You deployed a gradient-boosted model on a Vertex AI endpoint to predict loan default. Overall prediction accuracy and the marginal distributions of each input feature (monitored via training-serving skew and drift detection) have stayed within alert thresholds for three months. However, business stakeholders report that recent predictions no longer match domain intuition, and a manual audit suggests the model is now weighting certain features very differently than at launch. Which monitoring approach would most directly detect this problem?