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⚠️ '''Model risk''' is the potential for adverse consequences arising from decisions based on incorrect or misused quantitative models within insurance operations. Insurers depend on models at virtually every stage of the value chain — [[Definition:Actuarial science | actuarial]] [[Definition:Pricing | pricing]] models, [[Definition:Catastrophe model | catastrophe models]], [[Definition:Loss reserve | reserve estimation]] models, [[Definition:Credit risk | credit scoring]] algorithms, and increasingly [[Definition:Machine learning (ML) | machine learning]] classifiers for [[Definition:Underwriting | underwriting]] and [[Definition:Fraud detection | fraud detection]]. When any of these models contains flawed assumptions, coding errors, biased training data, or is applied outside its intended context, the insurer faces model risk — a category of [[Definition:Operational risk | operational risk]] that can translate directly into financial loss, regulatory sanction, or reputational harm.

🔬 Managing model risk follows a lifecycle approach. It begins with rigorous development practices: proper feature selection, out-of-sample testing, and documentation of assumptions. Before deployment, independent [[Definition:Model validation | model validation]] teams — or external specialists — stress-test the model against extreme scenarios, check for [[Definition:Bias | bias]], and confirm that outputs remain sensible across a range of inputs. Once in production, ongoing monitoring tracks key performance indicators such as actual-versus-expected loss ratios, discrimination power metrics, and [[Definition:Model drift | model drift]] signals. An effective [[Definition:Model governance | model governance]] framework assigns clear ownership, establishes escalation procedures for material deviations, and maintains a model inventory so senior leadership and [[Definition:Insurance regulator | regulators]] know which models drive which decisions.

📊 The stakes are particularly high in insurance because models often dictate the very solvency of the enterprise. A [[Definition:Catastrophe model | catastrophe model]] that underestimates tail risk could lead a [[Definition:Reinsurance | reinsurer]] to hold inadequate [[Definition:Capital | capital]] against a major [[Definition:Natural catastrophe | natural catastrophe]]. A [[Definition:Predictive model | predictive]] pricing model that inadvertently discriminates on [[Definition:Protected class | protected-class]] variables could trigger enforcement action and costly remediation. Regulatory bodies like the [[Definition:National Association of Insurance Commissioners (NAIC) | NAIC]], the [[Definition:Prudential Regulation Authority (PRA) | PRA]], and the [[Definition:European Insurance and Occupational Pensions Authority (EIOPA) | EIOPA]] have all sharpened their focus on how insurers govern algorithmic decision-making, making model risk management an essential pillar of modern [[Definition:Enterprise risk management (ERM) | enterprise risk management]].

'''Related concepts:'''
{{Div col|colwidth=20em}}
* [[Definition:Model drift]]
* [[Definition:Model validation]]
* [[Definition:Catastrophe model]]
* [[Definition:Enterprise risk management (ERM)]]
* [[Definition:Actuarial science]]
* [[Definition:Operational risk]]
{{Div col end}}

Definition:Model risk - Revision history

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