Lenders’ traditional Model Risk Management (MRM) process was set up for conventional models based on logistic regression. Model verification, fair lending analysis, and model risk management documentation are already onerous for these traditional models. Machine learning (ML), which can use thousands of variables and multiple modeling methods, is likely to present a significant challenge for your organization’s established manual MRM process. Do the same principles of MRM apply for ML models? Does the MRM process for ML models need to be as time-consuming and laborious as it has been in the past?