Banks are rapidly expanding their use of data models – especially machine learning-enabled (ML) models – because they can provide step-level improvements in accuracy. But ML models need even more rigorous governance than traditional models. The time to implement effective ML model governance is now – before today’s global, multidimensional marketplace, massive data volumes and complexity overload traditional model risk guardrails and governance practices. This paper explores what’s required to implement effective ML model governance.