires insurers to use approved internal models or a standard formula to calculate their [[Definition:Solvency capital requirement (SCR) | solvency capital requirement]], while the [[Definition:National Association of Insurance Commissioners (NAIC) | NAIC's]] [[Definition:Risk-based capital (RBC) | risk-based capital]] framework in the United States and China's [[Definition:C-ROSS | C-ROSS]] regime impose their own quantitative modelling expectations. [[Definition:IFRS 17 | IFRS 17]] has further elevated the role of modelling by requiring granular, probability-weighted estimates of future cash flows for insurance contract measurement.

💡 Without robust risk modelling, insurers would be unable to price [[Definition:Policy | policies]] accurately, allocate [[Definition:Capital | capital]] efficiently, or negotiate meaningful [[Definition:Reinsurance treaty | reinsurance treaties]]. Models inform [[Definition:Underwriting | underwriting]] appetite, guide [[Definition:Portfolio management | portfolio management]] by revealing accumulation risks, and underpin the disclosures that [[Definition:Rating agency | rating agencies]] and regulators require. As the industry confronts emerging perils — from climate change-driven [[Definition:Secondary peril | secondary perils]] to systemic cyber events — the capacity to build, validate, and iterate on risk models increasingly separates well-managed carriers from those exposed to adverse surprises. The rise of [[Definition:Artificial intelligence (AI) | artificial intelligence]] and [[Definition:Machine learning | machine learning]] is accelerating model sophistication, enabling real-time parameter updates and the incorporation of alternative data sources that were previously impractical to harness.

'''Related concepts:'''
{{Div col|colwidth=20em}}
* [[Definition:Catastrophe model]]
* [[Definition:Actuarial science]]
* [[Definition:Solvency capital requirement (SCR)]]
* [[Definition:Probable maximum loss (PML)]]
* [[Definition:Exposure management]]
* [[Definition:Risk quantification]]
{{Div col end}}

Definition:Risk modelling - Revision history

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