how to interpret a high AUC value but a low F1 score after upsampling minority class?

I use to gradient boosting to classify my data between default and paid. The data is very imbalanced where default is in minority. The fisrt classification report from sklearn gradient boosting classifier give:

             precision    recall  f1-score   support

Defaulted       0.56      0.03      0.05       364
Paid       0.87      1.00      0.93      2420

micro avg       0.87      0.87      0.87      2784
macro avg       0.71      0.51      0.49      2784
weighted avg       0.83      0.87      0.82      2784


and an AUC = 0.614

To addressed the imbalence issue, I use SMOTE to upsample the default class
and I am getting an AUC = 0.941

However, the classification report wost f1-score for the default class:

              precision    recall  f1-score   support

Defaulted       0.38      0.01      0.02       364
Paid       0.87      1.00      0.93      2420

micro avg       0.87      0.87      0.87      2784
macro avg       0.62      0.50      0.47      2784
weighted avg       0.81      0.87      0.81      2784


Is this possible and how should I interpret or understand this difference between the worst f1-score but a better AUC?