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📊 '''Model risk management''' is the discipline of identifying, assessing, and mitigating the risks that arise when quantitative models used by [[Definition:Insurance carrier | insurers]] produce inaccurate or misleading outputs. In insurance, models drive some of the most consequential decisions an organization makes — from [[Definition:Pricing | pricing]] and [[Definition:Reserving | reserving]] to [[Definition:Catastrophe modeling | catastrophe exposure estimation]] and [[Definition:Capital adequacy | capital adequacy]] calculations. When those models contain errors in logic, rely on flawed data, or are applied outside the conditions they were designed for, the resulting decisions can erode [[Definition:Solvency | solvency]], distort [[Definition:Loss ratio (L/R) | loss ratios]], or trigger [[Definition:Regulatory compliance | regulatory]] sanctions.

🔍 Effective model risk management follows a lifecycle framework. Before a model enters production, an independent validation team stress-tests its assumptions, back-tests its predictions against historical [[Definition:Loss experience | loss experience]], and evaluates its sensitivity to changes in key variables. Once deployed, the model is subject to ongoing monitoring — tracking actual versus expected outcomes for metrics like [[Definition:Claims frequency | claims frequency]] or [[Definition:Loss severity | severity]] — and periodic revalidation to account for shifts in the underlying risk environment. Regulators such as the [[Definition:National Association of Insurance Commissioners (NAIC) | NAIC]] and international bodies under [[Definition:Solvency II | Solvency II]] increasingly expect insurers to maintain formal model inventories, documented governance policies, and clear escalation paths when a model's performance degrades beyond acceptable thresholds.

⚠️ The stakes have grown as insurers incorporate [[Definition:Machine learning | machine learning]] and [[Definition:Artificial intelligence (AI) | artificial intelligence]] into their modeling toolkits. Unlike traditional [[Definition:Actuarial science | actuarial]] models built on transparent formulas, complex algorithms can behave as "black boxes," making it harder to explain why a particular [[Definition:Risk classification | risk classification]] or [[Definition:Premium | premium]] was produced. This opacity raises both regulatory and reputational concerns — particularly around [[Definition:Unfair discrimination | unfair discrimination]] — and has pushed model risk management from a back-office compliance exercise to a board-level governance priority. Insurers that invest in robust model governance not only reduce the chance of costly surprises but also build credibility with [[Definition:Rating agency | rating agencies]] and supervisory authorities.

'''Related concepts'''
{{Div col|colwidth=20em}}
* [[Definition:Catastrophe modeling]]
* [[Definition:Actuarial science]]
* [[Definition:Artificial intelligence (AI)]]
* [[Definition:Reserving]]
* [[Definition:Regulatory compliance]]
* [[Definition:Data governance]]
{{Div col end}}

Definition:Model risk management - Revision history

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