I am currently working on a credit risk related project where i built a binary logistic regression model for an imbalanced dataset.
According to the regulations i have to prove that the model performs well on different subsets of data (e.g. age-group [18, 25] compared to age-group [26, 40], mortgages compared to consumer loans, high/low income). Typically, the sub-Segments would be indicated by a binary variable, but it could also be that there are more than two Segments to compare.
I spent the whole day looking for possible solutions but so far I did not find something particular useful for this challenge. Unfortunately, it is not enough to show that AUC does not drop significantly on each of the sub-segments.
Are there any of you who already have experience with these kind of problems?
Thank you very much!