Low leves of probability observed after modelling.Is it right to scale the probability I have done modelling on imbalanced class , without any sampling methods.
Event rate is around 0.1 ,After modelling I am getting probalities more at the lower side close to zero.I have tried differnt models and an ensemble as well using caretlist in R.I am getting AUC around 79.I have rank ordered the probability and the maximum probability predicted is less than .5.

My question is that 

1)Is there any thing i can try for getting the predicted probabilities ranging from 1(or in the higher level close to 1 eg : .7,.8 etc)

2)Is it right to transform the predicted probabilities between 0 and 1 by by applying min - max normalization and considering all probabilities above .5 have chance of winning and less than .5 have a chance of losing  

Note:I have tried sampling methods like smote, rose and up sampling but resulted in low AUC because of the small number of observations.

Please let me know if any clarification is needed.
Any assistance would be really helpful.
It sometimes happens that a classification model will output probability estimates that are all in the low range. That means the model does not make any predictions that it is very sure are the positive class. Since only 10% of your data falls into the positive class, it appears to be a difficult problem to predict using your model. 

Do not transform the probability estimates. When this happened to me multiple times, the predictions were still useful. For example, you may be interested in the highest 10% of predictions. You can always set your prediction threshold anywhere between 0 and 1.