Validating the modeling data
Introduction Organizations use various models for varied purposes, e.g., credit risk models, pricing models, operational risk models, stress testing models, fraud analytics models, marketing mix models, etc. In principle, every model follows a life cycle involving stages such as development, validation, implementation, monitoring, etc. As per various regulatory guidelines (e.g., SR11-7 [1] , TRIM [2] , etc.) and based on internal guidelines on model risk management, model validation entails assessment of various components, e.g., modeling data, conceptual soundness, model performance, etc. This article provides practitioner views on validating the modeling data component during a model validation exercise. In principle, remarks made on the modeling data in this article apply to the production data as well. Practitioner views on validating the modeling data Figure 1 depicts components of modeling data validation. Figure 1: Components of Modeling Data Validation 1. ...