Is Gini coefficient a good metric for measuring predictive model performance on highly imbalanced data
The Gini Coefficient can also be expressed in terms of the area under the ROC curve (AUC): G = 2*AUC -1 link. The ROC curve, on the other hand, is influenced by class imbalance through the false positive rate FP/(FP+TN). If the number of negatives is a lot larger, this could be a potential issue. 

In short, the Gini Coefficient has similar pros and cons as the AUC ROC metric.