Suppose I want to train a binary model in order to predict the probability of who will buy a personal loan and in the dataset only 5 percent of the examples are people who marked as bought a personal loan. So, in this scenario maybe I can leverage downsampling or upsampling to balance the dataset, but if my dataset isn't big enough there may left very few examples or may be upsampling isn't appropriate. Then suppose I decided to use whole dataset, I partitioned it to the training and test sets in order to predict the probability of who won't buy a personal loan. Considering it's a binary model does it make sense to subtract this model's output probabilities from 1 and predicting who will buy a personal loan by using this result?
Yes that's correct, but assuming that you follow the exact same methodology you will obtain exactly the same performance at the end, so there's no advantage.
Keep in mind that the problem with class imbalance is not that one class is harder to identify than the other, but rather that it's harder to properly separate the two classes.
 It would be a different story when using one-class classification. I'm not sure if it makes sense in this case but maybe it could be something worth trying.