# Probability each day till payment

I'm tasked to predict if a receipt will be paid or not.

I know how to build a classification model that says, on day 0, if the receipt will be paid or not. But how would one build a model to run everyday, not just day 0? For my training set, do I need to build a data point for each day each receipt hasn't been paid?

In this industry, nothing really helps between day 0 and day X, generally the customer has no activity, as such the data fed to a model will most likely be the same, but at the same time, as days go by, inevitably the probability of payment will go down.

How can one tackle this problem?

I would start empirically. Take all customers who are open on day 0 (lets assume that is the day after their previous payment). How many pay? Now move up 1 day (the 2nd day after their previous payment). How many pay? Then repeat until end of study. You need to decide what day 0 is, then align all customers from the past. You now have a probability of any customer paying on each day from day 0. You can calculate what is the expected number of customers paying today by calculating how many customers are n days away from day 0.