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🛡️ '''Model governance''' is the framework of policies, procedures, and controls that insurance organizations use to oversee the development, validation, deployment, and ongoing monitoring of quantitative models — including [[Definition:Actuarial model | actuarial]], [[Definition:Catastrophe model | catastrophe]], [[Definition:Predictive model | predictive analytics]], and [[Definition:Pricing model | pricing]] models — that drive critical business decisions. Because insurers rely on models to set [[Definition:Premium | premiums]], estimate [[Definition:Loss reserve | reserves]], assess [[Definition:Solvency | solvency]], and allocate [[Definition:Capital | capital]], weak governance can lead to systematic mispricing, regulatory penalties, and unexpected financial losses.

🔧 A well-structured governance program assigns clear ownership of each model, establishes documentation standards, and requires independent [[Definition:Model validation | validation]] before any model goes into production. Validation teams test assumptions, stress-test outputs against historical [[Definition:Loss data | loss experience]], and evaluate whether the model performs as intended across different scenarios. Ongoing monitoring tracks [[Definition:Model drift | model drift]] — the gradual divergence between predicted and actual outcomes — and triggers recalibration or retirement when performance deteriorates. With the proliferation of [[Definition:Machine learning | machine learning]] and [[Definition:Artificial intelligence (AI) | AI]] models in [[Definition:Underwriting | underwriting]] and [[Definition:Claims management | claims]], governance frameworks must also address explainability, [[Definition:Algorithmic bias | bias]] detection, and auditability to satisfy both internal risk committees and external regulators.

📊 Regulatory expectations around model governance have sharpened considerably. The [[Definition:National Association of Insurance Commissioners (NAIC) | NAIC]] has issued guidance on the use of predictive models and [[Definition:Big data | big data]] in insurance, and [[Definition:Solvency II | Solvency II]] in Europe imposes explicit requirements on [[Definition:Internal model | internal model]] approval and documentation. For insurers and [[Definition:Reinsurer | reinsurers]] alike, robust model governance is not merely a compliance checkbox — it is foundational to earning the trust of [[Definition:Rating agency | rating agencies]], investors, and regulators. Organizations that treat governance as an afterthought risk deploying flawed models that compound errors silently until a major loss event reveals the cracks.

'''Related concepts'''
{{Div col|colwidth=20em}}
* [[Definition:Model validation]]
* [[Definition:Catastrophe model]]
* [[Definition:Actuarial model]]
* [[Definition:Artificial intelligence (AI)]]
* [[Definition:Solvency II]]
* [[Definition:Algorithmic bias]]
{{Div col end}}

Definition:Model governance - Revision history

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