So I have an interesting problem that I'm working on. I have a dataset of customers from a bank for car loans. For each customer, I also have their associated payment information including repayment amounts, days past due and payment channel (cash, bank transfer etc.) and the type of car. (brand, manufacturing year, new or used etc.)
The bank classifies a loan as NPL (non performing loan) if the customer misses payments 3 times in a row. So I would like to compute the probability that one particular customer misses 1 repayment (30days) 2 repayments in a row (60 days) and 3 repayments in a row (NPL, 90 days)
My first thought was that this is a classification problem, but for each customer I would have to compute 3 probabilities as well as the confidence levels of each probability computed, and I'm not sure how to approach this compute the probabilities involved.
Another approach would be to treat the problem as a time series problem where the y axis would be number of days past due on the loan and I could predict up to 3 time steps ahead (where 1 time step is 30 days) if the days past due would reach 30, 60 and 90 days respectively.
Naturally, the model(s) trained would have to be retrained at least once a month to incorporate new payment records.
Any thoughts on general ideas of how to approach the problem would be greatly appreciated.