Model Validation

Banks and insurers use complex models to value assets, measure risk, and calculate capital requirements. Regulators require that these models are independently validated before they can be used in production. The goal of a model validation project is to identify weaknesses in the model's methodology, data, or assumptions before they affect business decisions or regulatory reporting.

The work involves reviewing the model's documentation, replicating its calculations independently, running tests, and writing a validation report. You compare the model's outputs against benchmarks or independent implementations to check whether the results are consistent. Stress testing is part of the process: you run the model under extreme but plausible scenarios to check that its behaviour remains reasonable.

The main tools are Python and R for testing and analysis. Knowledge of the type of model being validated is important: credit risk models require knowledge of credit markets and statistical methods; pricing models require knowledge of the relevant instruments and pricing theory. Familiarity with regulatory requirements, including ECB model risk guidance and Basel IV standards, is essential.

Model validation work appears in the Banking and Actuarial Sector sectors. It connects closely to the Risk Manager, Quantitative Analyst, and Actuary roles. At large banks, dedicated model risk management teams carry out this work. Smaller institutions sometimes use external consultancies.