Model risk management (MRM) is the art of handling the inherent uncertainty related to mathematical modelling. 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.
Jos Gheerardyn has built the first FinTech platform that uses AI for real-time model testing and validation on an enterprise-wide scale. A zealous proponent of model risk governance & strategy, Jos is on a mission to empower quants, risk managers and model validators with smarter tools to turn model risk into a business driver. Prior to his current role, he has been active in quantitative finance both as a manager and as an analyst.
Do you have questions about the white paper? Send us an email.