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🖥️ '''Actuarial model''' is a quantitative representation of insurance risks and financial outcomes, built by actuaries to simulate, project, or evaluate scenarios that inform critical decisions such as [[Definition:Reserve | reserve]] setting, [[Definition:Pricing | pricing]], [[Definition:Capital | capital]] allocation, and [[Definition:Reinsurance | reinsurance]] program design. These models translate assumptions about [[Definition:Claim | claim]] frequency, severity, timing, and correlation into numerical outputs that an [[Definition:Insurance carrier | insurer]]'s management and board can use to understand the range of possible futures the company faces.

⚙️ The construction of an actuarial model varies widely depending on its purpose. A [[Definition:Loss reserving | reserving]] model might project the runoff of existing [[Definition:Liability | liabilities]] using [[Definition:Loss development factor | development patterns]] and [[Definition:Trend factor | trend assumptions]], while a [[Definition:Catastrophe model | catastrophe model]] simulates thousands of natural-disaster scenarios to estimate [[Definition:Probable maximum loss (PML) | probable maximum losses]]. [[Definition:Dynamic financial analysis (DFA) | Dynamic financial analysis]] models integrate underwriting, investment, and operational variables to test an insurer's [[Definition:Solvency | solvency]] under stress. Regardless of type, a well-built model clearly documents its inputs, logic, limitations, and validation procedures. [[Definition:Actuarial Standards of Practice (ASOP) | ASOP No. 56]] specifically addresses the actuary's responsibilities when using models, requiring that they understand the model sufficiently to judge its appropriateness and communicate its limitations.

📊 In an era when [[Definition:Insurtech | insurtech]] firms and established carriers alike are deploying increasingly sophisticated [[Definition:Predictive model | predictive analytics]] and [[Definition:Machine learning | machine learning]] tools, the actuarial model remains the foundational structure through which risk is quantified for regulatory and financial reporting purposes. [[Definition:Insurance regulator | Regulators]] expect that models underlying [[Definition:Rate filing | rate filings]] or [[Definition:Risk-based capital (RBC) | risk-based capital]] calculations are transparent, reproducible, and subject to periodic [[Definition:Model validation | validation]]. When a model performs well — accurately capturing the relationship between risk factors and outcomes — it gives underwriters, executives, and [[Definition:Investor | investors]] the confidence to act decisively. When it fails, the financial and reputational consequences can be severe, which is why governance around actuarial models has become a discipline in its own right.

'''Related concepts:'''
{{Div col|colwidth=20em}}
* [[Definition:Catastrophe model]]
* [[Definition:Predictive model]]
* [[Definition:Dynamic financial analysis (DFA)]]
* [[Definition:Actuarial method]]
* [[Definition:Model validation]]
* [[Definition:Loss reserving]]
{{Div col end}}

Definition:Actuarial model - Revision history

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