Building a robust model risk management framework in financial institutions
With increasing volumes of data, and the introduction of Artificial Intelligence (AI) and machine learning (ML) technologies, models are at the heart of every Financial Institution’s (FI) operations. However, as FIs increasingly rely on model outputs for decision-making, the focus on model risk—or risk of errors in the development, implementation, or use of models—has continued to gain momentum.
There are several reasons for this. Firstly, the evolving technological capability of algorithms has resulted in widespread democratization of model development, enabling users to deploy models without relying on internal IT or traditional model development functions. While this increases the speed of innovation, it also increases the risk for organizations, as these new models are not subjected to the same robust testing systems and governance structures as traditional ones. Secondly, there has been increasing stakeholder expectations related to the documentation, accountability, controls, and risk management of such models. Regulators have been intensifying their scrutiny on model risks, focusing on models with elements of AI systems and ML algorithms.
Performance magazine issue 31, January 2020
Performance is a triannual digest, dedicated to investment management professionals, which brings you the latest articles, news and market developments from Deloitte’s professionals and clients.