I have a machine learning model trained with a list of mortgage features that include macro variables where the field to predict (the label) is "Mortgage Defaulted" = 1 or 0 (Yes or No).
Now, I need to determine if a mortgage will default in the future. For that, I use the same macro features where the model returns if a payment will be made or not.
But if a single payment is not made, that doesn't mean that the mortgage will default, as the borrower may delay the payment. Moreover, if macro conditions improve, I may have a sequence of payments not made followed by a sequence of payments made.
What is the best way to determine that the mortgage will default? My thinking is to have a percentage of payments NOT made, let's say 10%, and if the number of payments is greater than the percentage, then declare the mortgage as defaulted. Is this assumption valid? Any additional ideas will be welcome.