Model Selection when there is trade-off

This is one of my model variants. It achieves an AUC score of 0.73. Another one of my model variants achieves an AUC score of 0.7265. Below is the the confusion matrix -

Like many problems, the minority class(positive class) represents the customers I'd like to target. But having many false positives is going to cost me money.

Q - how to select a model and how such a massive difference in confusion matrix gives similar AUC scores?

Topic auc classification machine-learning

Category Data Science

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