Predictive Modeling Governance and Model Risk Management

Model Governance Frameworks

As insurers rely increasingly on predictive models for pricing, underwriting, and claims management, robust model governance has become essential. A model governance framework defines policies for model development, validation, implementation, and ongoing monitoring. Key elements include model inventory management, documentation standards, approval workflows, and roles and responsibilities for model owners, developers, validators, and users. Regulatory guidance such as the NAIC Model Bulletin on AI and predictive models sets expectations for insurers.

Managing Model Risk

Model risk arises when models produce inaccurate outputs or are used inappropriately. Effective model risk management includes independent validation (testing model accuracy on holdout data), sensitivity analysis (assessing how outputs change with input variations), benchmarking against alternative approaches, and ongoing performance monitoring. Models should be reassessed when underlying data distributions shift or business conditions change. Documentation of model limitations and known weaknesses is as important as documenting model strengths.