Model Validator

The goal of a model validator is to provide an independent assessment of whether a quantitative model is fit for purpose. Regulators require financial institutions to validate their internal models before using them for regulatory capital calculations or risk reporting. A flawed model can lead to incorrect capital levels and regulatory penalties.

The daily work involves reviewing model documentation, replicating the model independently, and testing its assumptions and limitations. You look for cases where the model might produce unreliable results and document these as findings. The output is a validation report that describes what was tested, what was found, and whether the model is approved for use.

The main tools are Python and R for replicating and testing models, and SQL for accessing the data the model uses. Statistical testing methods such as backtesting, stress testing, and benchmarking are central to the work. Knowledge of financial products and regulatory frameworks such as Basel IV or Solvency II is important.

The role is independent by design: model validators work separately from the teams that build the models. This separation is required by regulators to avoid conflicts of interest. The role connects closely to the Quantitative Analyst role, whose models are often the subject of validation, and to the Risk Manager role, which uses validated models in its risk reporting.