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📋 '''Validation of internal model''' is the independent, structured process by which an [[Definition:Insurance carrier | insurer]] verifies that its [[Definition:Internal model | internal model]] — used to calculate regulatory capital under frameworks such as [[Definition:Solvency II | Solvency II]] — remains statistically sound, methodologically appropriate, and fit for the purposes to which it is applied. Unlike the [[Definition:Use test | use test]], which examines whether the model is genuinely embedded in decision-making, validation scrutinizes the model's technical accuracy, data quality, and predictive reliability. Solvency II's Article 124 requires a regular validation process that is independent of model development, encompassing quantitative testing, stability analysis, and comparison against external benchmarks and observed outcomes.

🔬 In practice, validation operates through a layered set of quantitative and qualitative techniques. [[Definition:Backtesting | Backtesting]] compares model predictions against actual experience — for example, checking whether projected [[Definition:Loss ratio | loss ratios]] or [[Definition:Catastrophe model | catastrophe losses]] align with realized results over multiple periods. Stress testing and [[Definition:Sensitivity analysis | sensitivity analysis]] probe how outputs respond to extreme or unusual parameter changes, revealing hidden dependencies or model fragility. Statistical goodness-of-fit tests assess whether the probability distributions chosen for key [[Definition:Risk factor | risk factors]] — such as [[Definition:Mortality risk | mortality rates]], [[Definition:Interest rate risk | interest rate]] movements, or [[Definition:Natural catastrophe | natural catastrophe]] frequencies — adequately capture observed data patterns. Profit-and-loss attribution exercises decompose actual versus expected results to identify which model components drive deviations. A dedicated [[Definition:Model validation | validation function]], organizationally separate from the teams that build and calibrate the model, typically leads this work. Supervisory authorities in jurisdictions like Germany's [[Definition:BaFin | BaFin]], the UK's [[Definition:Prudential Regulation Authority (PRA) | PRA]], and France's ACPR review validation reports as a critical element of internal model approval and ongoing supervision.

⚖️ Robust validation is what prevents an internal model from becoming a black box that no one — including the board — can meaningfully challenge. Without it, subtle errors in calibration, coding, or assumption-setting can compound over time, leading to material misstatement of [[Definition:Solvency capital requirement (SCR) | capital requirements]] and potentially endangering [[Definition:Policyholder | policyholder]] protection. The importance of validation extends beyond Europe: regulators developing risk-based capital regimes in Asia, including under [[Definition:C-ROSS | China's C-ROSS]] framework and Singapore's RBC 2 regime, have incorporated analogous expectations for model governance and independent review. For large insurers and [[Definition:Reinsurance | reinsurers]] operating across multiple jurisdictions, maintaining a consistent global validation framework — while accommodating local regulatory nuances — has become a significant governance challenge and a key area of investment in actuarial and risk management talent.

'''Related concepts:'''
{{Div col|colwidth=20em}}
* [[Definition:Internal model]]
* [[Definition:Use test]]
* [[Definition:Backtesting]]
* [[Definition:Solvency capital requirement (SCR)]]
* [[Definition:Model risk]]
* [[Definition:Sensitivity analysis]]
{{Div col end}}

Definition:Validation of internal model - Revision history

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