The risk of using a flawed model, yielding incorrect outputs. Model risk applies wherever a model, prototype, or other human construct is used. While models are widely used to help explain everything from life expectancy, to disease progression, to aerodynamics, to behavioral responses, it remains the case that no model is perfect. All self-respecting scientists and practitioners understand this. Model risk doesn't refer to the known deficiencies of a model, but rather to the potential that some unknown aspect of the model is imperfect to the extent that use of its outputs may lead to loss or damage.

Example:

An insurance company uses a model to calcuate mortality rates, and then designs life insurance products based on the model's outputs.

Downside Scenario:

The model contains hidden flaws that lead to poorly-priced life insurance products. More specifically, the model underestimates mortality rates. Over time, as the true rates manifest themselves, the insurance company incurs great losses.

Upside Scenario:

The model properly captures mortality rates, with even greater precision than the models used by competing companies. This greater precision allows the firm to provide its products at a lower cost, building a strong customer base.

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