ML Governance Within Banking: A study into the oversight of ML model development and compliance.

Financial institutions are increasingly leveraging ML to perform difficult and laborious tasks in order to save costs and/or gain competitive advantage. DNB, however, is hesitant in allowing ML as they are seen as black-box models. As part of the model governance, DNB requires financial institutions to audit their models on a regular basis. However audit requirements for ML model validation are not clearly defined. This results in an indeterminate expectation of the scope and outcomes from both sides. In this thesis an effort is made to bridge the expectation gap by facilitating ML governance with the use of a framework for challenger banks.