XG Boost result interpretation for unbalanced datasets (Accuracy & AUCROC)
With Success already being the larger class, you probably shouldn't be using a scale_pos_weight larger than one: you want to scale the positive class's contribution to the loss function down rather than up.
I suspect that's what's happening in the first case.  With scale_pos_weight=75, the model ends up basically only caring about the positive class, predicts everyone is in the positive class, and so your accuracy is just a little better than the 77% baseline you'd expect with that strategy.  With that motivation, it's not too surprising the AUC is poor, although I wouldn't have expected a drop all the way to the 50% baseline...
If you don't use scale_pos_weight (you said &quot;if I did not tune&quot;, but does that mean you left it at the default 1?), then the model performs better in rank-ordering (AUC=70%), but not so well in the hard classification.  You might want to tweak the prediction threshold here; there's probably a different threshold that will perform better for accuracy score.  You could also try scale_pos_weight=0.25 or so; that should make the default cutoff better, hopefully with little effect on AUC?