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✅ '''Model validation''' is the independent process of evaluating whether a quantitative model — whether used for [[Definition:Pricing model | pricing]], [[Definition:Loss reserving | reserving]], [[Definition:Catastrophe modeling | catastrophe analysis]], [[Definition:Capital adequacy | capital adequacy]], or [[Definition:Fraud detection | fraud detection]] — performs as intended, rests on sound assumptions, and produces reliable outputs within its intended scope. In [[Definition:Insurance | insurance]], where models directly influence [[Definition:Premium | premiums]] charged, [[Definition:Claim reserve | reserves]] held, and [[Definition:Capital allocation | capital allocated]], flawed models can silently erode [[Definition:Underwriting profitability | profitability]] or produce outcomes that violate [[Definition:Insurance regulation | regulatory]] requirements.

🔍 A thorough validation typically proceeds along three dimensions. Conceptual soundness examines whether the model's theoretical framework and assumptions are appropriate for the problem — for instance, whether a [[Definition:Frequency-severity model | frequency-severity model]] correctly accounts for correlation between variables. Outcome analysis compares model predictions against actual results using back-testing, out-of-sample testing, and sensitivity analysis to assess accuracy and stability. Process verification confirms that data inputs are clean, transformations are correctly implemented, and the model's operational environment — from code to governance documentation — meets internal and [[Definition:Insurance regulator | regulatory]] standards. Findings are documented in a validation report with identified limitations and recommended remediation.

🛡️ As [[Definition:Machine learning | machine learning]] and [[Definition:Artificial intelligence | AI-driven]] models proliferate across [[Definition:Underwriting | underwriting]], [[Definition:Claims handling | claims]], and [[Definition:Risk assessment | risk assessment]], the validation function has grown more complex and more critical. Traditional [[Definition:Actuarial analysis | actuarial]] models were relatively transparent, but complex algorithms can behave as opaque systems whose internal logic resists straightforward explanation. [[Definition:Insurance regulator | Regulators]] increasingly expect [[Definition:Insurance carrier | carriers]] to demonstrate that all models used in consequential decisions — especially those affecting consumers — are subject to periodic, independent review. For [[Definition:Insurtech | insurtech]] firms building next-generation tools, embedding a validation culture early is far less costly than retrofitting one after a [[Definition:Model risk management | model governance]] failure surfaces in production.

'''Related concepts'''
{{Div col|colwidth=20em}}
* [[Definition:Model risk management]]
* [[Definition:Actuarial analysis]]
* [[Definition:Pricing model]]
* [[Definition:Catastrophe modeling]]
* [[Definition:Machine learning]]
* [[Definition:Algorithmic bias]]
{{Div col end}}

Definition:Model validation - Revision history

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