Loading content
Concept for identifying, assessing and managing risks arising from the use of quantitative models.
Model risk refers to the potential for losses or adverse outcomes caused by flawed, biased, or misapplied quantitative models and their outputs. The concept covers model validation, data quality, governance, monitoring and documentation to detect uncertainty, overfitting, performance drift and implementation errors, and to manage model-related business and regulatory exposure.
Proportion of time the model performance deviates significantly from baseline.
Quantitative error measures such as RMSE, AUC or log-loss in production.
Share of models and use-cases subject to formal validation processes.
A large institution establishes a centralized validation team, standard tests and reporting to risk management.
Automated pipeline monitors drift, performance and issues alerts for production models.
Audit reveals gaps in documentation and validation scope; remediation required.
Define governance and validation policies.
Build validation and monitoring infrastructure.
Establish regular reviews, reporting and escalation paths.