A bank used XGBoost trained on 7 years of transaction data to build a credit eligibility model. The model is accurate but the credit team (with little data science expertise) cannot understand why some customers are denied. What is the most operationally efficient way for the data science team to provide explanations to the credit team?

The most operationally efficient way is to use SageMaker Debugger to compute Shapley values. SageMaker Debugger integrates directly into the training process, allowing it to collect model insights, including Shapley values, during or after training without requiring separate inference capturing or post-processing steps. This streamlines the generation of SHAP charts, which provide detailed, interpretable explanations for individual predictions, directly addressing the credit team's need to understand specific denial reasons. Using SageMaker Model Monitor with subsequent Shapley value computation is less efficient as it requires capturing inferences from a deployed endpoint, adding an extra step. Retraining locally and using plotimportance() only provides global feature importance, not individual prediction explanations. SageMaker Processing for post-analysis is an option but Debugger's direct integration for Shapley values is more efficient for this specific use case.