You are looking at an unsupervised learning problem, i.e. your transactions do not have "regular" or "irregular" activity labels. Regularity is customer dependent, you can try to derive customer specific regularity features, e.g. the most frequent category for that customer (and whether or not a new activity is deviant from that)given day of the week, location of the customer, etc. and then label some of your data (semi-supervised, just because labelling all may not be feasible) and fit a single classifier. There will not be an easy shortcut here I am afraid.
Depending on your dataset, you can carry out novelty & outlier detection.
Or you can look at one-class supervised learning.
I am not going into more detail, there are plenty of threads on this website discussing these two.