Training discrete time hazard model with xgboost on right censored loan data I am currently developing a loan default risk model using a discrete time hazard approach with xgboost. The goal is to generate a series of predicted monthly default probabilities using a new borrowers features at the time of origination.
The training data set contains monthly snapshots of each loan's payment history until the loan terminates via payoff or default. The data is set up as a binary classification problem with the target variable indicating if the borrower paid or defaulted that month. This indicator, plus the variable for loan age are the only time varying features in this data set. The rest are repeating features collected at the time of origination such as credit info and loan characteristics such as term length and loan amount.
The problem here is that some of the loans are right censored, so the loan history is not available for later months, especially for those that originated in recent years. What are some solutions to this problem? Should I remove the censored data?


Should I remove the censored data?

That depends on what you mean by "remove."
You need to include all cases at times when they are at risk of an event. Otherwise you risk substantial bias. So you do not remove all data from individuals who end up having censored survival times.
If an individual is not at risk after a particular time, however, then that individual should not be included in the analysis at those later times. Anything that erroneously includes that individual in a risk set after that time (e.g., a data row for an observation time at which the individual could not have been observed) should be removed. That's standard practice for right censoring.
