Single Decision Tree output from a XGBoost Model Would it be methodologically acceptable to export all the different decision trees constructed by an XGBoost model and test them singularly as a potential risk classification system?

For an XGBoost model, examining one tree at a time makes little sense, because gradient boosting is an iterative algorithm primarily driven by the "mistakes" done up to that point. Thus the $i$-th tree on it's own has little practical meaning. For all we know, a seemingly "risky" tree corrects existing biases/lapses of the overall XGBoost model.
