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🔬 '''Robustness check''' is an analytical procedure used in insurance to verify that the conclusions drawn from a [[Definition:Statistical model | statistical model]] or actuarial analysis remain stable when key assumptions, data inputs, or methodological choices are varied. Insurers and [[Definition:Reinsurance | reinsurers]] rely heavily on models for [[Definition:Pricing | pricing]], [[Definition:Reserving | reserving]], [[Definition:Catastrophe modeling | catastrophe risk estimation]], and [[Definition:Capital modeling | capital modeling]] — and a result that collapses under slight perturbations signals fragility that could translate into real financial exposure. Regulators across major markets increasingly expect companies to demonstrate that their internal models have undergone robustness testing, whether under [[Definition:Solvency II | Solvency II]]'s internal model approval process in Europe or the [[Definition:Own Risk and Solvency Assessment (ORSA) | ORSA]] frameworks adopted in the United States and parts of Asia.

⚙️ In practice, analysts perform robustness checks by systematically altering elements of their analysis and observing whether core findings hold. An [[Definition:Actuary | actuary]] developing a [[Definition:Loss reserve | loss reserve]] estimate might test alternative [[Definition:Loss development factor | loss development factors]], swap out [[Definition:Tail factor | tail factors]], or exclude outlier claim years to see if the indicated reserve range shifts materially. In [[Definition:Catastrophe modeling | catastrophe modeling]], a robustness check could involve running the same portfolio through multiple vendor models — such as those from [[Definition:Risk Management Solutions (RMS) | RMS]], [[Definition:AIR Worldwide | AIR Worldwide]], or [[Definition:CoreLogic | CoreLogic]] — and comparing outputs. For [[Definition:Insurtech | insurtech]] firms deploying [[Definition:Machine learning | machine learning]] in [[Definition:Underwriting | underwriting]], robustness checks often include testing model performance on out-of-sample data, varying feature sets, or evaluating sensitivity to changes in the training window. The goal is not to prove the model perfect but to map the boundaries within which its outputs remain trustworthy.

📊 The practical stakes are considerable. A pricing model that appears profitable under one set of assumptions but produces losses under plausible alternatives may be capturing noise rather than genuine risk signal — a dangerous foundation for committing [[Definition:Underwriting capacity | underwriting capacity]]. Robustness checks also serve a governance function: when presented to boards, [[Definition:Risk committee | risk committees]], or regulators, they demonstrate that management understands the limitations of its own tools. In the context of [[Definition:IFRS 17 | IFRS 17]] implementation, for example, insurers have had to validate that their chosen measurement approaches yield stable results across different economic scenarios and demographic assumptions. Ultimately, robustness checking is what separates disciplined quantitative practice from over-reliance on a single number — a distinction that matters enormously in an industry where model outputs directly determine how much capital backs policyholder promises.

'''Related concepts:'''
{{Div col|colwidth=20em}}
* [[Definition:Sensitivity analysis]]
* [[Definition:Statistical model]]
* [[Definition:Stress testing]]
* [[Definition:Catastrophe modeling]]
* [[Definition:Capital modeling]]
* [[Definition:Model validation]]
{{Div col end}}

Definition:Robustness check - Revision history

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