Model risk management (MRM) is the art of handling the inherent uncertainty related to mathematical modeling. We create algorithms for many different reasons. In the past, most models were built to study the evolution of dynamical systems (e.g. a credit risk model or a valuation model). Models were often created via a first-principles approach with analytical tractability in mind. Nowadays, ML models are everywhere, impacting both our individual behavior and changing the dynamics of entire societies. With such a persistent use of models, understanding the risks involved becomes mandatory since the consequences of model failure can be massive.
This is why there is a continuously growing pressure from governments and regulators to increase requirements for MRM and improve AI governance. Because of this evolution, financial organizations are looking at technology to address these challenges. In the current white paper we expand on this topic.