The basis of my dataset is likelihood to default at end of the month. I have been messing around with some glm() modeling to determine the probability of yes/no outcome based on an initial input of variables, but I do not know if this translates into my actual scenario where the variables change everyday leading up to the end of the month.

The customer may have a very unlikely chance to default based on initial variables but as the month goes on, this chance could change to very likely. However, from my existing glm() testing, I am always using the initial variables from that first day (call it the first day of the month). Is there a way with glm() to have it factor in how a customer's values change day over day leading up to the end of the month so I get a true probability based on all days so far, or do I need to expand to a different model type?