unbalanced data classification
For starters, I am not confident that combining the results will give you what you expect. Have you checked if the true negative remain the same in both occurrences?

Moreover, have you tried to alternate the hyper parameters on the XGBoost model responsible for class balancing like max delta step or scale pos weight. 

You can also try to use Sampling techniques for Over Sampling and Under Sampling. Or try possibly using many trees on  XGBoost on a different random Under Sample. 

P.S. I am interested on the anomaly detection paper you mentioned, could you provide a link, thnx.