I am evaluating a Credit Risk model that predicts the estimated likelihood of customers defaulting on their mortgage accounts. The model is a Logistic Regression estimator and was built by another team. They use the Gini metric to measure the performance of the model. They achieved 87%. Upon evaluation, I found that the recall was 51% whilst the error rate of the non rare event class (do not default) was 0.9%. Am I correct in thinking that the Gini is actually a misleading metric in this case because it doesn't really show the extremely poor predictive performance of the rare event class? I have questioned them about this and tried to recommend them to use precision/recall metrics as well as confusion matrices and a precision-recall trade-off graph but they quickly dismissed me.
Any advice would be much appreciated.