AUC measure the area under the ROC curve, wich is built by taking (TPR,FPR) for all the possible thresholds. See here for the whole definition and construction : What does AUC stand for and what is it?

It is a good metric for simple problems, when you want to look at multiple thresholds at once to keep your possibilities open. In general, it is not a good metric if you want only one threshold, ie. to make a decision. It is especially bad with imbalanced data set as you have seen. See Frank Harrell answer to the question mentionned above.

There might be a bigger problem with your approach : with imbalanced data set the class are probably not separable. So it doesn't make sense to use a classifier in the ML sense. You might want to use a probabilistic approach to model your data, then use a good metric to choose how you separate your classes. See Frank Harrel blog post: https://www.fharrell.com/post/classification/

AUC measure the area under the ROC curve, wich is built by taking (TPR,FPR) for all the possible thresholds. See here for the whole definition and construction : What does AUC stand for and what is it?

It is a good metric for textbook problems with separable classes and when your decision has no practical impact. It let you look at multiple thresholds at once. In general, it is not a good metric if you want only one threshold, ie. to make a decision. It is especially bad with imbalanced data set as you have seen. See Frank Harrell answer to the question mentionned above.

There might be a bigger problem with your approach : with imbalanced data set the class are probably not separable. So it doesn't make sense to use a classifier in the ML sense. You might want to use a probabilistic approach to model your output, then use a good metric to choose how you set a threshold and you separate your classes. That metric would heavily depend on what you are trying to model and practical implication associated to your decision. See Frank Harrel blog post: https://www.fharrell.com/post/classification/